title: Strategic Map for Hospital Management : Perspectives and Priorities

abstract: IntroductionIt seems certain that there will be more changes in the healthcare industry in the next 15 years then there has been in the past 15 years. Why? What is the source of change? First of all, healthcare institutions continue to face a turbulent, confusing, and often threatening environment. It means that one of the most well described source of the change is external (Luigi et al., 2014). Legislative and policy initiatives, changes in demographics, increasing prevalence of chronic diseases, changes in lifestyles, consumers' lack of trust in traditional health systems, demand for more sophisticated, convenient, transparent, affordable and personalized service as well as increasing costs on healthcare (as % to GDP) have great impact on healthcare institutions Rudawska & Pctlak, 2013). This trends are well described in literature, and continuously researched by organizations and institution like: PWC (2014, 2015), CDC (Bernstein et al., 2003), Premier Healthcare Alliance, individual scientists (Rudawska & Urbanczyk, 2014) and practitioners.But there are also other sources of change. Nowadays, healthcare institutions change in the way every organization does (Rudawska, 2013). One issue of this change is especially important for healthcare institutions because of their specificity. It is sustainable development. The concept of sustainable development represents a shift in understanding of humanity's place on the planet (Hopwood et al., 2005). Why it is so important for management of a healthcare institution? Mostly, because these organizations are predestinated to implement sustainable development in some parts world-wide. In some parts they are doing it already. The UN have identified 17 goals and 169 targets of sustainable development (UN, 2015). One of the main goals is to ensure healthy lives and promote well-being for all at all ages. Some of the targets are obvious: reduce the global maternal mortality ratio to less than 70 per 100,000 live births or preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births. But some of them need additional effort. In particular, the epidemics of AIDS, tuberculosis, malaria and tropical diseases; combating hepatitis, water-borne diseases and other communicable diseases by the end of 2030 or reduce by one third premature mortality from non-communicable diseases through prevention and treatment and also, promoting mental health and well-being. Summarizing, healthcare institutions in future should become more open for the society, and more cooperative in fulfilling these goals.For hospital management it becomes increasingly difficult to adapt to the pace of policy changes and reforms while balancing economical considerations, social responsibility to provide high-quality health care to the community served and, finally, ecological concerns (Glouberman, Mintzberg, 2001). In this context, hospital management focuses on understanding the interconnections on the macro level - between society and the ecosystem - and changing its strategy to optimize the outcomes for these systems.Sustainable development concept requires managing hospitals on three different fields using three different value systems. Economic aims requires economic calculation of revenues, costs, incomes and expanses. Social aims in hospital management requires considering social reflections i.e. impacts of various diseases on society (epidemiology). Ecologic aims requires also different calculation. Ecologic calculation is focused on hospital impact on the environment. It is connected with waste management, drugs usage, as well as bioethics issues (Fig. 1).Between economic, social and ecologic aspects here there is an obvious conflict. The main source of this conflict is the lack of resources, especially financial ones (Urbaniec, 2015). …


title: Unsupervised Models For Named Entity Classification

abstract: This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use of unlabeled data can reduce the requirements for supervision to just 7 simple "seed" rules. The approach gains leverage from natural redundancy in the data: for many named-entity instances both the spelling of the name and the context inwhich it appears are sufficient to determine its type. We present two algorithms. The first method uses a similar algorithm to that of (Yarowsky 95), with modifications motivated by (Blum and Mitchell 98). The second algorithm extends ideas from boosting algorithms, designed for supervised learning tasks, to the framework suggested by (Blum and Mitchell 98).


title: Domain randomization for transferring deep neural networks from simulation to the real world

abstract: Bridging the ‘reality gap’ that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability. This paper explores domain randomization, a simple technique for training models on simulated images that transfer to real images by randomizing rendering in the simulator. With enough variability in the simulator, the real world may appear to the model as just another variation. We focus on the task of object localization, which is a stepping stone to general robotic manipulation skills. We find that it is possible to train a real-world object detector that is accurate to 1.5 cm and robust to distractors and partial occlusions using only data from a simulator with non-realistic random textures. To demonstrate the capabilities of our detectors, we show they can be used to perform grasping in a cluttered environment. To our knowledge, this is the first successful transfer of a deep neural network trained only on simulated RGB images (without pre-training on real images) to the real world for the purpose of robotic control.


title: Skippy: single view 3D curve interactive modeling

abstract: We introduce Skippy, a novel algorithm for 3D interactive curve modeling from a single view. While positing curves in space can be a tedious task, our rapid sketching algorithm allows users to draw curves in and around existing geometry in a controllable manner. The key insight behind our system is to automatically infer the 3D curve coordinates by enumerating a large set of potential curve trajectories. More specifically, we partition 2D strokes into continuous segments that land both on and off the geometry, duplicating segments that could be placed in front or behind, to form a directed graph. We use distance fields to estimate 3D coordinates for our curve segments and solve for an optimally smooth path that follows the curvature of the scene geometry while avoiding intersections. Using our curve design framework we present a collection of novel editing operations allowing artists to rapidly explore and refine the combinatorial space of solutions. Furthermore, we include the quick placement of transient geometry to aid in guiding the 3D curve. Finally we demonstrate our interactive design curve system on a variety of applications including geometric modeling, and camera motion path planning.


title: Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge

abstract: Abstract This paper provides a comprehensive analysis of the first shared task on End-to-End Natural Language Generation (NLG) and identifies avenues for future research based on the results. This shared task aimed to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena. Introducing novel automatic and human metrics, we compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures – with the majority implementing sequence-to-sequence models (seq2seq) – as well as systems based on grammatical rules and templates. Seq2seq-based systems have demonstrated a great potential for NLG in the challenge. We find that seq2seq systems generally score high in terms of word-overlap metrics and human evaluations of naturalness – with the winning Slug system (Juraska et al., 2018) being seq2seq-based. However, vanilla seq2seq models often fail to correctly express a given meaning representation if they lack a strong semantic control mechanism applied during decoding. Moreover, seq2seq models can be outperformed by hand-engineered systems in terms of overall quality, as well as complexity, length and diversity of outputs. This research has influenced, inspired and motivated a number of recent studies outwith the original competition, which we also summarise as part of this paper.


title: Securing combined Fog-to-Cloud system Through SDN Approach

abstract: Future IoT services execution may benefit from combining resources at cloud and at the edge. To that end, new architectures should be proposed to handle IoT services in a coordinated way at either the edge of the network, the cloud, or both. Reacting to that need, the Fog-to-Cloud concept has been recently proposed. A key aspect in the F2C design refers to security, since F2C raises security issues besides those yet unsolved in fog and cloud. Thus, we envision the need for new security strategies to handle all components in the F2C architecture. In this paper we propose an SDN-based (mater/slave) security architecture leveraging a centralized controller on the cloud, and distributed controllers at the edge of the network. We argue that the proposed architecture brings more security and privacy to the cloud users by reducing the distance between them and, therefore, reducing the risks of the so called man-in-the-middle attacks. The proposed security architecture is analyzed in some critical infrastructure scenarios in order to illustrate their potential benefits.


Transition-Based Neural Word Segmentation


title: RePaBit: Automated generation of relocatable partial bitstreams for Xilinx Zynq FPGAs

abstract: Partial reconfiguration in FPGAs increases the flexibility of a system due to dynamic replacement of hardware modules. However, more memory is needed to store all partial bitstreams and the generation of all partial bitstreams for all possible regions on the FPGA is very time-consuming. In order to overcome these issues, bitstream relocation can be used. In this paper, a novel approach that facilitates bitstream relocation with the Xilinx Vivado tool flow is presented. In addition, the approach is automated by TCL scripts that extend Vivado to RePaBit. RePaBit is successfully evaluated on the Xilinx Zynq FPGA using 1D and 2D relocation of complex modules such as MicroBlaze processors. The results show a negligible overhead in terms of area and frequency while enabling more flexibility by partial bitstream relocation as well as a faster design time.


title: Ingested protein dose response of muscle and albumin protein synthesis after resistance exercise in young men

abstract: Background: The anabolic effect of resistance exercise is enhanced by the provision of dietary protein. Objectives: We aimed to determine the ingested protein dose response of muscle (MPS) and albumin protein synthesis (APS) after resistance exercise. In addition, we measured the phosphorylation of candidate signaling proteins thought to regulate acute changes in MPS. Design: Six healthy young men reported to the laboratory on 5 separate occasions to perform an intense bout of leg-based resistance exercise. After exercise, participants consumed, in a randomized order, drinks containing 0, 5, 10, 20, or 40 g whole egg protein. Protein synthesis and whole-body leucine oxidation were measured over 4 h after exercise by a primed constant infusion of [1- 13 C]leucine. Results: MPS displayed a dose response to dietary protein ingestion and was maximally stimulated at 20 g. The phosphorylation of ribosomal protein S6 kinase (Thr 389 ), ribosomal protein S6 (Ser 240/244 ), and the e-subunit of eukaryotic initiation factor 2B (Ser 539 )w ere unaffected by protein ingestion. APS increased in a dose-dependent manner and also reached a plateau at 20 g ingested protein. Leucine oxidation was significantly increased after 20 and 40 g protein were ingested. Conclusions: Ingestion of 20 g intact protein is sufficient to maximally stimulate MPS and APS after resistance exercise. Phosphorylation of candidate signaling proteins was not enhanced with any dose of protein ingested, which suggested that the stimulation of MPS after resistance exercise may be related to amino acid availability. Finally, dietary protein consumed after exercise in excess of the rate at which it can be incorporated into tissue protein stimulates irreversible oxidation. Am J Clin Nutr 2009;89:161‐8.


title: A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems

abstract: In this paper we present a queuing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We ref...


title: A Low Power Quad 25.78-Gbit/s 2.5 V Laser Diode Driver Using Shunt-Driving in 0.18 µm SiGe-BiCMOS

abstract: We report on circuit design and measurement results of the newly developed quad 25.78-Gbit/s laser diode driver, fabricated by 0.18 μm SiGe-BiCMOS (ft/fmax =200/200 GHz). Power dissipation of the laser diode driver is only 40 mW/lane and 190 mW/lane including a laser bias current when the DFB laser is driven under 2.5 V supply. This IC is co-packaged with laser diodes in small size transmitter optical subassembly. We confirm excellent optical waveform, which is compliant with 100GBASE-LR4 optical eye specifications in IEEE802.ba.


title: The social structure of right and wrong

abstract: Introduces and illustrates Black's"pure sociology", a theoretical paradigm applicable to human behavior of various kinds. This work contains concepts and typologies applicable to partisan and nonpartisan forms of conflict management. It also gives theoretical perspectives on: crime as self-help; the broadening liability of organizations; and more.


title: AdRob: examining the landscape and impact of android application plagiarism

abstract: Malicious activities involving Android applications are rising rapidly. As prior work on cyber-crimes suggests, we need to understand the economic incentives of the criminals to design the most effective defenses. In this paper, we investigate application plagiarism on Android markets at a large scale. We take the first step to characterize plagiarized applications and estimate their impact on the original application developers. We first crawled 265,359 free applications from 17 Android markets around the world and ran a tool to identify similar applications ("clones"). Based on the data, we examined properties of the cloned applications, including their distribution across different markets, application categories, and ad libraries. Next, we examined how cloned applications affect the original developers. We captured HTTP advertising traffic generated by mobile applications at a tier-1 US cellular carrier for 12 days. To associate each Android application with its advertising traffic, we extracted a unique advertising identifier (called the client ID) from both the applications and the network traces. We estimate a lower bound on the advertising revenue that cloned applications siphon from the original developers, and the user base that cloned applications divert from the original applications. To the best of our knowledge, this is the first large scale study on the characteristics of cloned mobile applications and their impact on the original developers.


title: The prevalence of bacteraemia-related retinal lesions in seriously ill patients

abstract: Summary Objectives In this study, we investigated the frequency and aetiology of retinal lesions in bacteraemic and septic patients and the risk factors involved. Methods A total of 150 adult patients in our hospital were included in the study. After consultation with the infectious diseases specialist, the following details were recorded: demographic data, area of admission, underlying diseases, Winston's clinical condition, Charlson's co-morbidity index, McCabe's criteria for underlying disease, APACHE II scoring, community or nosocomial acquisition of bacteraemia, and micro-organism responsible. Blood cultures were obtained from all the patients at least three times. All patients were examined for ocular lesions by the same ophthalmologist 48–72h after the first examination. Some long-term hospitalized patients were evaluated more than once. Results Patients were divided into six groups: 18 (12%) were bacteraemic non-septic; 31 (20.7%) were septic bacteraemic; 43 (28.7%) were septic non-bacteraemic; 19 (12.7%) had systemic inflammatory response syndrome (SIRS); 16 (10.7%) were non-septic non-bacteraemic but infectious; and 23 (15.3%) were controls. We found bacteraemia-related retinal lesions (BRRLs) in 22/150 (14.7%) of the patients, 19 of whom (86.4%) were in the septic-bacteraemic group while 3 (13.6%) were in the septic non-bacteraemic group. BRRLs were observed in 19/31 (61.3%) patients in the septic-bacteraemic group. Winston and APACHE II scores were found to be higher in patients with BRRLs than in others. BRRLs were more prevalent in septic or bacteraemic patients with central nervous system (CNS) diseases (31.8%) or cancer (27.3%) than in patients with other diseases. The organism most frequently responsible for bacteraemia in patients with BRRLs was Pseudomonas aeruginosa (27.3%), and the second most common was Staphylococcus aureus (22.7%). Conclusion BRRLs are most frequent in bacteraemic-septic patients (61.3%). The underlying diseases predisposing most to BRRLs are diseases of the CNS and cancers. Ocular examination appears to be a useful aid to diagnosis of bacteraemia or sepsis.


title: Discrete double integrator consensus

abstract: A distributed double integrator discrete time consensus protocol is presented along with stability analysis. The protocol will achieve consensus when the communication topology contains at least a directed spanning tree. Average consensus is achieved when the communication topology is strongly connected and balanced, where average consensus for double integrator systems is discussed. For second order systems average consensus occurs when the information states tend toward the average of the current information states not their initial values. Lastly, perturbation to the consensus protocol is addressed. Using a designed perturbation input, an algorithm is presented that accurately tracks the center of a vehicle formation in a decentralized manner.


title: Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?

abstract: The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. Building security into this new computing paradigm is a major technical challenge. What IoT security problems can we solve using existing security principles? And, what new problems and challenges in this space will require new security mechanisms?


title: Pedestrian Detection with Unsupervised Multi-stage Feature Learning

abstract: Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model. The model uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information, and an unsupervised method based on convolutional sparse coding to pre-train the filters at each stage.


title: CuriOS: Improving Reliability through Operating System Structure

abstract: An error that occurs in a microkernel operating system service can potentially result in state corruption and service failure. A simple restart of the failed service is not always the best solution for reliability. Blindly restarting a service which maintains client-related state such as session information results in the loss of this state and affects all clients that were using the service. CuriOS represents a novel OS design that uses lightweight distribution, isolation and persistence of OS service state to mitigate the problem of state loss during a restart. The design also significantly reduces error propagation within client-related state maintained by an OS service. This is achieved by encapsulating services in separate protection domains and granting access to client-related state only when required for request processing. Fault injection experiments show that it is possible to recover from between 87% and 100% of manifested errors in OS services such as the file system, network, timer and scheduler while maintaining low performance overheads.


title: Near-Saddle-Node Bifurcation Behavior as Dynamics in Working Memory for Goal-Directed Behavior

abstract: In consideration of working memory as a means for goal-directed behavior in nonstationary environments, we argue that the dynamics of working memory should satisfy two opposing demands: long-term maintenance and quick transition. These two characteristics are contradictory within the linear domain. We propose the near-saddle-node bifurcation behavior of a sigmoidal unit with a self-connection as a candidate of the dynamical mechanism that satisfies both of these demands. It is shown in evolutionary programming experiments that the near-saddle-node bifurcation behavior can be found in recurrent networks optimized for a task that requires efficient use of working memory. The results suggests that the near-saddle-node bifurcation behavior may be a functional necessity for survival in nonstationary environments.


Journal of Homeland Security and Emergency Management Disaster Resilience Indicators for Benchmarking Baseline Conditions


title: A review: Information extraction techniques from research papers

abstract: Text extraction is a crucial stage of analyzing Journal papers. Journal papers generally are in PDF format which is semi structured data. Journal papers are presented into different sections like Introduction, Methodology, Experimental setup, Result and analysis etc. so that it is easy to access information from any section as per the reader's interest. The main importance on section extraction is to find a representative subset of the data, which contains the information of the entire set. Various approaches to extract sections from research papers include stastical methods, NLP, Machine Learning etc. In this paper we present review of various extraction techniques from a PDF document.


title: Which cultural values matter to business process management?

abstract: Purpose – Business process management (BPM) requires a holistic perspective that includes managing the culture of an organization to achieve objectives of efficient and effective business processes. Still, the specifics of a BPM-supportive organizational culture have not been examined so far. Thus, the purpose of this paper is to identify the characteristics of a cultural setting supportive of BPM objectives. ::: ::: Design/methodology/approach – The paper examines the constituent values of a BPM-supportive cultural setting through a global Delphi study with BPM experts from academia and practice and explore these values in a cultural value framework. ::: ::: Findings – The paper empirically identifies and defines four key cultural values supporting BPM, viz., customer orientation, excellence, responsibility, and teamwork. The paper discusses the relationships between these values and identifies a particular challenge in managing these seemingly competing values. ::: ::: Research limitations/implications – The identification and definition of these values represents a first step towards the operationalization (and empirical analysis) of what has been identified as the concept of BPM culture, i.e. a culture supportive of achieving BPM objectives. ::: ::: Practical implications – Identifying these cultural values provides the basis for developing an instrument that can measure how far an existing cultural context is supportive of BPM. This, in turn, is fundamental for identifying measures towards achieving a BPM culture as a necessary, yet not sufficient means to obtain BPM success. ::: ::: Originality/value – The paper examines which cultural values create an environment receptive for BPM and, thus, specifies the important theoretical construct BPM culture. In addition, the paper raises awareness for realizing these values in a BPM context.


title: ETARM: an efficient top-k association rule mining algorithm

abstract: Mining association rules plays an important role in data mining and knowledge discovery since it can reveal strong associations between items in databases. Nevertheless, an important problem with traditional association rule mining methods is that they can generate a huge amount of association rules depending on how parameters are set. However, users are often only interested in finding the strongest rules, and do not want to go through a large amount of rules or wait for these rules to be generated. To address those needs, algorithms have been proposed to mine the top-k association rules in databases, where users can directly set a parameter k to obtain the k most frequent rules. However, a major issue with these techniques is that they remain very costly in terms of execution time and memory. To address this issue, this paper presents a novel algorithm named ETARM (Efficient Top-k Association Rule Miner) to efficiently find the complete set of top-k association rules. The proposed algorithm integrates two novel candidate pruning properties to more effectively reduce the search space. These properties are applied during the candidate selection process to identify items that should not be used to expand a rule based on its confidence, to reduce the number of candidates. An extensive experimental evaluation on six standard benchmark datasets show that the proposed approach outperforms the state-of-the-art TopKRules algorithm both in terms of runtime and memory usage.


title: State-of-the-art Chinese Word Segmentation with Bi-LSTMs

abstract: A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. ::: Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve better accuracy on many of the popular datasets as compared to models based on more complex neural-network architectures. ::: Furthermore, our error analysis shows that out-of-vocabulary words remain challenging for neural-network models, and many of the remaining errors are unlikely to be fixed through architecture changes. ::: Instead, more effort should be made on exploring resources for further improvement.


title: Young People, Mobile Technology and the Task Artefact Cycle

abstract: What happens during the earliest stages of technology use? How do users make sense of novel technologies? Why do some people persist in the use of an artefact, when others reject it? On the basis of empirical research, we are capturing the processes followed by young people during their earliest stages of technology use, stages we have called ‘appropriation’. Depending on the balance between what young people desire, the capabilities and implications of technology and the situations of use that young people inhabit, we have observed non-appropriation, appropriation and disappropriation. Design for the longterm persistent use of technology, requires the design process to be mindful of appropriation. In the paper we discuss how such mindfulness can be achieved through scenario-based design and we situate our model of appropriation within the task-artefact cycle.


The Inherent Dangers of Unidirectional Emotional Bonds between Humans and Social Robots


Radio Model-free Noise Reduction of Radio Transmissions with Convolutional Autoencoders


title: Google fusion tables: web-centered data management and collaboration

abstract: It has long been observed that database management systems focus on traditional business applications, and that few people use a database management system outside their workplace. Many have wondered what it will take to enable the use of data management technology by a broader class of users and for a much wider range of applications. Google Fusion Tables represents an initial answer to the question of how data management functionality that focused on enabling new users and applications would look in today's computing environment. This paper characterizes such users and applications and highlights the resulting principles, such as seamless Web integration, emphasis on ease of use, and incentives for data sharing, that underlie the design of Fusion Tables. We describe key novel features, such as the support for data acquisition, collaboration, visualization, and web-publishing.


title: Diagenetic overprint on negative δ13C excursions across the Permian/Triassic boundary: A case study from Meishan section, China

abstract: The Meishan Section D from China is the global stratotype section and point (GSSP) of the Permian-Triassic boundary (PTB). The succession has been divided into 115 beds with the PTB being placed in the middle of Bed 27. In this section and the laterally equivalent Section A, the diagenetic features of Beds 24 to 62 are examined in this study to determine if the negative shift in δ13Ccarb across the Permian-Triassic boundary is diagenetic in origin or not. Bed 24 is formed of skeletal packstones, whereas Beds 25 to 62 are formed of claystones or argillaceous dolomitic limestones. The dolomite crystals are typically < 30 μm long, subhedral to euhedral, and held in a very fine groundmass formed of calcite and clay minerals. The cathodoluminescent features and backscattered images show that the dolomite crystals are zoned, with a non-luminescent low-calcium calcian dolomite (LCD) core being encased successively by an irregular dull-orange calcite zone, a euhedral bright-orange high-calcium calcian dolomite (HCD) cortex, and an outermost ferroan-HCD zone. Available evidence indicates that the zoned dolomite crystals formed episodically. The dolomite crystals, following their initial syndepositional formation with euhedral LCD cores and HCD cortices, were subsequently modified by calcitization mediated by meteoric water and overgrowth of Fe-rich HCD during burial. ::: ::: The limestones in Bed 24 display δ13C values ranging from + 1.72 to + 2.92‰, δ18O values from − 7.74 to − 4.84‰. In Beds 25 to 62, the bulk samples have δ13C values from − 0.76 to + 1.28‰ and δ18O values from − 8.58 to − 3.99‰. There is a positive correlation between these isotopic compositions and dolomite content. It is estimated that the first-formed dolomite and the precursor limestones have δ13C value of + 2.19‰ and δ18O value of − 1.14‰. The dramatic negative δ13C excursions of calcite in Beds 26 and 28 are related to meteoric diagenesis, whereas the negative δ13C excursions of bulk samples in the dolomite-bearing beds are ascribed to the diagenetic modifications that post-dated the initial formation of dolomite. The results show that the δ13C signals recorded in the GSSP that spans the PTB are a mixture of original and diagenetic signals. For the dolomite-bearing bulk samples, diagenesis contributes ~ 1.7‰ to the negative shift in the δ13C. Hence, diagenetic alteration needs to be considered before using δ13Ccarb to estimate the dissolved inorganic carbon in the ancient oceans and to calculate the global carbon cycle.


Students Success in Mathematics and Its Relationship with the Attitude toward Numeracy Learning


title: Sketch-pix2seq: a Model to Generate Sketches of Multiple Categories

abstract: Sketch is an important media for human to communicate ideas, which reflects the superiority of human intelligence. Studies on sketch can be roughly summarized into recognition and generation. Existing models on image recognition failed to obtain satisfying performance on sketch classification. But for sketch generation, a recent study proposed a sequence-to-sequence variational-auto-encoder (VAE) model called sketch-rnn which was able to generate sketches based on human inputs. The model achieved amazing results when asked to learn one category of object, such as an animal or a vehicle. However, the performance dropped when multiple categories were fed into the model. Here, we proposed a model called sketch-pix2seq which could learn and draw multiple categories of sketches. Two modifications were made to improve the sketch-rnn model: one is to replace the bidirectional recurrent neural network (BRNN) encoder with a convolutional neural network(CNN); the other is to remove the Kullback-Leibler divergence from the objective function of VAE. Experimental results showed that models with CNN encoders outperformed those with RNN encoders in generating human-style sketches. Visualization of the latent space illustrated that the removal of KL-divergence made the encoder learn a posterior of latent space that reflected the features of different categories. Moreover, the combination of CNN encoder and removal of KL-divergence, i.e., the sketch-pix2seq model, had better performance in learning and generating sketches of multiple categories and showed promising results in creativity tasks.


title: Hydrophobic collapse of a stearic acid film by adsorbed l-phenylalanine at the air-water interface.

abstract: The surface morphology of atmospheric aerosol particles can influence the particle’s overall effect on climate through enhancing or impeding its ability to uptake and evaporate water. In the work presented here, complementary surface-sensitive information from π–A isotherms, Brewster angle microscopy (BAM), and infrared reflection–absorption spectroscopy (IRRAS) are used to monitor the induced hydrophobic collapse of a surfactant film by an adsorbed amino acid at the air–water interface. The stearic acid film studied here is well-known to form a very stable floating monolayer at the air–water interface, and is shown in this work to withstand isotherm compression–expansion cycles without any premature collapse. With the presence of the water-soluble amino acid l-phenylalanine, however, significant disruption is observed of the stearic acid film, evidenced by the disappearance of its liquid-condensed phase from the isotherm cycles, as well as premature collapse structures observed in the BAM images and a ch...


title: Precise dynamic turning of a 10 cm legged robot on a low friction surface using a tail

abstract: For maximum maneuverability, terrestrial robots need to be able to turn precisely, quickly, and with a small radius. Previous efforts at turning in legged robots primarily have used leg force or velocity modulation. We developed a palm-sized legged robot, called TAYLRoACH. The tailed robot was able to make rapid, precise turns using only the actuation of a tail appendage. By rapidly rotating the tail as the robot runs forward, the robot was able to make sudden 90° turns at 360 °s-1. Unlike other robots, this is done with almost no change in its running speed. We have also modeled the dynamics of this maneuver, to examine how features, such as tail length and mass, affect the robot's turning ability. This approach has produced turns with a radius of 0.4 body lengths at 3.2 body lengths per second running speed. Using gyro feedback and bang-bang control, we achieve an accuracy of ± 5° for a 60° turn.


title: GSNs : Generative Stochastic Networks

abstract: We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. Because the transition distribution is a conditional distribution generally involving a small move, it has fewer dominant modes, being unimodal in the limit of small moves. Thus, it is easier to learn, more like learning to perform supervised function approximation, with gradients that can be obtained by back-propagation. The theorems provided here generalize recent work on the probabilistic interpretation of denoising auto-encoders and provide an interesting justification for dependency networks and generalized pseudolikelihood (along with defining an appropriate joint distribution and sampling mechanism, even when the conditionals are not consistent). We study how GSNs can be used with missing inputs and can be used to sample subsets of variables given the rest. Successful experiments are conducted, validating these theoretical results, on two image datasets and with a particular architecture that mimics the Deep Boltzmann Machine Gibbs sampler but allows training to proceed with backprop, without the need for layerwise pretraining.


title: High Gain and High CMRR Two-Stage Folded Cascode OTA with Nested Miller Compensation

abstract: By using Gm-C compensation (GCC) technique, a two-stage recycling folded cascode (FC) operational transconductance amplifier (OTA) is designed. The proposed configuration consists of recycling structure, positive feedback and feed-forward compensation path. In comparison with the typical folded cascode CMOS Miller amplifier, this design has higher DC gain, unity-gain frequency (UGF), slew rate and common mode rejection ratio (CMRR). The presented OTA is simulated in 0.18-μm CMOS technology and the simulation results confirm the theoretical analyses. Finally, the proposed amplifier has a 111 dB open-loop DC gain, 20 MHz UGF and 145 dB CMRR @ 1.2 V supply voltage while the power consumption is 400 μW which makes it suitable for low-voltage applications.


title: An advanced entropy-based DDOS detection scheme

abstract: Prior work has shown entropy-based DDOS detection is suitable for detecting low-rate DOS. A key challenge in this approach is to determine the most suitable threshold for detecting DDOS attacks accurately. To address this challenge, we propose an advanced entropy-based (AEB) scheme, which divides variable rate DDOS attacks into different fields and treats each field with different methods. Compared with prior entropy-based approaches, our scheme has significant better performance on distinguishing waves of legal traffic and flash crowds from low-rate DOS. We validate the effectiveness of our scheme by conducting extensive simulation in NS-2. Both theoretical analysis and experimental results show that our scheme can efficiently detect DDOS attacks with high accuracy.


title: Fibroadenoma of the Ectopic Axillary Breast Tissue: Sonographic Appearances

abstract: Ectopic breast tissue is reported to occur in 0.4% - 6% of women. Breast neoplasms may develop in ectopic breast tissue, as in eutopic breast tissue. The most frequent condition in ectopic breast is breast cancer. Fibroadenomas of ectopic breast tissue have been reported, but are rare. We report two cases of fibroadenomas of ectopic axillary breast tissues. The sonographic appearance of the nodules was spherical and hypoechoic. We found the component of breast tissue near the nodule. It is important to diagnose fibroadenomas of ectopic breast tissue before a histologic examination.


title: Inkjet-Printed Microstrip Patch Antennas Realized on Textile for Wearable Applications

abstract: This letter introduces a new technique of inkjet printing antennas on textiles. A screen-printed interface layer was used to reduce the surface roughness of the polyester/cotton material that facilitated the printing of a continuous conducting surface. Conducting ink was used to create three inkjet-printed microstrip patch antennas. An efficiency of 53% was achieved for a fully flexible antenna with two layers of ink. Measurements of the antennas bent around a polystyrene cylinder indicated that a second layer of ink improved the robustness to bending.


title: Interactions between Quantifier Scope and Topic/Focus

abstract: This paper explores interactions between quantifier scope and topic/focus. More specifically, it is demonstrated that quantifier raising (as in English) can be accounted for as a consequence of covert topic/focus movement to higher projections. First, the delta QP hypothesis is proposed, which is characterized by (a) a universal principle of movement to Δ projection to satisfy [+topic] and [+focus] features and (b) parameterized strength of these features. Following Lambova's (2004) account of Bulgarian, these features are strong and thus NPs with these features must undergo movement prior to Spell-Out. On the other hand, these features are weak in English and therefore movement must take place after Spell-Out. This follows the absence of Topic/Focus obligatory movement in English. Second, it will be shown that the above proposal indeed corresponds to the scope interpretations of quantifiers under specific topic and focus allocation. Finally, it is shown that the delta QP hypothesis makes accurate predictions for Bulgarian quantifiers.


title: Support for vehicle-to-everything services based on LTE

abstract: Vehicular applications and communications technologies are often referred to as vehicle-to-everything (V2X), which is classified into four different types: vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-network (V2N), and vehicle-to-pedestrian (V2P) [1]. V2X related research projects, field tests, and regulatory work have been promoted in different countries and regions. In May 2015, the Ministry of Industry and Information Technology (MIIT) of China explained national strategies, "Made in China 2025," about intelligent connected vehicles. In 2020 and 2025, the overall technology and key technologies for intelligent driver assistance and automatic driving will be available in China, respectively [2]. V2X solutions are the critical technologies to support the realization of such visions. Although IEEE 802.11p has been selected as the technology for V2X communications in some countries such as the United States and in Europe, the intrinsic characteristics of IEEE 802.11p have confined the technology to support low latency with high reliability [3, 4]. Standardization of Long Term Evolution (LTE)-based V2X is being actively conducted by the Third Generation Partnership Project (3GPP) to provide the solution for V2X communications that benefit from the global deployment and fast commercialization of LTE systems. Because of the wide deployment of LTE networks, V2I and V2N services can be provided with high data rate, comprehensive quality of service (QoS) support, ubiquitous coverage, and high penetration rate [5]. Meanwhile, LTE can be extended to support V2V direct communications based on device-to-device (D2D) sidelink design to satisfy the QoS requirements, such as low latency, high reliability, and high speed in the case of high vehicle density [6].


title: The place of knowledge management in influencing lasting health change in Africa: an analysis of AMREF's progress

abstract: INTRODUCTION ::: AMREF (African Medical and Research Foundation) developed a Knowledge Management Strategy that focused on creating, capturing and applying health knowledge to close the gap between communities and health systems in Africa. There was need to identify AMREF's current Knowledge Management implementation status, problems and constraints encountered after two years of enforcement of the strategy and suggest the way forward. ::: ::: ::: METHODS ::: This study was conducted between October 2011 and February 2012. Quantitative data on number and foci of AMREF research publications were collected using a questionnaire. Focus group discussions and in-depth interviews were used to gather data on explanations for the trend of publications and the status of the implementation of the 2010-2014 Knowledge Management Strategy. Quantitative data was analysed using SPSS computer software whereas content analysis of themes was employed on qualitative data. ::: ::: ::: RESULTS ::: Between 1960 and 2011, AMREF produced 257 peer reviewed publications, 158 books and manuals and about 1,188 technical publications including evaluations, guidelines and technical reports. However, the numbers of publications declined from around the year 2000. Large quantities of unpublished and unclassified materials are also in the custody of Heritage. Barriers to Knowledge Management included: lack of incentives for documentation and dissemination; limited documentation and use of good practices in programming; and superficial attention to results or use of evidence. ::: ::: ::: CONCLUSION ::: Alternative ways of reorganizing Knowledge Management will enable AMREF to use evidence-based knowledge to advocate for appropriate changes in African health policies and practices.


title: Using Tectogrammatical Annotation for Studying Actors and Actions in Sallust’s Bellum Catilinae

abstract: Abstract In the context of the Index Thomisticus Treebank project, we have enhanced the full text of Bellum Catilinae by Sallust with semantic annotation. The annotation style resembles the one used for the so called “tectogrammatical” layer of the Prague Dependency Treebank. By exploiting the results of semantic role labeling, ellipsis resolution and coreference analysis, this paper presents a network-based study of the main Actors and Actions (and their relations) in Bellum Catilinae.


title: On the Direction of Arrival (DoA) Estimation for a Switched-Beam Antenna System Using Neural Networks

abstract: A generic direction of arrival (DoA) estimation methodology is presented that is based on neural networks (NNs) and designed for a switched-beam system (SBS). The method incorporates the benefits of NNs and SBSs to achieve DoA estimation in a less complex and expensive way compared to the corresponding widely known super resolution algorithms. The proposed technique is step-by-step developed and thoroughly studied and explained, especially in terms of the beam pattern structure and the neuro-computational procedures. Emphasis is given on the direct sequence code division multiple access (DS-CDMA) applications, and particularly the Universal Mobile Telecommunication System (UMTS). Extensive simulations are realized for each step of the method, demonstrating its performance. It is shown that a properly trained NN can accurately find the signal of interest (SoI) angle of arrival at the presence of a varying number of mobile users and a varying SoI to interference ratio. The proposed NN-SBS DoA estimation method can be applied to current cellular communications base stations, promoting the wider use of smart antenna beamforming.


title: Indoor localization framework with WiFi fingerprinting

abstract: Indoor localization through WiFi fingerprinting requires a large number of fine-grained data samples. This study presents a data acquisition and indoor localization framework that collects crowd-sourced WiFi received signal strength data in a metropolitan high-rise building and predicts location through WiFi fingerprinting. The framework consists of a server and an Android application and was tested at NYIT for data collection for two weeks in December 2016. The dataset was preprocessed and analyzed through linear support vector machine to test location prediction accuracy. Various feature selection schemes were compared for their location prediction accuracy. We show that a small subset of features suffices to provide high location prediction accuracy. The average location prediction accuracy increases from 83% to 100% when time features are considered comparing to using only spatial features.


Lifting Wavelet Transform and Singular Values Decomposition for Secure Image Watermarking


title: A passivity based Cartesian impedance controller for flexible joint robots - part II: full state feedback, impedance design and experiments

abstract: The paper presents a Cartesian impedance controller for flexible joint robots based on the feedback of the complete state of the system, namely the motor position, the joint torque and their derivatives. The approach is applied to a quite general robot model, in which also a damping element is considered in parallel to the joint stiffness. Since passivity and asymptotic stability of the controller hold also for varying damping matrices, some possibilities of designing those gain matrices (depending on the actual inertia matrix) are addressed. The passivity of the controller relies on the usage of only motor side measurements for the position feedback. A method is introduced, which provides the exact desired link side stiffness based on this motor position information. Experimental results are validating the proposed controller.


title: Periodic Ciphers with Small Blocks and Cryptanalysis of KeeLoq

abstract: ABSTRACT. KeeLoq is a lightweight block cipher that is massively used in theautomobile industry [12, 13, 31, 32]. KeeLoq has two remarkable properties: itis periodic and has a very short block size (32 bits). Many diﬁerent attacks onKeeLoq have been published in recent years [8, 15, 9, 10, 5]. In this paper we studya unique way of attacking KeeLoq, in which the periodic property of KeeLoq isused in to distinguish 512 rounds of KeeLoq from a random permutation. Ourattacks require the knowledge of the entire code-book and are not among thefastest attacks known on this cipher. However one of them works for 100 % ofall keys, including so called \strong keys", see [15]. In general, it is important toshow how many diﬁerent attacks are possible on a weak cipher such as KeeLoq. 1. Introduction KeeLoq was designed in the 1980’s by Willem Smit from South Africa [36].It is a block cipher used in wireless devices that unlock the doors of cars manu-factured by Chrysler, Daewoo, Fiat, GM, Honda, Jaguar, Toyota, Volvo, Volk-swagen, etc...[12, 13, 31, 32]. In 1995 KeeLoq was sold to Microchip TechnologyInc. for more than 10 million US dollars (which is documented in [12]). FollowingMicrochip, [33], the speciﬂcation of KeeLoq is \not secret" but is patented andwas released only under license. The question whether one can really break intocars, and how, is a secondary one in a scientiﬂc paper. The main question iswhat ciphers could be broken, how, due to what properties, and how to designbetter ciphers.KeeLoq operates with 32-bit blocks and 64-bit keys. Compared to the typ-ical block ciphers which have a few carefully-designed rounds, this cipher has528 extremely simple rounds with extremely few intermediate variables (in ourformulation one per round). Also, only one bit of the state is modiﬂed in eachround. As a result, KeeLoq can be implemented very e–ciently in hardware.


title: When Privacy meets Security: Leveraging personal information for password cracking

abstract: Passwords are widely used for user authentication and, despite their weaknesses, will likely remain in use in the foreseeable future. Human-generated passwords typically have a rich structure, which makes them susceptible to guessing attacks. In this paper, we study the effectiveness of guessing attacks based on Markov models. Our contributions are two-fold. First, we propose a novel password cracker based on Markov models, which builds upon and extends ideas used by Narayanan and Shmatikov (CCS 2005). In extensive experiments we show that it can crack up to 69% of passwords at 10 billion guesses, more than all probabilistic password crackers we compared again t. Second, we systematically analyze the idea that additional personal information about a user helps in speeding up password guessing. We find that, on average and by carefully choosing parameters, we can guess up to 5% more passwords, especially when the number of attempts is low. Furthermore, we show that the gain can go up to 30% for passwords that are actually based on personal attributes. These passwords are clearly weaker and should be avoided. Our cracker could be used by an organization to detect and reject them. To the best of our knowledge, we are the first to systematically study the relationship between chosen passwords and users' personal information. We test and validate our results over a wide collection of leaked password databases.


title: A Novel Broadband Circularly Polarized Antenna Based on Off-Center-Fed Dipoles

abstract: A novel broadband circularly polarized (CP) antenna is proposed. The operating principle of this CP antenna is different from those of conventional CP antennas. An off-center-fed dipole is introduced to achieve the 90° phase difference required for circular polarization. The new CP antenna consists of two off-center-fed dipoles. Combining such two new CP antennas leads to a bandwidth enhancement for circular polarization. A T-shaped microstrip probe is used to excite the broadband CP antenna, featuring a simple planar configuration. It is shown that the new broadband CP antenna achieves an axial ratio (AR) bandwidth of 55% (1.69–3.0 GHz) for ${\mathbf{AR}} , an impedance bandwidth of 60% (1.7–3.14 GHz) for return loss (RL) $> {\mathbf{15}}\;{\mathbf{dB}}$ , and an antenna gain of 6–9 dBi. The new mechanism for circular polarization is described and an experimental verification is presented.


title: Principles of cognitive science in education: The effects of generation, errors, and feedback

abstract: Principles of cognitive science hold the promise of helping children to study more effectively, yet they do not always make successful transitions from the laboratory to applied settings and have rarely been tested in such settings. For example, self-generation of answers to questions should help children to remember. But what if children cannot generate anything? And what if they make an error? Do these deviations from the laboratory norm of perfect generation hurt, and, if so, do they hurt enough that one should, in practice, spurn generation? Can feedback compensate, or are errors catastrophic? The studies reviewed here address three interlocking questions in an effort to better implement a computer-based study program to help children learn: (1) Does generation help? (2) Do errors hurt if they are corrected? And (3) what is the effect of feedback? The answers to these questions are: Yes, generation helps; no, surprisingly, errors that are corrected do not hurt; and, finally, feedback is beneficial in verbal learning. These answers may help put cognitive scientists in a better position to put their well-established principles in the service of children’s learning.


title: Adapting a parallel sparse direct solver to architectures with clusters of SMPs

abstract: We consider the direct solution of general sparse linear systems baseds on a multifrontal method. The approach combines partial static scheduling of the task dependency graph during the symbolic factorization and distributed dynamic scheduling during the numerical factorization to balance the work among the processes of a distributed memory computer. We show that to address clusters of Symmetric Multi-Processor (SMP) architectures, and more generally non-uniform memory access multiprocessors, our algorithms for both the static and the dynamic scheduling need to be revisited to take account of the non-uniform cost of communication. The performance analysis on an IBM SP3 with 16 processors per SMP node and up to 128 processors shows that we can significantly reduce both the amount of inter-node communication and the solution time.


title: Extractive Summarization using Deep Learning

abstract: This paper proposes a text summarization approach for factual reports using a deep learning model. This approach consists of three phases: feature extraction, feature enhancement, and summary generation, which work together to assimilate core information and generate a coherent, understandable summary. We are exploring various features to improve the set of sentences selected for the summary, and are using a Restricted Boltzmann Machine to enhance and abstract those features to improve resultant accuracy without losing any important information. The sentences are scored based on those enhanced features and an extractive summary is constructed. Experimentation carried out on several articles demonstrates the effectiveness of the proposed approach. Source code available at: this https URL


title: A machine learning approach for personalized autonomous lane change initiation and control

abstract: We study an algorithm that allows a vehicle to autonomously change lanes in a safe but personalized fashion without the driver's explicit initiation (e.g. activating the turn signals). Lane change initiation in autonomous driving is typically based on subjective rules, functions of the positions and relative velocities of surrounding vehicles. This approach is often arbitrary, and not easily adapted to the driving style preferences of an individual driver. Here we propose a data-driven modeling approach to capture the lane change decision behavior of human drivers. We collect data with a test vehicle in typical lane change situations and train classifiers to predict the instant of lane change initiation with respect to the preferences of a particular driver. We integrate this decision logic into a model predictive control (MPC) framework to create a more personalized autonomous lane change experience that satisfies safety and comfort constraints. We show the ability of the decision logic to reproduce and differentiate between two lane changing styles, and demonstrate the safety and effectiveness of the control framework through simulations.


title: Convex Optimization of Wireless Power Transfer Systems With Multiple Transmitters

abstract: Wireless power transfer systems with multiple transmitters promise advantages of higher transfer efficiencies and focusing effects over single-transmitter systems. From the standard formulation, straightforward maximization of the power transfer efficiency is not trivial. By reformulating the problem, a convex optimization problem emerges, which can be solved efficiently. Further, using Lagrangian duality theory, analytical results are found for the achievable maximum power transfer efficiency and all parameters involved. With these closed-form results, planar and coaxial wireless power transfer setups are investigated.


title: TCP-like congestion control for layered multicast data transfer

abstract: We present a novel congestion control algorithm suitable for use with cumulative, layered data streams in the MBone. Our algorithm behaves similarly to TCP congestion control algorithms, and shares bandwidth fairly with other instances of the protocol and with TCP flows. It is entirely receiver driven and requires no per-receiver status at the sender, in order to scale to large numbers of receivers. It relies on standard functionalities of multicast routers, and is suitable for continuous stream and reliable bulk data transfer. In the paper we illustrate the algorithm, characterize its response to losses both analytically and by simulations, and analyse its behaviour using simulations and experiments in real networks. We also show how error recovery can be dealt with independently from congestion control by using FEC techniques, so as to provide reliable bulk data transfer.


title: Capsicum: Practical Capabilities for UNIX

abstract: Capsicum is a lightweight operating system capability and sandbox framework planned for inclusion in FreeBSD 9. Capsicum extends, rather than replaces, UNIX APIs, providing new kernel primitives (sandboxed capability mode and capabilities) and a userspace sandbox API. These tools support compartmentalisation of monolithic UNIX applications into logical applications, an increasingly common goal supported poorly by discretionary and mandatory access control. We demonstrate our approach by adapting core FreeBSD utilities and Google's Chromium web browser to use Capsicum primitives, and compare the complexity and robustness of Capsicum with other sandboxing techniques.


title: Optimal Design of Focused Antenna Arrays

abstract: In this paper, we propose an optimal design procedure for the focused antenna array based on the maximization of power transmission efficiency between two antennas. In the optimization process, the focused antenna array to be designed is used as the transmitting antenna and a testing antenna is used as the receiving antenna, positioned in a desired area to be focused. An optimal excitation distribution can thus be obtained for the antenna array to be focused in the desired area. Three 4 × 4 microstrip patch arrays with different focusing distances are designed to validate the optimization technique and demonstrate the focusing capabilities when the antenna array size is fixed. A 6 × 6 microstrip patch array is also built to reveal how the antenna size enhances the focusing effect.


title: Ingestion and transfer of microplastics in the planktonic food web.

abstract: Experiments were carried out with different Baltic Sea zooplankton taxa to scan their potential to ingest plastics. Mysid shrimps, copepods, cladocerans, rotifers, polychaete larvae and ciliates were exposed to 10 μm fluorescent polystyrene microspheres. These experiments showed ingestion of microspheres in all taxa studied. The highest percentage of individuals with ingested spheres was found in pelagic polychaete larvae, Marenzelleria spp. Experiments with the copepod Eurytemora affinis and the mysid shrimp Neomysis integer showed egestion of microspheres within 12 h. Food web transfer experiments were done by offering zooplankton labelled with ingested microspheres to mysid shrimps. Microscopy observations of mysid intestine showed the presence of zooplankton prey and microspheres after 3 h incubation. This study shows for the first time the potential of plastic microparticle transfer via planktonic organisms from one trophic level (mesozooplankton) to a higher level (macrozooplankton). The impacts of plastic transfer and possible accumulation in the food web need further investigations.


title: Strategic Implications of Openness in AI Development

abstract: This paper attempts a preliminary analysis of the global desirability of different forms of openness in AI development (including openness about source code, science, data, safety techniques, capabilities, and goals). Short-term impacts of increased openness appear mostly socially beneficial in expectation. The strategic implications of medium and long-term impacts are complex. The evaluation of long-term impacts, in particular, may depend on whether the objective is to benefit the present generation or to promote a time-neutral aggregate of well-being of future generations. Some forms of openness are plausibly positive on both counts (openness about safety measures, openness about goals). Others (openness about source code, science, and possibly capability) could lead to a tightening of the competitive situation around the time of the introduction of advanced AI, increasing the probability that winning the AI race is incompatible with using any safety method that incurs a delay or limits performance. We identify several key factors that must be taken into account by any well-founded opinion on the matter.


abstract: Title. Turnover intention in new graduate nurses: a multivariate analysis ::: ::: Aim. This paper is a report of a study to determine the relationship of new nurse turnover intent with individual characteristics, work environment variables and organizational factors and to compare new nurse turnover with actual turnover in the 18 months of employment following completion of a residency. ::: ::: Background. Because of their influence on patient safety and health outcomes nurse turnover and turnover intent have received considerable attention worldwide. When nurse staffing is inadequate, especially during nursing shortages, unfavourable clinical outcomes have been documented. ::: ::: Method. Prospective data collection took place from 1999 to 2006 with 889 new paediatric nurses who completed the same residency. Scores on study instruments were related to likelihood of turnover intent using logistic regression analysis models. Relationships between turnover intent and actual turnover were compared using Kaplan–Meier survivorship. ::: ::: Results. The final model demonstrated that older respondents were more likely to have turnover intent if they did not get their ward choice. Also higher scores on work environment and organizational characteristics contributed to likelihood that the new nurse would not be in the turnover intent group. These factors distinguish a new nurse with turnover intent from one without 79% of the time. Increased seeking of social support was related to turnover intent and older new graduates were more likely to be in the turnover intent group if they did not get their ward choice. ::: ::: Conclusion. When new graduate nurses are satisfied with their jobs and pay and feel committed to the organization, the odds against turnover intent decrease.


PRODUCTION AND SUPPORTIVE POLICES IN IRAN (CASE STUDY OF WHEAT, RICE AND BARLEY)*


title: Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

abstract: Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of ``what to fuse'', ``when to fuse'', and ``how to fuse'' remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/.


title: Information-Theoretic Limits of Strategic Communication

abstract: In this article, we investigate strategic information transmission over a noisy channel. This problem has been widely investigated in Economics, when the communication channel is perfect. Unlike in Information Theory, both encoder and decoder have distinct objectives and choose their encoding and decoding strategies accordingly. This approach radically differs from the conventional Communication paradigm, which assumes transmitters are of two types: either they have a common goal, or they act as opponent, e.g. jammer, eavesdropper. We formulate a point-to-point source-channel coding problem with state information, in which the encoder and the decoder choose their respective encoding and decoding strategies in order to maximize their long-run utility functions. This strategic coding problem is at the interplay between Wyner-Ziv's scenario and the Bayesian persuasion game of Kamenica-Gentzkow. We characterize a single-letter solution and we relate it to the previous results by using the concavification method. This confirms the benefit of sending encoded data bits even if the decoding process is not supervised, e.g. when the decoder is an autonomous device. Our solution has two interesting features: it might be optimal not to use all channel resources; the informational content impacts the encoding process, since utility functions capture preferences on source symbols.


title: Understanding user intent in community question answering

abstract: Community Question Answering (CQA) services, such as Yahoo! Answers, are specifically designed to address the innate limitation of Web search engines by helping users obtain information from a community. Understanding the user intent of questions would enable a CQA system identify similar questions, find relevant answers, and recommend potential answerers more effectively and efficiently. In this paper, we propose to classify questions into three categories according to their underlying user intent: subjective, objective, and social. In order to identify the user intent of a new question, we build a predictive model through machine learning based on both text and metadata features. Our investigation reveals that these two types of features are conditionally independent and each of them is sufficient for prediction. Therefore they can be exploited as two views in co-training - a semi-supervised learning framework - to make use of a large amount of unlabelled questions, in addition to the small set of manually labelled questions, for enhanced question classification. The preliminary experimental results show that co-training works significantly better than simply pooling these two types of features together.


title: “Cultures in negotiation”: teachers’ acceptance/resistance attitudes considering the infusion of technology into schools

abstract: A teachers' training project, employing teacher-mentored in-school training approach has been recently initiated in Greek secondary education for the introduction of Information and Communication Technology (ICT) into the classroom. Data resulting from this project indicate that although teachers express considerable interest in learning how to use technology they need consistent support and extensive training in order to consider themselves able for integrating it into their instructional practice. Teachers are interested in using ICT (1) to attain a better professional profile, and (2) to take advantage of any possible learning benefits offered by ICT but always in the context of the school culture. They are willing to explore open and communicative modes of ICT-based teaching whenever school objectives permit, otherwise they appear to cautiously adapt the use of ICT to the traditional teacher-centered mode of teaching (strongly connected to the established student examination system). Teachers' attitude to adapt ICT mode of use is supported by research evidence that emphasize the situational character of knowledge and expertise. Authors discuss the view that introducing ICT into schools can be understood as initiating a "negotiation" process between cultures and the way that technological tools are used reflects school "single context" epistemological stance.


title: Cross-Lingual Type Inference

abstract: Entity typing is an essential task for constructing a knowledge base. However, many non-English knowledge bases fail to type their entities due to the absence of a reasonable local hierarchical taxonomy. Since constructing a widely accepted taxonomy is a hard problem, we propose to type these non-English entities with some widely accepted taxonomies in English, such as DBpedia, Yago and Freebase. We define this problem as cross-lingual type inference. In this paper, we present CUTE to type Chinese entities with DBpedia types. First we exploit the cross-lingual entity linking between Chinese and English entities to construct the training data. Then we propose a multi-label hierarchical classification algorithm to type these Chinese entities. Experimental results show the effectiveness and efficiency of our method.


title: Diagnostic Rule Extraction Using Neural Networks

abstract: 2 The neural networks have trained on incomplete sets that a doctor could collect. Trained neural networks have correctly classified all the presented instances. The number of intervals entered for encoding the quantitative variables is equal two. The number of features as well as the number of neurons and layers in trained neural net- works was minimal. Trained neural networks are adequately represented as a set of logical formulas that more comprehensible and easy-to-understand. These formulas are as the syndrome-complexes, which may be easily tabulated and represented as a diagnostic tables that the doctors usually use. Decision rules provide the evaluations of their confidence in which interested a doctor. Conducted clinical researches have shown that diagnostic decisions produced by symbolic rules have coincided with the doctor's conclusions.


title: Emotion Perception from Face, Voice, and Touch: Comparisons and Convergence

abstract: Historically, research on emotion perception has focused on facial expressions, and findings from this modality have come to dominate our thinking about other modalities. Here we examine emotion perception through a wider lens by comparing facial with vocal and tactile processing. We review stimulus characteristics and ensuing behavioral and brain responses and show that audition and touch do not simply duplicate visual mechanisms. Each modality provides a distinct input channel and engages partly nonoverlapping neuroanatomical systems with different processing specializations (e.g., specific emotions versus affect). Moreover, processing of signals across the different modalities converges, first into multi- and later into amodal representations that enable holistic emotion judgments.


title: SEEM: a scalable visualization for comparing multiple large sets of attributes for malware analysis

abstract: Recently, the number of observed malware samples has rapidly increased, expanding the workload for malware analysts. Most of these samples are not truly unique, but are related through shared attributes. Identifying these attributes can enable analysts to reuse analysis and reduce their workload. Visualizing malware attributes as sets could enable analysts to better understand the similarities and differences between malware. However, existing set visualizations have difficulty displaying hundreds of sets with thousands of elements, and are not designed to compare different types of elements between sets, such as the imported DLLs and callback domains across malware samples. Such analysis might help analysts, for example, to understand if a group of malware samples are behaviorally different or merely changing where they send data. To support comparisons between malware samples' attributes we developed the Similarity Evidence Explorer for Malware (SEEM), a scalable visualization tool for simultaneously comparing a large corpus of malware across multiple sets of attributes (such as the sets of printable strings and function calls). SEEM's novel design breaks down malware attributes into sets of meaningful categories to compare across malware samples, and further incorporates set comparison overviews and dynamic filtering to allow SEEM to scale to hundreds of malware samples while still allowing analysts to compare thousands of attributes between samples. We demonstrate how to use SEEM by analyzing a malware sample from the Mandiant APT1 New York Times intrusion dataset. Furthermore, we describe a user study with five cyber security researchers who used SEEM to rapidly and successfully gain insight into malware after only 15 minutes of training.


title: Pole-Like Road Object Detection in Mobile LiDAR Data via Supervoxel and Bag-of-Contextual-Visual-Words Representation

abstract: This letter addresses the problem of detecting pole-like road objects (including light poles and traffic signposts) from mobile light detection and ranging (LiDAR) data for transportation-related applications. The method consists of two consecutive stages: training and pole-like object detection. At the training stage, a contextual visual vocabulary is created from the feature regions generated from a training data set by supervoxel segmentation. At the pole-like object detection stage, a bag-of-contextual-visual-words representation is generated for each semantic object segmented from mobile LiDAR data. The experimental results show that the proposed method achieves correctness, omission, and commission of 88.9%, 11.1%, and 2.8%, respectively, in detecting pole-like road objects. Computational complexity analysis demonstrates that our method provides a promising and effective solution to rapid and accurate detection of pole-like objects from large volumes of mobile LiDAR data.


title: Study on an Agricultural Environment Monitoring Server System using Wireless Sensor Networks

abstract: This paper proposes an agricultural environment monitoring server system for monitoring information concerning an outdoors agricultural production environment utilizing Wireless Sensor Network (WSN) technology. The proposed agricultural environment monitoring server system collects environmental and soil information on the outdoors through WSN-based environmental and soil sensors, collects image information through CCTVs, and collects location information using GPS modules. This collected information is converted into a database through the agricultural environment monitoring server consisting of a sensor manager, which manages information collected from the WSN sensors, an image information manager, which manages image information collected from CCTVs, and a GPS manager, which processes location information of the agricultural environment monitoring server system, and provides it to producers. In addition, a solar cell-based power supply is implemented for the server system so that it could be used in agricultural environments with insufficient power infrastructure. This agricultural environment monitoring server system could even monitor the environmental information on the outdoors remotely, and it could be expected that the use of such a system could contribute to increasing crop yields and improving quality in the agricultural field by supporting the decision making of crop producers through analysis of the collected information.


title: Synchronization for QDPSK —Costas loop and Gardner algorithm using FPGAs

abstract: This paper discusses the mathematical model and implements the physical verification when Carrier synchronization (carrier loop) and Timing synchronization (Gardner algorithm) working simultaneously. Firstly, a brief analysis of the above mentioned systems is completed, and the functional simulation is accomplished by Verilog HDL code on ModelSim platform. In addition, a systematic analysis is done for the workflow and interrelation of Carrier synchronization and Timing synchronization when both of them are active. Finally, we implement the hardware verification on the Stratix series field programmable gate array (FPGA) device EP3SL150F1152C2N of Altera Company, and give the engineering results through the RF part and space transmission.


title: TensorTextures: multilinear image-based rendering

abstract: This paper introduces a tensor framework for image-based rendering. In particular, we develop an algorithm called TensorTextures that learns a parsimonious model of the bidirectional texture function (BTF) from observational data. Given an ensemble of images of a textured surface, our nonlinear, generative model explicitly represents the multifactor interaction implicit in the detailed appearance of the surface under varying photometric angles, including local (per-texel) reflectance, complex mesostructural self-occlusion, interreflection and self-shadowing, and other BTF-relevant phenomena. Mathematically, TensorTextures is based on multilinear algebra, the algebra of higher-order tensors, hence its name. It is computed through a decomposition known as the N-mode SVD, an extension to tensors of the conventional matrix singular value decomposition (SVD). We demonstrate the application of TensorTextures to the image-based rendering of natural and synthetic textured surfaces under continuously varying viewpoint and illumination conditions.


title: A CMOS bandgap voltage reference

abstract: A simple micropower CMOS bandgap voltage reference is described. The reference utilizes MOS devices operating in the weak inversion region in conjunction with a process compatible bipolar device. The voltage reference is insensitive to threshold and mobility variations and is independent of the slope factor which characterizes weak inversion.


title: Do unto others or treat yourself? The effects of prosocial and self-focused behavior on psychological flourishing.

abstract: When it comes to the pursuit of happiness, popular culture encourages a focus on oneself. By contrast, substantial evidence suggests that what consistently makes people happy is focusing prosocially on others. In the current study, we contrasted the mood- and well-being-boosting effects of prosocial behavior (i.e., doing acts of kindness for others or for the world) and self-oriented behavior (i.e., doing acts of kindness for oneself) in a 6-week longitudinal experiment. Across a diverse sample of participants (N = 473), we found that the 2 types of prosocial behavior led to greater increases in psychological flourishing than did self-focused and neutral behavior. In addition, we provide evidence for mechanisms explaining the relative improvements in flourishing among those prompted to do acts of kindness-namely, increases in positive emotions and decreases in negative emotions. Those assigned to engage in self-focused behavior did not report improved psychological flourishing, positive emotions, or negative emotions relative to controls. The results of this study contribute to a growing literature supporting the benefits of prosocial behavior and challenge the popular perception that focusing on oneself is an optimal strategy to boost one's mood. People striving for happiness may be tempted to treat themselves. Our results, however, suggest that they may be more successful if they opt to treat someone else instead. (PsycINFO Database Record


title: Learning to Transform Natural to Formal Languages

abstract: This paper presents a method for inducing transformation rules that map natural-language sentences into a formal query or command language. The approach assumes a formal grammar for the target representation language and learns transformation rules that exploit the non-terminal symbols in this grammar. The learned transformation rules incrementally map a natural-language sentence or its syntactic parse tree into a parse-tree for the target formal language. Experimental results are presented for two corpora. one which maps English instructions into an existing formal coaching language for simulated RoboCup soccer agents, and another which maps English U.S.-geography questions into a database query language. We show that our method performs overall better and faster than previous approaches in both domains.


title: Anatomical Basis for Safe and Effective Volumization of the Temple.

abstract: BACKGROUND ::: One of the earliest but often unaddressed signs of facial aging is volume loss in the temple. Treatment of the area can produce satisfying results for both patient and practitioner. ::: ::: ::: OBJECTIVE ::: Safe injection requires explicit knowledge of the anatomy to avoid complications related to the multitude of vessels that course throughout the region at various depths. The authors aim to detail the anatomy of the area and provide a safe and easy-to-follow method for injection. ::: ::: ::: MATERIALS AND METHODS ::: The authors review the relevant anatomy of the temporal region and its application to cosmetic filler injections. ::: ::: ::: RESULTS ::: The authors describe an easy-to-follow approach for a safe and effective injection window based on numerous anatomical studies. Injection in this area is not without risk, including potential blindness. The authors review the potential complications and their treatments. ::: ::: ::: CONCLUSION ::: Hollowing of the temple is an early sign of aging that, when corrected, can lead to significant patient and practitioner satisfaction. Proper anatomically knowledge is required to avoid potentially severe complications. In this study, the authors present a reliable technique to safely and effectively augment this often undertreated area of the aging face.


title: Dual word and document seed selection for semi-supervised sentiment classification

abstract: Semi-supervised sentiment classification aims to train a classifier with a small number of labeled data (called seed data) and a large amount of unlabeled data. a big advantage of this approach is its saving of annotation effort by using the unlabeled data which is usually freely available. In this paper, we propose an approach to further minimize the annotation effort of semi-supervised sentiment classification by actively selecting the seed data. Specifically, a novel selection strategy is proposed to simultaneously select good words and documents for manual annotation by considering both of their annotation costs and informativeness. Experimental results demonstrate the effectiveness of our approach.


title: A Gaze-Contingent Adaptive Virtual Reality Driving Environment for Intervention in Individuals with Autism Spectrum Disorders

abstract: In addition to social and behavioral deficits, individuals with Autism Spectrum Disorder (ASD) often struggle to develop the adaptive skills necessary to achieve independence. Driving intervention in individuals with ASD is a growing area of study, but it is still widely under-researched. We present the development and preliminary assessment of a gaze-contingent adaptive virtual reality driving simulator that uses real-time gaze information to adapt the driving environment with the aim of providing a more individualized method of driving intervention. We conducted a small pilot study of 20 adolescents with ASD using our system: 10 with the adaptive gaze-contingent version of the system and 10 in a purely performance-based version. Preliminary results suggest that the novel intervention system may be beneficial in teaching driving skills to individuals with ASD.


title: Semantic Model Vectors for Complex Video Event Recognition

abstract: We propose semantic model vectors, an intermediate level semantic representation, as a basis for modeling and detecting complex events in unconstrained real-world videos, such as those from YouTube. The semantic model vectors are extracted using a set of discriminative semantic classifiers, each being an ensemble of SVM models trained from thousands of labeled web images, for a total of 280 generic concepts. Our study reveals that the proposed semantic model vectors representation outperforms-and is complementary to-other low-level visual descriptors for video event modeling. We hence present an end-to-end video event detection system, which combines semantic model vectors with other static or dynamic visual descriptors, extracted at the frame, segment, or full clip level. We perform a comprehensive empirical study on the 2010 TRECVID Multimedia Event Detection task (http://www.nist.gov/itl/iad/mig/med10.cfm), which validates the semantic model vectors representation not only as the best individual descriptor, outperforming state-of-the-art global and local static features as well as spatio-temporal HOG and HOF descriptors, but also as the most compact. We also study early and late feature fusion across the various approaches, leading to a 15% performance boost and an overall system performance of 0.46 mean average precision. In order to promote further research in this direction, we made our semantic model vectors for the TRECVID MED 2010 set publicly available for the community to use (http://www1.cs.columbia.edu/~mmerler/SMV.html).


title: Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

abstract: Abstract In this article, we present results on the identification and behavioral analysis of social bots in a sample of 542,584 Tweets, collected before and after Japan's 2014 general election. Typical forms of bot activity include massive Retweeting and repeated posting of (nearly) the same message, sometimes used in combination. We focus on the second method and present (1) a case study on several patterns of bot activity, (2) methodological considerations on the automatic identification of such patterns and the prerequisite near-duplicate detection, and (3) we give qualitative insights into the purposes behind the usage of social/political bots. We argue that it was in the latency of the semi-public sphere of social media—and not in the visible or manifest public sphere (official campaign platform, mass media)—where Shinzō Abe's hidden nationalist agenda interlocked and overlapped with the one propagated by organizations such as Nippon Kaigi and Internet right-wingers (netto uyo) during the election c...


title: Understanding Concept Drift

abstract: Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning. We argue that to tackle concept drift it is important to develop the capacity to describe and analyze it. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift in marginal distributions. We present quantitative drift analysis techniques along with methods for communicating their results. We demonstrate their effectiveness by application to three real-world learning tasks.


title: Embodied social interaction for service robots in hallway environments

abstract: A key aspect of service robotics for everyday use is the motion in close proximity to humans. It is essential that the robot exhibits a behavior that signals safety of motion and awareness of the persons in the environment. To achieve this, there is a need to define control strategies that are perceived as socially acceptable by users that are not familiar with robots. In this paper a system for navigation in a hallway is presented, in which the rules of proxemics are used to define the interaction strategies. The experimental results show the contribution to the establishment of effective spatial interaction patterns between the robot and a person.


title: Medical matters: Primary syphilis remains a cause of oral ulceration

abstract: After many years the incidence of infective syphilis (infection with Treponema pallidum) is increasing in the United Kingdom. This may reflect changes in sexual attitudes and behaviour, altering trends in HIV disease, and increased foreign travel, Oral disease as a consequence of primary syphilis is rare. The present report details two patients presenting to an oral medicine clinic in London, within a 6-month period in 1999, with oral ulceration as their only clinical manifestation of undiagnosed primary syphilis. The oral aspects of early syphilis and the need for dentists to be aware of changing epidemiological trends in relevant infectious diseases are highlighted.


title: Chang's Conjecture and semiproperness of nonreasonable posets

abstract: Let $\mathbb{Q}$ denote the poset which adds a Cohen real then shoots a club through the complement of $\big( [\omega_2]^\omega \big)^V$ with countable conditions. We prove that the version of Strong Chang's Conjecture from \cite{MR2965421} implies semiproperness of $\mathbb{Q}$, and that semiproperness of $\mathbb{Q}$---in fact semiproperness of any poset which is sufficiently \emph{nonreasonable} in the sense of Foreman-Magidor~\cite{MR1359154}---implies the version of Strong Chang's Conjecture from \cite{MR2723878} and \cite{MR1261218}. In particular, semiproperness of $\mathbb{Q}$ has large cardinal strength, which answers a question of Friedman-Krueger~\cite{MR2276627}. One corollary of our work is that the version of Strong Chang's Conjecture from \cite{MR2965421} does not imply the existence of a precipitous ideal on $\omega_1$.


title: Data Mining for Optimizing IC Feature Designs to Enhance Overall Wafer Effectiveness

abstract: As global competition continues to strengthen in semiconductor industry, semiconductor companies have to continuously advance manufacturing technology and improve productivity to maintain competitive advantages. Die cost is significantly influenced by wafer productivity that is determined by yield rate and the number of gross dies per wafer. However, little research has been done on design for manufacturing and productivity enhancement through increasing the gross die number per wafer and decreasing the required shot number for exposure. This paper aims to propose a novel approach to improve overall wafer effectiveness via data mining to generate the optimal IC feature designs that can bridge the gap between integrated circuit (IC) design and wafer fabrication by providing chip designer with the optimal IC feature size in the design phase to increase gross dies and reduce the required shots. An empirical study was conducted in a leading semiconductor company for validation. The results have shown that the proposed approach can effectively enhance wafer productivity. Indeed, the developed solution has been implemented in the company to provide desired IC features to IC designers to enhance overall wafer effectiveness.


title: An empirical investigation of decision-making satisfaction in web-based decision support systems

abstract: Web-based information systems are increasingly being used for decision support applications. However, few empirical studies have been conducted on web-based decision support systems (DSS). This experimental research endeavors to understand factors that impact decision-making satisfaction in web-based decision support systems. Using structural equation modeling (SEM) approach, the analysis reveals that information quality and system quality influence decision-making satisfaction, while information presentation does not have an effect on decision-making satisfaction.


title: Four Emerging Issues in Americans with Disabilities Act Litigation Involving Hospitals and Other Health Care Providers

abstract: I. Introduction The Americans with Disabilities Act of 1990 ("ADA")1 was passed to combat discrimination against people with disabilities in a wide range of areas, including "employment, housing, public accommodations, education, transportation, communication, recreation, institutionalization, health services, voting, and access to public services."2 The ADA provides specific directions to employers, communications entities, and transportation providers, among others, on how to best accommodate citizens with disabilities.3 However, nothing in the ADA, and little in its implementing regulations, specifically directs health care providers how to best remove the barriers that impede access to health care and employment in the health care field for Americans with disabilities.4 ADA litigation involving hospitals and health care providers covers many of the same issues as litigation involving other entities covered by the ADA. However, many of these issues take on a different tone in the health care field. For example, courts take special care to evaluate whether a health care employee's proposed job accommodation will affect patient care. The question of whether an employee poses a "direct threat"5 to third parties is magnified greatly when the employee is directly responsible for patient care. Also, providing effective communication is especially important when conveying information to patients or family members with disabilities in an emergency room. This Article surveys four emerging issues that have become common in ADA litigation against health care providers. First, the Article discusses issues regarding a health care employer's duty to provide reasonable accommodations to employees with disabilities. Second, it explores problems hospitals face in dealing with employees with disabilities who may pose a direct threat to patients. Third, it discusses the duty to provide "effective communication"6 to patients or family members with disabilities, especially those who are deaf or hard of hearing. Fourth, it discusses whether hospitals must provide employment accommodations to independent contract providers. Finally, this Article concludes by discussing the need to balance the responsibility of ADA compliance with the responsibility of patient care. II. Reasonable Accommodation Issues Title I of the ADA makes employers responsible for providing "reasonable accommodations to the known physical or mental limitations of an otherwise qualified individual with a disability who is an applicant or employee."7 The Equal Opportunity Employment Commission ("EEOC"), the federal agency in charge of drafting regulations to implement Title I, defines "reasonable accommodation" as "any change in the work environment or in the way things are customarily done that enables an individual with a disability to enjoy equal employment opportunities."8 Examples of reasonable accommodations include: making existing facilities accessible, restructuring jobs, modifying work schedules, reassigning an employee with a disability to a vacant position, providing assistive technology devices, modifying examinations or policies, and providing qualified sign language interpreters or readers.9 However, an employer is not required to provide an accommodation that would create an "undue hardship" for it.10 This Section will discuss four of the most commonly litigated reasonable accommodation issues in the health care industry. First, it will focus on cases in which employees with disabilities experienced difficulty lifting patients and requested accommodations. Second, it will review cases in which employees requested leave or schedule changes as accommodations. Third, it will discuss the problems involved with reassigning an employee to a new job as an accommodation. Fourth, it will examine cases involving the restructuring of health care jobs, particularly those involving the reassignment of patient caseloads. A. …