title: Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

abstract: Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ("torques") from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for long-term predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques.


title: Closed-form Estimators for High-dimensional Generalized Linear Models

abstract: We propose a class of closed-form estimators for GLMs under high-dimensional sampling regimes. Our class of estimators is based on deriving closed-form variants of the vanilla unregularized MLE but which are (a) well-defined even under high-dimensional settings, and (b) available in closed-form. We then perform thresholding operations on this MLE variant to obtain our class of estimators. We derive a unified statistical analysis of our class of estimators, and show that it enjoys strong statistical guarantees in both parameter error as well as variable selection, that surprisingly match those of the more complex regularized GLM MLEs, even while our closed-form estimators are computationally much simpler. We derive instantiations of our class of closed-form estimators, as well as corollaries of our general theorem, for the special cases of logistic, exponential and Poisson regression models. We corroborate the surprising statistical and computational performance of our class of estimators via extensive simulations.


title: Hardware Reverse Engineering: Overview and Open Challenges

abstract: Hardware reverse engineering is a universal tool for both legitimate and illegitimate purposes. On the one hand, it supports confirmation of IP infringement and detection of circuit malicious manipulations, on the other hand it provides adversaries with crucial information to plagiarize designs, infringe on IP, or implant hardware Trojans into a target circuit. Although reverse engineering is commonplace in practice, the quantification of its complexity is an unsolved problem to date since both technical and human factors have to be accounted for. A sophisticated understanding of this complexity is crucial in order to provide a reasonable threat estimation and to develop sound countermeasures, i.e. obfuscation transformations of the target circuit, to mitigate risks for the modern IC landscape. The contribution of our work is threefold: first, we systematically study the current research branches related to hardware reverse engineering ranging from decapsulation to gate-level netlist analysis. Based on our overview, we formulate several open research questions to scientifically quantify reverse engineering, including technical and human factors. Second, we survey research on problem solving and on the acquisition of expertise and discuss its potential to quantify human factors in reverse engineering. Third, we propose novel directions for future interdisciplinary research encompassing both technical and psychological perspectives that hold the promise to holistically capture the complexity of hardware reverse engineering.


title: A Fast Approximate Nearest Neighbor Search Algorithm in the Hamming Space

abstract: A fast approximate nearest neighbor search algorithm for the (binary) Hamming space is proposed. The proposed Error Weighted Hashing (EWH) algorithm is up to 20 times faster than the popular locality sensitive hashing (LSH) algorithm and works well even for large nearest neighbor distances where LSH fails. EWH significantly reduces the number of candidate nearest neighbors by weighing them based on the difference between their hash vectors. EWH can be used for multimedia retrieval and copy detection systems that are based on binary fingerprinting. On a fingerprint database with more than 1,000 videos, for a specific detection accuracy, we demonstrate that EWH is more than 10 times faster than LSH. For the same retrieval time, we show that EWH has a significantly better detection accuracy with a 15 times lower error rate.


title: Intelligent IGBT drivers with exceptional driving and protection features

abstract: The family of intelligent IPS-drivers was invented to drive high power IGBT modules with blocking voltages up to 6,500V. They may be used use in industrial drives, power supplies, transportation, renewable energies as well as induction heating applications. The IPS- drivers decrease the switching losses and offer a reliable protection for high power IGBT modules. Varying the software enables an easy adaptation to specific applications. The main features of the IPS-drivers are: variable gate ON and OFF resistors, advanced desaturation and di/dt protections, active feedback clamping, high peak output current and output power, short signal transition times, and multiple soft shut down.


title: Mood Based Music Categorization System for Bollywood Music

abstract: Music shares a very special relation with human emotions. We often choose to listen to a song or music which best fits our mood at that instant. In spite of this strong correlation, most of the music applications today are devoid of providing the facility of mood-aware playlist generation. We wish to contribute to automatic identification of mood in audio songs by utilizing their spectral and temporal audio features. Our current work involves analysis of various features in order to learn, train and test the model representing the moods of the audio songs. Focus of our work is on the Indian popular music pieces and our work continues to analyse, develop and improve the algorithms to produce a system to recognize the mood category of the audio files automatically.


title: A mobile-health application to detect wandering patterns of elderly people in home environment

abstract: Wandering is a common and risky behavior in people with dementia (PWD). In this paper, we present a mobile healthcare application to detect wandering patterns in indoor settings. The application harnesses consumer electronics devices including WiFi access points and mobile phones and has been tested successfully in a home environment. Experimental results show that the mobile-health application is able to detect wandering patterns including lapping, pacing and random in real-time. Once wandering is detected, an alert message is sent using SMS (Short Message Service) to attending caregivers or physicians for further examination and timely interventions.


title: Design of tetra-band frequency reconfigurable antenna for portable wireless applications

abstract: This paper presents the design of multi and wide band frequency reconfigurable monopole antenna, using FR4 as a substrate with thickness of 1.6mm. The radiating element is printed on a truncated metallic ground plane. The antenna works in a single-band and two dual-band modes using two switches. The single-band (WiMAX at 3.45GHz) mode is obtained when both switches (SW1 and SW2) are in OFF state. The first Dual-band (Wi-Fi at 2.45 GHz and WLAN at 5.45 GHz) mode is achieved when one particular switch of the two switches is turned ON. When both switches are turned ON, the antenna functions in the second dual band mode(GSM at 1.8GHz and WLAN at 5.45GHz). This monopole antenna is designed using computer simulation technology 2011(CST Microwave studio). The designed antenna's performance is examined on the basis of return loss, radiation pattern, Gain,Efficiency and VSWR.


title: Carrier-Based Discontinuous PWM Method for Vienna Rectifiers

abstract: Vienna rectifiers, which are nongenerative-boost types of rectifiers, are used in grid-connected applications with a unity power factor such as telecommunication systems and wind turbine systems. These rectifiers are advantageous in that they have low total harmonic distortion and efficiency. There are many switching methods, which are different from those of three-level topologies, for Vienna rectifiers. In this paper, we propose a carrier-based discontinuous pulse width modulation (CB-DPWM) method. This method is simple compared to the discontinuous PWM (DPWM) method for Vienna rectifiers based on space vectors and guarantees normal rectifier operation for all modulation index ( ${\bf M}_{\bf a}$ ) values. We perform experiments to verify the suitability and performance of the proposed CB-DPWM method.


title: Scapular and clavicular kinematics during humeral elevation: A study with cadavers * ** *

abstract: Abstract A combination of kinematic testing and graphic reconstruction of cadaveric shoulders was used to characterize shoulder kinematics during a simulated passive clinical range-of-motion examination. Cadaveric shoulders were elevated in the coronal, scapular, and sagittal planes while the scapula, clavicle, and humerus were kinematically tracked. Graphic models of each shoulder were created from computed tomography data. The models were animated to display the experimental motions. Shoulder kinematics varied between elevation planes. The scapular and clavicular rotations were relatively small until the humerus reached approximately 90° of elevation. Clavicular and scapular rotations that occurred at low humeral elevation angles for elevation in the coronal plane were significantly larger than for the other two planes. The glenohumeral to scapulothoracic ratio was approximately equal to 2 for the entire range of elevation for each elevation plane, but it was dramatically larger during early elevation than during late elevation. (J Shoulder Elbow Surg 2001;10:278-85.)


title: Integrated antennas in eWLB packages for 77 GHz and 79 GHz automotive radar sensors

abstract: The feasibility of an antenna-in-package (AiP) concept is demonstrated for automotive radar sensors at the 77 GHz-band. Infineon's embedded wafer level ball grid array (eWLB) is employed as packaging technology. We developed a printed dipole antenna (PDA) and a printed loop antenna (PLA) in 8×8 mm2 eWLB packages that include 3×3 mm2 dummy Si chips. The fabricated eWLB packages are soldered onto evaluation boards that are designed and fabricated on an RF substrate. The measurements and the simulations are in a good agreement and predict a promising solution for the realization of cost efficient antenna-in-package concepts.


Cue-Based Dialogue Act Classification


title: Stacked patch circular polarized antenna for GPS/Galileo receiver applications

abstract: The compact multi-band circular polarized antenna is on high demand in Global Navigation Satellite Systems. This paper presents a novel design of a stacked patch circular polarized antenna for triple band GPS/Galileo application. The proposed antenna has achieved a minimum axial ratio of 0.48dB with a broad beamwidth of 175° necessary for multiband operation in GPS and Galileo systems.


Automated Road Lane Detection for Intelligent Vehicles


title: Improved AoA based localization of UHF RFID tags using spatial diversity

abstract: In a recent paper we presented new measurement results for an angle of arrival based approach to localize UHF RFID transponders in an indoor environment with off-the-shelf IDS R901G RFID reader ICs and UPM Raflatac DogBone RFID transponders. We were able to localize the transponders with a mean accuracy of 0.21 m using three antenna array positions for triangulation in a 3 m × 3 m room. In this paper we present an RSS based algorithm to select a special subset of antenna array positions for the estimation of the transponder position in a measurement setup with five different antenna array positions. With this algorithm a reduction of the mean measurement error by 50% was achieved for the considered test grid compared to the original approach in [1].


title: The Retrosplenial Cortex: Intrinsic Connectivity and Connections with the (Para)Hippocampal Region in the Rat. An Interactive Connectome

abstract: A connectome is an indispensable tool for brain researchers, since it quickly provides comprehensive knowledge of the brain’s anatomical connections. Such knowledge lies at the basis of understanding network functions. Our first comprehensive and interactive account of brain connections comprised the rat hippocampal-parahippocampal network. We have now added all anatomical connections with the retrosplenial cortex as well as the intrinsic connections of this region, because of the interesting functional overlap between these brain regions. The retrosplenial cortex is involved in a variety of cognitive tasks including memory, navigation and prospective thinking, yet the exact role of the retrosplenial cortex and the functional differences between its subdivisions remain elusive. The connectome presented here may help to define this role by providing an unprecedented interactive and searchable overview of all connections within and between the rat retrosplenial cortex, parahippocampal region and hippocampal formation.


title: A Grid-Interactive Permanent-Magnet Synchronous Motor-Driven Solar Water-Pumping System

abstract: This paper deals with an effective power transfer scheme between the solar photovoltaic (PV) array and single-phase grid, feeding a field-oriented-controlled (FOC) permanent-magnet synchronous motor (PMSM) drive applied to a water-pumping system (WPS). Owing to the intermittency associated with solar (PV) system, the requirement of constant water supply is not possible with the standalone system. In order to mitigate this, a grid-intergraded WPS is proposed here. The grid integration enables the consumer an uninterrupted operation of water pump irrespective of solar insolation level. Moreover, the PV power can be fed to the utility grid when water pumping is not required. To make it possible, one voltage-source converter (VSC) and one voltage-source inverter connected to a common dc link are used for utility grid and PMSM control, respectively. The unit vector template theory is utilized to generate switching pulses for VSC to control the bidirectional power flow between the solar PV system and utility grid through the common dc link. A sensorless FOC is used to drive the PMSM coupled to the water pump. An intermediate stage boost converter is used for extracting optimum power from solar PV array under variable insolation. A perturb and observe algorithm is used for generating the duty ratio for the maximum power point (MPP) operation. The applicability of overall system constituting utility grid in conjugation with PV array fed PMSM-coupled water pump ensuring bidirectional power flow control with MPP tracking of PV array and abiding the utility grid, IEEE-519 standard for power factor, and total harmonic distortion is simulated in MATLAB/Simulink environment with SimPowerSystem toolbox and validated on a prototype developed in the laboratory. The system prototype is tested under variable solar insolation and grid abnormalities such as voltage sag and voltage swell.


title: A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles

abstract: This paper presents an easy-to-use battery model applied to dynamic simulation software. The simulation model uses only the battery State-Of-Charge (SOC) as a state variable in order to avoid the algebraic loop problem. It is shown that this model, composed of a controlled voltage source in series with a resistance, can accurately represent four types of battery chemistries. The model's parameters can easily be extracted from the manufacturer's discharge curve, which allows for an easy use of the model. A method is described to extract the model's parameters and to approximate the internal resistance. The model is validated by superimposing the results with the manufacturer's discharge curves. Finally, the battery model is included in the SimPowerSystems (SPS) simulation software and is used in the Hybrid Electric Vehicle (HEV) demo. The results for the battery and for the DC-DC converter are analysed and they show that the model can accurately represent the general behaviour of the battery.


title: Hierarchical Text Categorization Using Neural Networks

abstract: This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to define smaller categorization problems based on a predefined hierarchical structure. The final classifier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with an optimized version of the traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks.


title: Combining motion planning and optimization for flexible robot manipulation

abstract: Robots that operate in natural human environments must be capable of handling uncertain dynamics and underspecified goals. Current solutions for robot motion planning are split between graph-search methods, such as RRT and PRM which offer solutions to high-dimensional problems, and Reinforcement Learning methods, which relieve the need to specify explicit goals and action dynamics. This paper addresses the gap between these methods by presenting a task-space probabilistic planner which solves general manipulation tasks posed as optimization criteria. Our approach is validated in simulation and on a 7-DOF robot arm that executes several tabletop manipulation tasks. First, this paper formalizes the problem of planning in underspecified domains. It then describes the algorithms necessary for applying this approach to planar manipulation tasks. Finally it validates the algorithms on a series of sample tasks that have distinct objectives, multiple objects with different shapes/dynamics, and even obstacles that interfere with object motion.


title: Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke

abstract: Spinal orthoses are common in the treatment of various conditions that affect the spine. They encompass both the spine and pelvis and thus have implications for pelvic and lower-limb motion during walking in addition to a direct effect on spinal motion. The role of the spine in walking is largely ill-defined, and the consequences of restricted spinal motion on walking have yet to be explored. This study investigated the effect of spinal restriction on gait in able-bodied persons. Gait analyses were performed on 10 able-bodied subjects as they walked at five different speeds that were distributed across their comfortable range of speeds. Data were collected during walking with and without spinal restriction by a fiberglass body jacket, which is similar to a thoracolumbosacral orthosis (TLSO). With spinal restriction, peak-to-peak (PP) pelvic obliquity and rotation were significantly reduced across all walking speeds (p < 0.001), while PP pelvic tilt was significantly reduced at only the fastest walking speeds (p = 0.017). PP hip abduction-adduction motion was significantly reduced with spinal restriction across all speeds (p < 0.001), while PP hip flexion-extension significantly increased at only the slow and very slow speeds (p < 0.001 and p = 0.023, respectively). A better understanding of the effects of restricted spinal motion on gait may help clinicians predict and avoid development of additional problems from TLSO use or surgical restriction of spinal motion. An awareness of these issues will enable clinicians to monitor patients for problems that may result from decreased spine and pelvic motion.


title: Using ProVerif to Analyze Protocols with Diffie-Hellman Exponentiation

abstract: ProVerif is one of the most successful tools for cryptographic protocol analysis. However, dealing with algebraic properties of operators such as the exclusive OR (XOR) and Diffie-Hellman exponentiation has been problematic. Recently, we have developed an approach which enables ProVerif, and related tools, to analyze a large class of protocols that employ the XOR operator. In this work, we adapt this approach to the case of Diffie-Hellman exponentiation. The core of our approach is to reduce the derivation problem for Horn theories modulo algebraic properties of Diffie-Hellman exponentiation to a purely syntactical derivation problem for Horn theories. The latter problem can then be solved by tools such as ProVerif. Our reduction works for a large class of Horn theories, allowing to model a wide range of intruder capabilities and protocols. We implemented our reduction and, in combination with ProVerif, applied it in the automatic analysis of several state-of-the-art protocols that use Diffie-Hellman exponentiation. While the general idea of our approach follows the one for XOR in our previous work, the reduction itself and the proof of soundness and completeness of our reduction are entirely different from the XOR case. Surprisingly, the reduction for Diffie-Hellman exponentiation is more efficient than the one for XOR.


title: Extracting candidate terms from medical texts

abstract: In this paper, we present a new method for the construction of domain ontology from texts in the medical field. In our method, NLP tools are not used since our goal is to reduce the amount of noise and the number of filters applied. This allows us to manage the control of semantic quality of Candidate Terms (CT). The proposed method is based on the technique of semantic annotation for controlled terminology extraction via semantic resources, which we supplement with a Coloring Strategy Identification (CSI) method allowing the identification of medical terms and an extraction of CT according to their appearance in the text. In CSI, we exploit the resulting metadata for semantic annotation via some rules. Moreover, we apply a method of categorization by prototype model to overcome perceived silence. The result of this extraction is structured in XML representing the hierarchy of concepts that composes our medical ontology.


title: Face Expression Recognition and Analysis: The State of the Art

abstract: The automatic recognition of facial expressions has been an active research topic since the early nineties. There have been several advances in the past few years in terms of face detection and tracking, feature extraction mechanisms and the techniques used for expression classification. This paper surveys some of the published work since 2001 till date. The paper presents a time-line view of the advances made in this field, the applications of automatic face expression recognizers, the characteristics of an ideal system, the databases that have been used and the advances made in terms of their standardization and a detailed summary of the state of the art. The paper also discusses facial parameterization using FACS Action Units (AUs) and MPEG-4 Facial Animation Parameters (FAPs) and the recent advances in face detection, tracking and feature extraction methods. Notes have also been presented on emotions, expressions and facial features, discussion on the six prototypic expressions and the recent studies on expression classifiers. The paper ends with a note on the challenges and the future work. This paper has been written in a tutorial style with the intention of helping students and researchers who are new to this field.


title: Crust: A Bounded Verifier for Rust (N)

abstract: Rust is a modern systems language that provides guaranteed memory safety through static analysis. However, Rust includes an escape hatch in the form of "unsafe code," which the compiler assumes to be memory safe and to preserve crucial pointer aliasing invariants. Unsafe code appears in many data structure implementations and other essential libraries, and bugs in this code can lead to memory safety violations in parts of the program that the compiler otherwise proved safe. We present CRUST, a tool combining exhaustive test generation and bounded model checking to detect memory safety errors, as well as violations of Rust's pointer aliasing invariants within unsafe library code. CRUST requires no programmer annotations, only an indication of the modules to check. We evaluate CRUST on data structures from the Rust standard library. It detects memory safety bugs that arose during the library's development and remained undetected for several months.


title: Security Automation and Threat Information-Sharing Options

abstract: Security information sharing isn't a new concept. Various government organizations and security vendors have been creating security collateral that's consumed by their products and services. Given the expansion of security in the IT world, security collateral needs to be interoperable and sharable between organizations to be used efficiently. To achieve this, there have been multiple attempts to standardize security information sharing over the past decade. This article presents the basic features of these schemes, identifies the problems they're trying to solve, and summarizes areas where they overlap and differ. The author addresses the most common questions and concerns raised in the industry when information is shared among vendors and helps organizations develop a common understanding of the options available and adopt those that serve their purpose.


title: Internet-Based Mindfulness Treatment for Anxiety Disorders: A Randomized Controlled Trial

abstract: Mindfulness-based interventions have proven effective for the trans diagnostic treatment of heterogeneous anxiety disorders. So far, no study has investigated the potential of mindfulness-based tre ...


title: Automatic detection of users’ skill levels using high-frequency user interface events

abstract: Computer users have different levels of system skills. Moreover, each user has different levels of skill across different applications and even in different portions of the same application. Additionally, users' skill levels change dynamically as users gain more experience in a user interface. In order to adapt user interfaces to the different needs of user groups with different levels of skills, automatic methods of skill detection are required. In this paper, we present our experiments and methods, which are used to build automatic skill classifiers for desktop applications. Machine learning algorithms were used to build statistical predictive models of skill. Attribute values were extracted from high frequency user interface events, such as mouse motions and menu interactions, and were used as inputs to our models. We have built both task-independent and task-dependent classifiers with promising results.


title: Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation

abstract: Unsupervised transfer of object recognition models from synthetic to real data is an important problem with many potential applications. The challenge is how to"adapt"a model trained on simulated images so that it performs well on real-world data without any additional supervision. Unfortunately, current benchmarks for this problem are limited in size and task diversity. In this paper, we present a new large-scale benchmark called Syn2Real, which consists of a synthetic domain rendered from 3D object models and two real-image domains containing the same object categories. We define three related tasks on this benchmark: closed-set object classification, open-set object classification, and object detection. Our evaluation of multiple state-of-the-art methods reveals a large gap in adaptation performance between the easier closed-set classification task and the more difficult open-set and detection tasks. We conclude that developing adaptation methods that work well across all three tasks presents a significant future challenge for syn2real domain transfer.


title: Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm

abstract: In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection - CADe) and diagnosis (Computer-Aided Diagnosis - CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean?standard deviation value of AOM for the proposed method was 79.2?8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views.


title: Implementation of human-like driving skills by autonomous fuzzy behavior control on an FPGA-based car-like mobile robot

abstract: In this paper, the concepts of car maneuvers, fuzzy logic control (FLC), and sensor-based behaviors are merged to implement the human-like driving skills by an autonomous car-like mobile robot (CLMR). Four kinds of FLCs, fuzzy wall-following control, fuzzy corner control, fuzzy garage-parking control, and fuzzy parallel-parking control, are synthesized to accomplish the autonomous fuzzy behavior control (AFBC). Computer simulation results illustrate the effectiveness of the proposed control schemes. The setup of the CLMR is provided, where the implementation of the AFBC on a field-programmable gate array chip is also addressed. Finally, the real-time implementation experiments of the CLMR in the test ground demonstrate the feasibility in practical car maneuvers.


title: Composition of Word Representations Improves Semantic Role Labelling

abstract: State-of-the-art semantic role labelling systems require large annotated corpora to achieve full performance. Unfortunately, such corpora are expensive to produce and often do not generalize well across domains. Even in domain, errors are often made where syntactic information does not provide sufficient cues. In this paper, we mitigate both of these problems by employing distributional word representations gathered from unlabelled data. While straight-forward word representations of predicates and arguments improve performance, we show that further gains are achieved by composing representations that model the interaction between predicate and argument, and capture full argument spans.


title: Reference-Dependent Consumption Plans

abstract: We develop a rational dynamic model in which people are loss averse over changes in beliefs about present and future consumption. Because changes in wealth are news about future consumption, preferences over money are reference-dependent. If news resonates more when about imminent consumption than when about future consumption, a decision maker might (to generate pleasant surprises) overconsume early relative to the optimal committed plan, increase immediate consumption following surprise wealth increases, and delay decreasing consumption following surprise losses. Since higher wealth mitigates the effect of bad news, people exhibit an unambiguous first-order precautionary-savings motive. (JEL D14, D81, D83, D91)


title: Securing web application code by static analysis and runtime protection

abstract: Security remains a major roadblock to universal acceptance of the Web for many kinds of transactions, especially since the recent sharp increase in remotely exploitable vulnerabilities have been attributed to Web application bugs. Many verification tools are discovering previously unknown vulnerabilities in legacy C programs, raising hopes that the same success can be achieved with Web applications. In this paper, we describe a sound and holistic approach to ensuring Web application security. Viewing Web application vulnerabilities as a secure information flow problem, we created a lattice-based static analysis algorithm derived from type systems and typestate, and addressed its soundness. During the analysis, sections of code considered vulnerable are instrumented with runtime guards, thus securing Web applications in the absence of user intervention. With sufficient annotations, runtime overhead can be reduced to zero. We also created a tool named.WebSSARI (Web application Security by Static Analysis and Runtime Inspection) to test our algorithm, and used it to verify 230 open-source Web application projects on SourceForge.net, which were selected to represent projects of different maturity, popularity, and scale. 69 contained vulnerabilities. After notifying the developers, 38 acknowledged our findings and stated their plans to provide patches. Our statistics also show that static analysis reduced potential runtime overhead by 98.4%.


title: Balanced Crystalloids versus Saline in Noncritically Ill Adults

abstract: BACKGROUND ::: Comparative clinical effects of balanced crystalloids and saline are uncertain, particularly in noncritically ill patients cared for outside an intensive care unit (ICU). ::: ::: ::: METHODS ::: We conducted a single-center, pragmatic, multiple-crossover trial comparing balanced crystalloids (lactated Ringer's solution or Plasma-Lyte A) with saline among adults who were treated with intravenous crystalloids in the emergency department and were subsequently hospitalized outside an ICU. The type of crystalloid that was administered in the emergency department was assigned to each patient on the basis of calendar month, with the entire emergency department crossing over between balanced crystalloids and saline monthly during the 16-month trial. The primary outcome was hospital-free days (days alive after discharge before day 28). Secondary outcomes included major adverse kidney events within 30 days - a composite of death from any cause, new renal-replacement therapy, or persistent renal dysfunction (defined as an elevation of the creatinine level to ≥200% of baseline) - all censored at hospital discharge or 30 days, whichever occurred first. ::: ::: ::: RESULTS ::: A total of 13,347 patients were enrolled, with a median crystalloid volume administered in the emergency department of 1079 ml and 88.3% of the patients exclusively receiving the assigned crystalloid. The number of hospital-free days did not differ between the balanced-crystalloids and saline groups (median, 25 days in each group; adjusted odds ratio with balanced crystalloids, 0.98; 95% confidence interval [CI], 0.92 to 1.04; P=0.41). Balanced crystalloids resulted in a lower incidence of major adverse kidney events within 30 days than saline (4.7% vs. 5.6%; adjusted odds ratio, 0.82; 95% CI, 0.70 to 0.95; P=0.01). ::: ::: ::: CONCLUSIONS ::: Among noncritically ill adults treated with intravenous fluids in the emergency department, there was no difference in hospital-free days between treatment with balanced crystalloids and treatment with saline. (Funded by the Vanderbilt Institute for Clinical and Translational Research and others; SALT-ED ClinicalTrials.gov number, NCT02614040 .).


title: Stereotype Threat and Executive Resource Depletion: Examining the Influence of Emotion Regulation.

abstract: Research shows that stereotype threat reduces performance by diminishing executive resources, but less is known about the psychological processes responsible for these impairments. The authors tested the idea that targets of stereotype threat try to regulate their emotions and that this regulation depletes executive resources, resulting in underperformance. Across 4 experiments, they provide converging evidence that targets of stereotype threat spontaneously attempt to control their expression of anxiety and that such emotion regulation depletes executive resources needed to perform well on tests of cognitive ability. They also demonstrate that providing threatened individuals with a means to effectively cope with negative emotions—by reappraising the situation or the meaning of their anxiety—can restore executive resources and improve test performance. They discuss these results within the framework of an integrated process model of stereotype threat, in which affective and cognitive processes interact to undermine performance.


title: Multicast Outage Probability and Transmission Capacity of Multihop Wireless Networks

abstract: Multicast transmission, wherein the same packet must be delivered to multiple receivers, is an important aspect of sensor and tactical networks and has several distinctive traits as opposed to more commonly studied unicast networks. Specially, these include 1) identical packets must be delivered successfully to several nodes, 2) outage at any receiver requires the packet to be retransmitted at least to that receiver, and 3) the multicast rate is dominated by the receiver with the weakest link in order to minimize outage and retransmission. A first contribution of this paper is the development of a tractable multicast model and throughput metric that captures each of these key traits in a multicast wireless network. We utilize a Poisson cluster process (PCP) consisting of a distinct Poisson point process (PPP) for the transmitters and receivers, and then define the multicast transmission capacity (MTC) as the maximum achievable multicast rate per transmission attempt times the maximum intensity of multicast clusters under decoding delay and multicast outage constraints. A multicast cluster is a contiguous area over which a packet is multicasted, and to reduce outage it can be tessellated into v smaller regions of multicast. The second contribution of the paper is the analysis of several key aspects of this model, for which we develop the following main result. Assuming τ/v transmission attempts are allowed for each tessellated region in a multicast cluster, we show that the MTC is Θ(ρkxlog(k)vy) where ρ, x and y are functions of τ and v depending on the network size and intensity, and k is the average number of the intended receivers in a cluster. We derive {ρ, x, y} for a number of regimes of interest, and also show that an appropriate number of retransmissions can significantly enhance the MTC.


title: A Theory of Personal Budgeting

abstract: Prominent research argues that consumers often use personal budgets to manage self-control problems. This paper analyzes the link between budgeting and self-control problems in consumption-saving decisions. It shows that the use of good-specific budgets depends on the combination of a demand for commitment and the demand for flexibility resulting from uncertainty about intratemporal trade-offs between goods. It explains the subtle mechanism which renders budgets useful commitments, their interaction with minimum-savings rules (another widely-studied form of commitment), and how budgeting depends on the intensity of self-control problems. This theory matches several empirical findings on personal budgeting.


title: EMG-based control for lower-limb power-assist exoskeletons

abstract: Power-Assist for lower-limb is expected by many physically weak persons to assist their daily activities. It is important for the power-assist robots that the assist motion is generated based on users motion intention. This paper presents a muscle-model-oriented EMG-based (electromyogram-based) control method to activate a lower-limb power-assist robot according to the users motion intention since the EMG directly reflects the users muscle activities. In the proposed method, a matrix which expresses the relationship between the muscle activities and the generating joint torque is applied to estimate the users motion intention in real-time. The effectiveness of the proposed control method was evaluated by performing experiments.


title: Longitudinal Effects of Domestic Violence on Employment and Welfare Outcomes

abstract: This study uses longitudinal data spanning 13 years from a study of 234 adolescent mothers to evaluate the effects of cumulative domestic violence on employment and welfare use before and after welfare reform. Domestic violence increased the odds of unemployment after welfare reform, but not before; domestic violence had no effect on welfare use during any time period. Psychological distress after welfare reform was associated with unemployment, but not with welfare outcomes. Thus, the authors found that the direct effect of domestic violence on unemployment is not mediated by concurrent level of psychological distress. The relationship of psychological distress to unemployment exists only for those with a history of domestic violence. Cumulative domestic violence can have negative effects on economic capacity many years after the violence occurs, suggesting that policymakers recognize the long-term nature of the impact of domestic violence on women's capacity to be economically self-reliant.


title: A Method for Parallelizing Algorithms by Vector Scheduling Functions

abstract: A technique for parallelizing multidimensional algorithms with uniform dependencies designed for the implementation on parallel architectures of smaller dimensionalities is suggested. The technique is based on multidimensional separate scheduling of the basic operations of the algorithm. A procedure for the construction of a scheduling (parallelizing) function on the basis of theoretical results is suggested.


title: AUSUM: approach for unsupervised bug report summarization

abstract: In most software projects, resolved bugs are archived for future reference. These bug reports contain valuable information on the reported problem, investigation and resolution. When bug triaging, developers look for how similar problems were resolved in the past. Search over bug repository gives the developer a set of recommended bugs to look into. However, the developer still needs to manually peruse the contents of the recommended bugs which might vary in size from a couple of lines to thousands. Automatic summarization of bug reports is one way to reduce the amount of data a developer might need to go through. Prior work has presented learning based approaches for bug summarization. These approaches have the disadvantage of requiring large training set and being biased towards the data on which the model was learnt. In fact, maximum efficacy was reported when the model was trained and tested on bug reports from the same project. In this paper, we present the results of applying four unsupervised summarization techniques for bug summarization. Industrial bug reports typically contain a large amount of noise---email dump, chat transcripts, core-dump---useless sentences from the perspective of summarization. These derail the unsupervised approaches, which are optimized to work on more well-formed documents. We present an approach for noise reduction, which helps to improve the precision of summarization over the base technique (4% to 24% across subjects and base techniques). Importantly, by applying noise reduction, two of the unsupervised techniques became scalable for large sized bug reports.


title: Latent Semantic Analysis: five methodological recommendations

abstract: AbstractThe recent influx in generation, storage, and availability of textual data presents researchers with the challenge of developing suitable methods for their analysis. Latent Semantic Analysis (LSA), a member of a family of methodological approaches that offers an opportunity to address this gap by describing the semantic content in textual data as a set of vectors, was pioneered by researchers in psychology, information retrieval, and bibliometrics. LSA involves a matrix operation called singular value decomposition, an extension of principal component analysis. LSA generates latent semantic dimensions that are either interpreted, if the researcher's primary interest lies with the understanding of the thematic structure in the textual data, or used for purposes of clustering, categorization, and predictive modeling, if the interest lies with the conversion of raw text into numerical data, as a precursor to subsequent analysis. This paper reviews five methodological issues that need to be addressed ...


title: Assessing Grasp Stability Based on Learning and Haptic Data

abstract: An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machine-learning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov models (HMMs). In particular, we study the effect of different sensory streams to grasp stability. This includes object information such as shape; grasp information such as approach vector; tactile measurements from fingertips; and joint configuration of the hand. Sensory knowledge affects the success of the grasping process both in the planning stage (before a grasp is executed) and during the execution of the grasp (closed-loop online control). In this paper, we study both of these aspects. We propose a probabilistic learning framework to assess grasp stability and demonstrate that knowledge about grasp stability can be inferred using information from tactile sensors. Experiments on both simulated and real data are shown. The results indicate that the idea to exploit the learning approach is applicable in realistic scenarios, which opens a number of interesting venues for the future research.


title: Towards a fully automated 3D printability checker

abstract: 3D printing has become one of the most popular evolutionary techniques with diverse application, even as normal people's hobby. As printing target, enormous 3D virtual models from game industry and virtual reality flood the internet, shared in various online forums, such as thingiverse. But which of them can really be printed? In this paper we propose the 3D Printability Checker, which can be used to automatically answer this non-trivial question. One of the major novelties of this paper is the process of dependable software engineering we use to build this Printability Checker. Firstly, we prove that this question is decidable with a given 3D object and a list of printer profiles. Secondly, we design and implement such a checker. Finally, we show our experimental results and use them further for a machine learning approach to improve our system in an automatic way. The generic framework provides a useful basis of automatic self-improvement of the software by combining current techniques in the area of formal method, geometry modelling and machine learning.


title: Unsupervised Video Surveillance for Anomaly Detection of Street Traffic

abstract: Intelligent transportation systems enables the analysis of large multidimensional street traffic data to detect pattern and anomaly, which otherwise is a difficult task. Advancement in computer vision makes great contribution in the progress of video based traffic surveillance system. But still there are some challenges which need to be solved like objects occlusion, behavior of objects. This paper developed a novel framework which explores multidimensional data of road traffic to analyze different patterns of traffic and anomaly detection. This framework is implemented on road traffic dataset collected from different areas of the city.


title: Design and development of 6-DOF robotic arm controlled by Man Machine Interface

abstract: This paper presents the design and development of a low cost and user friendly interface for the control of a 6-DOF slave tele-operated anthropomorphic robotic arm. Articulation of the robotic arm is achieved about six single-axis revolute joints: one for each shoulder abduction-adduction (abd-add), shoulder flexion-extension (flx-ext), elbow flx-ext, wrist flx-ext, wrist radial-ulnar (rad-uln), and gripper open-close. Tele-operator, master, uses the Man Machine Interface (MMI) to operate in real-time the robotic arm. The MMI has simple motion capture devices that translate motion into analog voltages which bring about the corresponding actuating signals in the robotic arm.


title: Revising The Wordnet Domains Hierarchy: Semantics, Coverage And Balancing

abstract: The continuous expansion of the multilingual information society has led in recent years to a pressing demand for multilingual linguistic resources suitable to be used for different applications. ::: ::: In this paper we present the WordNet Domains Hierarchy (WDH), a language-independent resource composed of 164, hierarchically organized, domain labels (e.g. Architecture, Sport, Medicine). Although WDH has been successfully applied to various Natural Language Processing tasks, the first available version presented some problems, mostly related to the lack of a clear semantics of the domain labels. Other correlated issues were the coverage and the balancing of the domains. We illustrate a new version of WDH addressing these problems by an explicit and systematic reference to the Dewey Decimal Classification. The new version of WDH has a better defined semantics and is applicable to a wider range of tasks.


Integrating Fuzzy Logic With Data Mining Methods For Intrusion Detection


title: Edina: Building an Open Domain Socialbot with Self-dialogues

abstract: We present Edina, the University of Edinburgh's social bot for the Amazon Alexa Prize competition. Edina is a conversational agent whose responses utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative new technique we call self-dialogues. These are conversations in which a single AMT Worker plays both participants in a dialogue. Such dialogues are surprisingly natural, efficient to collect and reflective of relevant and/or trending topics. These self-dialogues provide training data for a generative neural network as well as a basis for soft rules used by a matching score component. Each match of a soft rule against a user utterance is associated with a confidence score which we show is strongly indicative of reply quality, allowing this component to self-censor and be effectively integrated with other components. Edina's full architecture features a rule-based system backing off to a matching score, backing off to a generative neural network. Our hybrid data-driven methodology thus addresses both coverage limitations of a strictly rule-based approach and the lack of guarantees of a strictly machine-learning approach.


title: Formal language, grammar and set-constraint-based program analysis by abstract interpretation

abstract: Formal Language, Grammar and Set-Constraint-Based Program Analysis by Abstract Interpretation


title: Computational principles of movement neuroscience

abstract: Unifying principles of movement have emerged from the computational study of motor control. We review several of these principles and show how they apply to processes such as motor planning, control, estimation, prediction and learning. Our goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.


title: New approach for liveness detection in fingerprint scanners based on valley noise analysis

abstract: Recent research has shown that it is possible to spoof a ::: variety of fingerprint scanners using some simple techniques with ::: molds made from plastic, clay, Play-Doh, silicon, or gelatin materials. ::: To protect against spoofing, methods of liveness detection measure ::: physiological signs of life from fingerprints, ensuring that only ::: live fingers are captured for enrollment or authentication. We propose ::: a new liveness detection method based on noise analysis ::: along the valleys in the ridge-valley structure of fingerprint images. ::: Unlike live fingers, which have a clear ridge-valley structure, artificial ::: fingers have a distinct noise distribution due to the material’s properties ::: when placed on a fingerprint scanner. Statistical features are ::: extracted in multiresolution scales using the wavelet decomposition ::: technique. Based on these features, liveness separation (live/ ::: nonlive) is performed using classification trees and neural networks. ::: We test this method on the data set, that contains about 58 live, 80 ::: spoof (50 made from Play-Doh and 30 made from gelatin), and 25 ::: cadaver subjects for 3 different scanners. We also test this method ::: on a second data set that contains 28 live and 28 spoof (made from ::: silicon) subjects. Results show that we can get approximately 90.9– ::: 100% classification of spoof and live fingerprints. The proposed liveness ::: detection method is purely software-based, and application of ::: this method can provide antispoofing protection for fingerprint ::: scanners.


title: The Cog Project : Building a Humanoid Robot

abstract: To explore issues of developmental structure, physical embodiment, integration of multiple sensory and motor systems, and social interaction, we have constructed an upper-torso humanoid robot called Cog. The robot has twenty-one degrees of freedom and a variety of sensory systems, including visual, auditory, vestibular, kinesthetic, and tactile senses. This chapter gives a background on the methodology that we have used in our investigations, highlights the research issues that have been raised during this project, and provides a summary of both the current state of the project and our long-term goals. We report on a variety of implemented visual-motor routines (smooth-pursuit tracking, saccades, binocular vergence, and vestibular-ocular and opto-kinetic reflexes), orientation behaviors, motor control techniques, and social behaviors (pointing to a visual target, recognizing joint attention through face and eye finding, imitation of head nods, and regulating interaction through expressive feedback). We further outline a number of areas for future research that will be necessary to build a complete embodied system.


title: Effective periodic pattern mining in time series databases

abstract: The goal of analyzing a time series database is to find whether and how frequent a periodic pattern is repeated within the series. Periodic pattern mining is the problem that regards temporal regularity. However, most of the existing algorithms have a major limitation in mining interesting patterns of users interest, that is, they can mine patterns of specific length with all the events sequentially one after another in exact positions within this pattern. Though there are certain scenarios where a pattern can be flexible, that is, it may be interesting and can be mined by neglecting any number of unimportant events in between important events with variable length of the pattern. Moreover, existing algorithms can detect only specific type of periodicity in various time series databases and require the interaction from user to determine periodicity. In this paper, we have proposed an algorithm for the periodic pattern mining in time series databases which does not rely on the user for the period value or period type of the pattern and can detect all types of periodic patterns at the same time, indeed these flexibilities are missing in existing algorithms. The proposed algorithm facilitates the user to generate different kinds of patterns by skipping intermediate events in a time series database and find out the periodicity of the patterns within the database. It is an improvement over the generating pattern using suffix tree, because suffix tree based algorithms have weakness in this particular area of pattern generation. Comparing with the existing algorithms, the proposed algorithm improves generating different kinds of interesting patterns and detects whether the generated pattern is periodic or not. We have tested the performance of our algorithm on both synthetic and real life data from different domains and found a large number of interesting event sequences which were missing in existing algorithms and the proposed algorithm was efficient enough in generating and detecting periodicity of flexible patterns on both types of data.


title: A Probabilistic Analysis of the Efficiency of Automated Software Testing

abstract: We study the relative efficiencies of the random and systematic approaches to automated software testing. Using a simple but realistic set of assumptions, we propose a general model for software testing and define sampling strategies for random ( $\mathcal {R}$ ) and systematic ( $\mathcal {S}_0$ ) testing, where each sampling is associated with a sampling cost: 1 and ${\sf c}$ units of time, respectively. The two most important goals of software testing are: (i) achieving in minimal time a given degree of confidence $x$ in a program's correctness and (ii) discovering a maximal number of errors within a given time bound $\hat{n}$ . For both (i) and (ii), we show that there exists a bound on ${\sf c}$ beyond which $\mathcal {R}$ performs better than $\mathcal {S}_0$ on the average. Moreover for (i), this bound depends asymptotically only on $x$ . We also show that the efficiency of $\mathcal {R}$ can be fitted to the exponential curve. Using these results we design a hybrid strategy $\mathcal {H}$ that starts with $\mathcal {R}$ and switches to $\mathcal {S}_0$ when $\mathcal {S}_0$ is expected to discover more errors per unit time. In our experiments we find that $\mathcal {H}$ performs similarly or better than the most efficient of both and that $\mathcal {S}_0$ may need to be significantly faster than our bounds suggest to retain efficiency over $\mathcal {R}$ .


title: Research Note---Investigating the Influence of the Functional Mechanisms of Online Product Presentations

abstract: Internet-based interactive multimedia technologies enable online firms to employ a variety of formats to present and promote their products: They can use pictures, videos, and sounds to depict products, as well as give consumers the opportunity to try out products virtually. Despite the several previous endeavors that studied the effects of different product presentation formats, the functional mechanisms underlying these presentation methods have not been investigated in a comprehensive way. This paper investigates a model showing how these functional mechanisms (namely, vividness and interactivity) influence consumers' intentions to return to a website and their intentions to purchase products. A study conducted to test this model has largely confirmed our expectations: (1) both vividness and interactivity of product presentations are the primary design features that influence the efficacy of the presentations; (2) consumers' perceptions of the diagnosticity of websites, their perceptions of the compatibility between online shopping and physical shopping, and their shopping enjoyment derived from a particular online shopping experience jointly influence consumers' attitudes toward shopping at a website; and (3) both consumers' attitudes toward products and their attitudes toward shopping at a website contribute to their intentions to purchase the products displayed on the website.


title: Multi-Target Shrinkage Estimation for Covariance Matrices

abstract: Covariance matrix estimation is problematic when the number of samples is relatively small compared with the number of variables. One way to tackle this problem is through the use of shrinkage estimators that offer a compromise between the sample covariance matrix and a well-conditioned matrix (also known as the ”target”) with the aim of minimizing the mean-squared error (MSE). The use of only one target limits the shrinkage estimators' flexibility when minimizing the MSE. In this paper, we propose a multi-target shrinkage estimator (MTSE) for covariance matrices that exploits the Lediot-Wolf (LW) method by utilizing several targets simultaneously. This greatly increases the estimator's flexibility and enables it to attain a lower MSE. We also offer a general target that serves as a framework for designing a wide variety of targets. In consequence, instead of studying individual targets, the general framework can be utilized. We then show that the framework encompasses several targets that already exist in the literature. Numerical simulations demonstrate that the MTSE significantly reduces the MSE and is highly effective in classification tasks.


Smart stick for Blind: Obstacle Detection, Artificial vision and Real-time assistance via GPS


title: Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab

abstract: This paper presents a Matlab-based tutorial for model-based prognostics, which combines a physical model with observed data to identify model parameters, from which the remaining useful life (RUL) can be predicted. Among many model-based prognostics algorithms, the particle filter is used in this tutorial for parameter estimation of damage or a degradation model. The tutorial is presented using a Matlab script with 62 lines, including detailed explanations. As examples, a battery degradation model and a crack growth model are used to explain the updating process of model parameters, damage progression, and RUL prediction. In order to illustrate the results, the RUL at an arbitrary cycle are predicted in the form of distribution along with the median and 90% prediction interval. This tutorial will be helpful for the beginners in prognostics to understand and use the prognostics method, and we hope it provides a standard of particle filter based prognostics.


title: A Joint Location-Inventory Model

abstract: We consider a joint location-inventory problem involving a single supplier and multiple retailers. Associated with each retailer is some variable demand. Due to this variability, some amount of safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers (and therefore inventory storage locations) for other retailers. The problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. We formulate this problem as a nonlinear integer-programming model. We then restructure this model into a set-covering integer-programming model. The pricing problem that must be solved as part of the column generation algorithm for the set-covering model involves a nonlinear term in the retailerdistribution-center allocation terms. We show that this pricing problem can (theoretically) be solved efficiently, in general, and we show how to solve it practically in two important cases. We present computational results on several instances of sizes ranging from 33 to 150 retailers. In all cases, the lower bound from the linear-programming relaxation to the set-covering model gives the optimal solution.


title: Multi-user lax communications: A multi-armed bandit approach

abstract: Inspired by cognitive radio networks, we consider a setting where multiple users share several channels modeled as a multi-user multi-armed bandit (MAB) problem. The characteristics of each channel are unknown and are different for each user. Each user can choose between the channels, but her success depends on the particular channel chosen as well as on the selections of other users: if two users select the same channel their messages collide and none of them manages to send any data. Our setting is fully distributed, so there is no central control. As in many communication systems, the users cannot set up a direct communication protocol, so information exchange must be limited to a minimum. We develop an algorithm for learning a stable configuration for the multi-user MAB problem. We further offer both convergence guarantees and experiments inspired by real communication networks, including comparison to state-of-the-art algorithms.


title: Bee colony algorithm for real-time optimal path planning of mobile robots

abstract: This paper presents a novel method to solve the problem of path planning for mobile robots based on bee colony algorithm. The method is inspired by collective behavior of honeybees to find food sources around the hive. The proposed method includes two steps. The first step is to use a simple rule to establish an initial collision-free path from the starting point to the target and the second step is utilizing bee colony algorithm to optimize the initial path. The results of computer simulations show that the proposed method is effective and can be used in the real-time path planning of mobile robots.


title: System-level performance of downlink NOMA combined with SU-MIMO for future LTE enhancements

abstract: This paper investigates the system-level performance of downlink non-orthogonal multiple access (NOMA) combined with single user MIMO (SU-MIMO) for future LTE (Long-Term Evolution) enhancements. The goal is to clarify the performance gains of NOMA combined with SU-MIMO transmission, taking into account the LTE radio interface such as frequency-domain scheduling, adaptive modulation and coding (AMC), and NOMA specific functionalities such as multi-user pairing/ordering and transmit power allocation. In particular, we propose practical schemes to efficiently combine NOMA with open-loop SU-MIMO (Transmission Mode 3: TM3) and closed-loop SU-MIMO (Transmission Mode 4: TM4) specified in LTE. Based on computer simulations, we compare NOMA performance gains for different granularities of scheduling and MCS (modulation and coding scheme) selection, for both genie-aided channel quality information (CQI) estimation and approximated CQI estimation, and using different number of power sets. Evaluation results show that NOMA can still provide a hefty portion of its expected gains even with approximated CQI estimation and limited number of power sets, and also when LTE compliant subband scheduling and wideband MCS are applied.


title: Clustering with qualitative information

abstract: We consider the problem of clustering a collection of elements based on pairwise judgments of similarity and dissimilarity. N. Bansal et al. (2002) cast the problem thus: given a graph G whose edges are labeled "+" (similar) or "-" (dissimilar), partition the vertices into clusters so that the number of pairs correctly (resp. incorrectly) classified with respect to the input labeling is maximized (resp. minimized). Complete graphs, where the classifier labels every edge, and general graphs, where some edges are not labeled, are both worth studying. We answer several questions left open by N. Bansal et al. (2002) and provide a sound overview of clustering with qualitative information. We give a factor 4 approximation for minimization on complete graphs, and a factor O(log n) approximation for general graphs. For the maximization version, a PTAS for complete graphs is shown by N. Bansal et al. (2002); we give a factor 0.7664 approximation for general graphs, noting that a PTAS is unlikely by proving APX-hardness. We also prove the APX-hardness of minimization on complete graphs.


title: PCG biometric identification system based on feature level fusion using canonical correlation analysis

abstract: In this paper, a new technique for human identification task based on heart sound signals has been proposed. It utilizes a feature level fusion technique based on canonical correlation analysis. For this purpose a robust pre-processing scheme based on the wavelet analysis of the heart sounds is introduced. Then, three feature vectors are extracted depending on the cepstral coefficients of different frequency scale representation of the heart sound namely; the mel, bark, and linear scales. Among the investigated feature extraction methods, experimental results show that the mel-scale is the best with 94.4% correct identification rate. Using a hybrid technique combining MFCC and DWT, a new feature vector is extracted improving the system's performance up to 95.12%. Finally, canonical correlation analysis is applied for feature fusion. This improves the performance of the proposed system up to 99.5%. The experimental results show significant improvements in the performance of the proposed system over methods adopting single feature extraction.


Philosophy: 'Antifragility' as a mathematical idea


Intergroup Relations


title: Deep Neural Networks for Bot Detection

abstract: The problem of detecting bots, automated social media accounts governed by software but disguising as human users, has strong implications. For example, bots have been used to sway political elections by distorting online discourse, to manipulate the stock market, or to push anti-vaccine conspiracy theories that caused health epidemics. Most techniques proposed to date detect bots at the account level, by processing large amount of social media posts, and leveraging information from network structure, temporal dynamics, sentiment analysis, etc. In this paper, we propose a deep neural network based on contextual long short-term memory (LSTM) architecture that exploits both content and metadata to detect bots at the tweet level: contextual features are extracted from user metadata and fed as auxiliary input to LSTM deep nets processing the tweet text. Another contribution that we make is proposing a technique based on synthetic minority oversampling to generate a large labeled dataset, suitable for deep nets training, from a minimal amount of labeled data (roughly 3,000 examples of sophisticated Twitter bots). We demonstrate that, from just one single tweet, our architecture can achieve high classification accuracy (AUC>96%) in separating bots from humans. We apply the same architecture to account-level bot detection, achieving nearly perfect classification accuracy (AUC>99%). Our system outperforms previous state of the art while leveraging a small and interpretable set of features yet requiring minimal training data.


title: A nearly best-possible approximation algorithm for node-weighted Steiner trees

abstract: We give the first approximation algorithm for the node-weighted Steiner tree problem. Its performance guarantee is within a constant factor of the best possible unless $\tilde P \supseteq NP$. Our algorithm generalizes to handle other network design problems.


title: Mechanisms of biofilm resistance to antimicrobial agents

abstract: Abstract Biofilms are communities of microorganisms attached to a surface. It has become clear that biofilm-grown cells express properties distinct from planktonic cells, one of which is an increased resistance to antimicrobial agents. Recent work has indicated that slow growth and/or induction of an rpoS -mediated stress response could contribute to biocide resistance. The physical and/or chemical structure of exopolysaccharides or other aspects of biofilm architecture could also confer resistance by exclusion of biocides from the bacterial community. Finally, biofilm-grown bacteria might develop a biofilm-specific biocide-resistant phenotype. Owing to the heterogeneous nature of the biofilm, it is likely that there are multiple resistance mechanisms at work within a single community. Recent research has begun to shed light on how and why surface-attached microbial communities develop resistance to antimicrobial agents.


title: Robust Graph Mode Seeking by Graph Shift

abstract: In this paper, we study how to robustly compute the modes of a graph, namely the dense subgraphs, which characterize the underlying compact patterns and are thus useful for many applications. We first define the modes based on graph density function, then propose the graph shift algorithm, which starts from each vertex and iteratively shifts towards the nearest mode of the graph along a certain trajectory. Both theoretic analysis and experiments show that graph shift algorithm is very efficient and robust, especially when there exist large amount of noises and outliers.


title: Resilient 3D hierarchical architected metamaterials

abstract: Hierarchically designed structures with architectural features that span across multiple length scales are found in numerous hard biomaterials, like bone, wood, and glass sponge skeletons, as well as manmade structures, like the Eiffel Tower. It has been hypothesized that their mechanical robustness and damage tolerance stem from sophisticated ordering within the constituents, but the specific role of hierarchy remains to be fully described and understood. We apply the principles of hierarchical design to create structural metamaterials from three material systems: (i) polymer, (ii) hollow ceramic, and (iii) ceramic–polymer composites that are patterned into self-similar unit cells in a fractal-like geometry. In situ nanomechanical experiments revealed (i) a nearly theoretical scaling of structural strength and stiffness with relative density, which outperforms existing nonhierarchical nanolattices; (ii) recoverability, with hollow alumina samples recovering up to 98% of their original height after compression to ≥50% strain; (iii) suppression of brittle failure and structural instabilities in hollow ceramic hierarchical nanolattices; and (iv) a range of deformation mechanisms that can be tuned by changing the slenderness ratios of the beams. Additional levels of hierarchy beyond a second order did not increase the strength or stiffness, which suggests the existence of an optimal degree of hierarchy to amplify resilience. We developed a computational model that captures local stress distributions within the nanolattices under compression and explains some of the underlying deformation mechanisms as well as validates the measured effective stiffness to be interpreted as a metamaterial property.


title: Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study

abstract: The small-world organization has been hypothesized to reflect a balance between local processing and global integration in the human brain. Previous multimodal imaging studies have consistently demonstrated that the topological architecture of the brain network is disrupted in Alzheimer's disease (AD). However, these studies have reported inconsistent results regarding the topological properties of brain alterations in AD. One potential explanation for these inconsistent results lies with the diverse homogeneity and distinct progressive stages of the AD involved in these studies, which are thought to be critical factors that might affect the results. We investigated the topological properties of brain functional networks derived from resting functional magnetic resonance imaging (fMRI) of carefully selected moderate AD patients and normal controls (NCs). Our results showed that the topological properties were found to be disrupted in AD patients, which showing increased local efficiency but decreased global efficiency. We found that the altered brain regions are mainly located in the default mode network, the temporal lobe and certain subcortical regions that are closely associated with the neuropathological changes in AD. Of note, our exploratory study revealed that the ApoE genotype modulates brain network properties, especially in AD patients.


title: iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making

abstract: People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups. In contrast, the alternative paradigm of individual fairness has received relatively little attention, and this paper advances this less explored direction. The paper introduces a method for probabilistically mapping user records into a lowrank representation that reconciles individual fairness and the utility of classifiers and rankings in downstream applications. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on a variety of real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.


title: Dispersion characteristics of substrate integrated rectangular waveguide

abstract: Dispersion properties of the substrate integrated rectangular waveguide (SIRW) are rigorously obtained using the BI-RME method combined with the Floquet's theorem. Our analysis shows that the SIRW basically has the same guided-wave characteristics as the conventional rectangular waveguide. Empirical equations are derived from the calculated dispersion curves in order to estimate the cutoff frequency of the first two dominant modes of the SIRW To validate the analysis results, an SIRW guide was designed and measured. Very good agreements between the experimental and theoretical results were obtained.


title: Adolescent Health 1 Adolescence: a foundation for future health

abstract: Adolescence is a life phase in which the opportunities for health are great and future patterns of adult health are established. Health in adolescence is the result of interactions between prenatal and early childhood development and the specifi c biological and social-role changes that accompany puberty, shaped by social determinants and risk and protective factors that aff ect the uptake of health-related behaviours. The shape of adolescence is rapidly changing— the age of onset of puberty is decreasing and the age at which mature social roles are achieved is rising. New understandings of the diverse and dynamic eff ects on adolescent health include insights into the eff ects of puberty and brain development, together with social media. A focus on adolescence is central to the success of many public health agendas, including the Millennium Development Goals aiming to reduce child and maternal mortality and HIV/AIDS, and the more recent emphases on mental health, injuries, and non-communicable diseases. Greater attention to adolescence is needed within each of these public health domains if global health targets are to be met. Strategies that place the adolescent years centre stage—rather than focusing only on specifi c health agendas—provide important opportunities to improve health, both in adolescence and later in life.


title: Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

abstract: Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e.g., weather and events). We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i.e. inflow and outflow) in each and every region of a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We have developed a real-time system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd flow monitoring and forecasting in Guiyang City of China. In addition, we present an extensive experimental evaluation using two types of crowd flows in Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known baselines.


title: 28/38GHz dual band slotted patch antenna with proximity-coupled feed for 5G communication

abstract: This paper presents a slotted-rectangular patch antenna with proximity-coupled feed operated at dual band of millimeter-wave (mmV) frequencies, 28GHz and 38GHz. The antenna was built in multilayer substrate construct by 10-layers Low temperature Co-fiber Ceramic (LTCC) with 5 mils thickness each. The slotted-patch and thick substrate are configured to enhance the bandwidth and obtain a good result in gain as well. The bandwidth using are 21.3% at 28GHz and 13.0% at 38GHz with the direction gains are 8.63dBi and 8.62dBi at 28GHz and 38GHz respectively.


title: Pentylindole/Pentylindazole Synthetic Cannabinoids and Their 5-Fluoro Analogs Produce Different Primary Metabolites: Metabolite Profiling for AB-PINACA and 5F-AB-PINACA

abstract: Whereas non-fluoropentylindole/indazole synthetic cannabinoids appear to be metabolized preferably at the pentyl chain though without clear preference for one specific position, their 5-fluoro analogs' major metabolites usually are 5-hydroxypentyl and pentanoic acid metabolites. We determined metabolic stability and metabolites of N-(1-amino-3-methyl-1-oxobutan-2-yl)-1-pentyl-1H-indazole-3-carboxamide (AB-PINACA) and 5-fluoro-AB-PINACA (5F-AB-PINACA), two new synthetic cannabinoids, and investigated if results were similar. In silico prediction was performed with MetaSite (Molecular Discovery). For metabolic stability, 1 μmol/L of each compound was incubated with human liver microsomes for up to 1 h, and for metabolite profiling, 10 μmol/L was incubated with pooled human hepatocytes for up to 3 h. Also, authentic urine specimens from AB-PINACA cases were hydrolyzed and extracted. All samples were analyzed by liquid chromatography high-resolution mass spectrometry on a TripleTOF 5600+ (AB SCIEX) with gradient elution (0.1% formic acid in water and acetonitrile). High-resolution full-scan mass spectrometry (MS) and information-dependent acquisition MS/MS data were analyzed with MetabolitePilot (AB SCIEX) using different data processing algorithms. Both drugs had intermediate clearance. We identified 23 AB-PINACA metabolites, generated by carboxamide hydrolysis, hydroxylation, ketone formation, carboxylation, epoxide formation with subsequent hydrolysis, or reaction combinations. We identified 18 5F-AB-PINACA metabolites, generated by the same biotransformations and oxidative defluorination producing 5-hydroxypentyl and pentanoic acid metabolites shared with AB-PINACA. Authentic urine specimens documented presence of these metabolites. AB-PINACA and 5F-AB-PINACA produced suggested metabolite patterns. AB-PINACA was predominantly hydrolyzed to AB-PINACA carboxylic acid, carbonyl-AB-PINACA, and hydroxypentyl AB-PINACA, likely in 4-position. The most intense 5F-AB-PINACA metabolites were AB-PINACA pentanoic acid and 5-hydroxypentyl-AB-PINACA.


title: Network neuroscience

abstract: Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.


title: Evolution strategies – A comprehensive introduction

abstract: This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany. Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are. Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed.


title: A Real-Time Network Traffic Profiling System

abstract: This paper presents the design and implementation of a real-time behavior profiling system for high-speed Internet links. The profiling system uses flow-level information from continuous packet or flow monitoring systems, and uses data mining and information-theoretic techniques to automatically discover significant events based on the communication patterns of end-hosts. We demonstrate the operational feasibility of the system by implementing it and performing extensive benchmarking of CPU and memory costs using a variety of packet traces from OC-48 links in an Internet backbone network. To improve the robustness of this system against sudden traffic surges such as those caused by denial of service attacks or worm outbreaks, we propose a simple yet effective filtering algorithm. The proposed algorithm successfully reduces the CPU and memory cost while maintaining high profiling accuracy.


OFFER: Off-Environment Reinforcement Learning.


Your liking is my curiosity: a social popularity intervention to induce curiosity.


The water balance of the soil


title: Pushing the limits: a very compact and a threshold implementation of AES

abstract: Our contribution is twofold: first we describe a very compact hardware implementation of AES-128, which requires only 2400 GE. This is to the best of our knowledge the smallest implementation reported so far. Then we apply the threshold countermeasure by Nikova et al. to the AES S-box and yield an implementation of the AES improving the level of resistance against first-order side-channel attacks. Our experimental results on real-world power traces show that although our implementation provides additional security, it is still susceptible to some sophisticated attacks having enough number of measurements.


title: Bayesian Language ModelInterpolation forMobile SpeechInput

abstract: This paper explores various static interpolation methods for approximating a single dynamically-interpolated language model used for a variety of recognition tasks on the Google Android platform. The goal is to find the statically-interpolated firstpass LM that best reduces search errors in a two-pass system or that even allows eliminating the more complex dynamic second pass entirely. Static interpolation weights that are uniform, prior-weighted, and the maximum likelihood, maximum a posteriori, and Bayesian solutions are considered. Analysis argues and recognition experiments on Android test data show that a Bayesian interpolation approach performs best.


title: A Neural Network based Path Planning Algorithm for Extinguishing Forest Fires M.P.Sivaram Kumar 1, S.Rajasekaran 2

abstract: In this work an algorithm for automatic detection and suppression of Forest fires is proposed. The algorithm is implemented using parallel distributed model of neural network with three activation functions to determine the next consecutive moves to the cells for the actor. The algorithm uses reinforcement learning with weights determined dynamically in each iteration. The Entire forest is decomposed into grid of square cells with initial position of the Actor is assumed to be the cell 1 and the goal cell is the cell where the fire has occurred. The neural network model uses starting cell, goal cell and number of cells in each row or column and three activation functions to determine the next consecutive cells in which the robot has to travel. It uses only three movements LEFT, DIAGONAL and UP to reach the target cell. After calculating next cell, the check will be made for presence of obstacles in that cell. If there is any obstacle in that cell, then one cell from other two cells obtained using other two movements, which is free from obstacle will be chosen for next move. Then the cell number is stored in memory. This process is repeated till the next cell computed is same as the goal cell. The Actor will begin to move from start cell and reach the goal cell using the cell numbers available in the memory to extinguish Forest fire. This algorithm is designed keeping in mind only static obstacles and hence it works well for Forest environment with static obstacles. Computer simulation results show that path has been found successfully without collision with obstacles.


title: Application of generalized Bagley-polygon four-port power dividers to designing microwave dual-band bandpass planar filters

abstract: A new type of microwave dual-band bandpass planar filter based on signal-interference techniques is reported. The described filter approach consists of transversal filtering sections made up of generalized Bagley-polygon four-port power dividers. This transversal section, by exploiting feedforward signal-interaction concepts, enables dual-band bandpass filtering transfer functions with several transmission zeros to be obtained. A set of closed formulas and guidelines for the analytical synthesis of the dual-passband transversal filtering section are derived. Moreover, its practical usefulness is proven with the development and testing of a 2.75/3.25-GHz dual-band microstrip prototype.


title: Big Data Platforms and Techniques

abstract: Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in big data environments. Techniques used for big data are machine learning, Data Mining, Neural Network and Deep Learning. There are big data architecture offerings from Microsoft, IBM and National Institute of Standards and Technology. Big data potentials can transform economies and reduce running cost of institutions. Big data has challenges such as storage, computation, security and privacy


title: Parameterizable Fonts Based on Shape Components

abstract: We propose a new, highly flexible font description method that explicitly describes characters as assemblies of parameterizable shape components. By varying global parameters, we can derive fonts that vary in weight, condensation, and shape. Fonts made of shape components are flexible parameterizable designs, which we can easily adapt to various display and printing conditions (such as condensed font when lacking display space, high-quality optical scaling, adaptation of fonts to existing character metrics). Besides applications related to typeface design, fonts based on parameterizable components may be used in portable devices where memory is scarce. A single parameterizable design and its variations may allow enough flexibility for providing both high-quality antialiased fonts at low resolution and typefaces for high-resolution printing.


Cross-Genre Author Profile Prediction Using Stylometry-Based Approach


title: Emerging optical access network technologies for 5G wireless [invited]

abstract: With the advancement of radio access networks, more and more mobile data content needs to be transported by optical networks. Mobile fronthaul is an important network segment that connects centralized baseband units (BBUs) with remote radio units in cloud radio access networks (C-RANs). It enables advanced wireless technologies such as coordinated multipoint and massive multiple-input multiple-output. Mobile backhaul, on the other hand, connects BBUs with core networks to transport the baseband data streams to their respective destinations. Optical access networks are well positioned to meet the first optical communication demands of C-RANs. To better address the stringent requirements of future generations of wireless networks, such as the fifth-generation (5G) wireless, optical access networks need to be improved and enhanced. In this paper, we review emerging optical access network technologies that aim to support 5G wireless with high capacity, low latency, and low cost and power per bit. Advances in high-capacity passive optical networks (PONs), such as 100 Gbit/s PON, will be reviewed. Among the topics discussed are advanced modulation and detection techniques, digital signal processing tailored for optical access networks, and efficient mobile fronthaul techniques. We also discuss the need for coordination between RAN and PON to simplify the overall network, reduce the network latency, and improve the network cost efficiency and power efficiency.


title: A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model

abstract: Highlights? The K-S and AUC values of the behavioral scoring model are 44.6% and 78.7%. ? The K-S and AUC values of the credit bureau scoring model are 51.0% and 80.9%. ? The K-S and AUC values of the dual scoring model are 59.3% and 87.7%. ? The dual scoring model exhibits greater predictive ability. Credit scoring model is an important tool for assessing risks in financial industry, consequently the majority of financial institutions actively develops credit scoring model on the credit approval assessment of new customers and the credit risk management of existing customers. Nonetheless, most past researches used the one-dimensional credit scoring model to measure customer risk. In this study, we select important variables by genetic algorithm (GA) to combine the bank's internal behavioral scoring model with the external credit bureau scoring model to construct the dual scoring model for credit risk management of mortgage accounts. It undergoes more accurate risk judgment and segmentation to further discover the parts which are required to be enhanced in management or control from mortgage portfolio. The results show that the predictive ability of the dual scoring model outperforms both one-dimensional behavioral scoring model and credit bureau scoring model. Moreover, this study proposes credit strategies such as on-lending retaining and collection actions for corresponding customers in order to contribute benefits to the practice of banking credit.


title: 4 A Theory of Remote Scientific Collaboration

abstract: This chapter contains sections titled: The Developing Theory, Factors That Lead to Success, Discussion, Using TORSC, Summary, Acknowledgments, References


title: A general framework for discrete variable representation basis sets

abstract: A framework for discrete variable representation (DVR) basis sets is developed that is suitable for multidimensional generalizations. Those generalizations will be presented in future publications. The new axiomatization of the DVR construction places projection operators in a central role and integrates semiclassical and phase space concepts into the basic framework. Rates of convergence of basis set expansions are emphasized, and it is shown that the DVR method gives exponential convergence, assuming conditions of analyticity and boundary conditions are met. A discussion of nonorthogonal generalizations of DVR functions is presented, in which it is shown that projected δ-functions and interpolating functions form a biorthogonal basis. It is also shown that one of the generalized DVR proposals due to Szalay [J. Chem. Phys. 105, 6940 (1996)] gives exponential convergence.