title: A Social-Network-Based Cryptocurrency Wallet-Management Scheme

abstract: Effective cryptocurrency key management has become an urgent requirement for modern cryptocurrency. Although a large body of cryptocurrency wallet-management schemes has been proposed, they are mostly constructed for specific application scenarios and often suffer from weak security. In this paper, we propose a more effective, usable, and secure cryptocurrency wallet-management system based on semi-trusted social networks, therein allowing users to collaborate with involved parties to achieve some powerful functions and recovery under certain circumstances. Furthermore, we employ an identity-based hierarchical key-insulated encryption scheme to achieve time-sharing authorization and present a semi-trusted portable social-network-based wallet-management scheme that provides the features of security-enhanced storage, portable login on different devices, no-password authentication, flexible key delegation, and so on. The performance analysis shows that our proposed schemes require minimal additional overhead and have low time delays, making them sufficiently efficient for real-world deployment.


title: A compact CMOS Marchand balun incorporating meandered multilayer edge-coupled transmission lines

abstract: This work presents a compact CMOS Marchand balun design, which consists of the meandered two-conductor edge-coupled complementary-conducting-strip coupled-line (CCS CL). The CCS CL, which is fully compatible with the standard CMOS process, provides various structural parameters to synthesize the desired coupling coefficient for Marchand balun realization. The prototype, which is fabricated by the standard 0.18-µm 1P6M CMOS technology, reveals circuit size of 240.0 µm × 240.0 µm (without contact pads). The measured results, which are collected based on 50-Ω system, demonstrate excellent agreements with those of the simulations. The input return loss is less than −15.0 dB from 21.6 GHz to 33.1 GHz. The measured insertion losses between input and two output ports (S 21 and S 31 ) are 5.90 dB and 4.38 dB at 23.6 GHz, respectively. The phase difference between two output ports is 180° ± 10° from 15.5 GHz to 40.0 GHz.


title: Nano-LC FTICR tandem mass spectrometry for top-down proteomics: routine baseline unit mass resolution of whole cell lysate proteins up to 72 kDa.

abstract: Current high-throughput top-down proteomic platforms provide routine identification of proteins less than 25 kDa with 4-D separations. This short communication reports the application of technological developments over the past few years that improve protein identification and characterization for masses greater than 25 kDa. Advances in separation science have allowed increased numbers of proteins to be identified, especially by nanoliquid chromatography (nLC) prior to mass spectrometry (MS) analysis. Further, a goal of high-throughput top-down proteomics is to extend the mass range for routine nLC MS analysis up to 80 kDa because gene sequence analysis predicts that ~70% of the human proteome is transcribed to be less than 80 kDa. Normally, large proteins greater than 50 kDa are identified and characterized by top-down proteomics through fraction collection and direct infusion at relatively low throughput. Further, other MS-based techniques provide top-down protein characterization, however at low resolution for intact mass measurement. Here, we present analysis of standard (up to 78 kDa) and whole cell lysate proteins by Fourier transform ion cyclotron resonance mass spectrometry (nLC electrospray ionization (ESI) FTICR MS). The separation platform reduced the complexity of the protein matrix so that, at 14.5 T, proteins from whole cell lysate up to 72 kDa are baseline mass resolved on a nano-LC chromatographic time scale. Further, the results document routine identification of proteins at improved throughput based on accurate mass measurement (less than 10 ppm mass error) of precursor and fragment ions for proteins up to 50 kDa.


BIRTH ORDER AND SIBLING COMPETITION CHAPTER 2 BIRTH ORDER , SIBLING COMPETITION , AND HUMAN BEHAVIOR


title: Choice Processing in Emotionally Difficult Decisions

abstract: Choice conflicts between one's important values may cause negative emotion. This article extends the standard effort-accuracy approach to explaining task influences on decision processing by arguing that coping goals will interact with effort minimization and accuracy maximization goals for negatively emotion-laden decision tasks. These coping goals may involve both a desire to process in a thorough, accurate manner and a desire to avoid particularly distressing aspects of processing. On the basis of this extended framework, the authors hypothesized and found in 3 experiments that decision processing under increasing negative emotion both becomes more extensive and proceeds more by focusing on one attribute at a time. In particular, increased negative emotion leads to more attribute-based processing at the beginning of the decision process. The results are inconsistent with views that negative emotion acts only as an incentive or only as a source of decision complexity. Individuals make decisions by using a wide variety of processing strategies, ranging from normative procedures that process all relevant information and explicitly consider trade-offs between attributes to more heuristic procedures that use information selectively and avoid trade-offs between attributes. For a number of years, behavioral decision research has addressed how properties of choice tasks influence these decision-processing strategies (Einhom & Hogarth, 1981; Payne, Bettman, & Johnson, 1992). Decision makers are often depicted as deciding how to decide on the basis of trade-offs between the accuracy of various decision strategies and the cognitive effort required to implement those strategies. Thus, research has investigated both the factors affecting the relative effort needed for various strategies (e.g., task complexity, information format, and response mode) and the factors influencing the relative accuracy of various rules or the importance of making an accurate decision (e.g., correlation among attributes and incentives; see Payne, Bettman, & Johnson, 1993, for a review).


title: Modeling Event Importance for Ranking Daily News Events

abstract: We deal with the problem of ranking news events on a daily basis for large news corpora, an essential building block for news aggregation. News ranking has been addressed in the literature before but with individual news articles as the unit of ranking. However, estimating event importance accurately requires models to quantify current day event importance as well as its significance in the historical context. Consequently, in this paper we show that a cluster of news articles representing an event is a better unit of ranking as it provides an improved estimation of popularity, source diversity and authority cues. In addition, events facilitate quantifying their historical significance by linking them with long-running topics and recent chain of events. Our main contribution in this paper is to provide effective models for improved news event ranking. To this end, we propose novel event mining and feature generation approaches for improving estimates of event importance. Finally, we conduct extensive evaluation of our approaches on two large real-world news corpora each of which span for more than a year with a large volume of up to tens of thousands of daily news articles. Our evaluations are large-scale and based on a clean human curated ground-truth from Wikipedia Current Events Portal. Experimental comparison with a state-of-the-art news ranking technique based on language models demonstrates the effectiveness of our approach.


title: PETROGRAPHY AND GEOCHEMISTRY OF THE EARLY-MIDDLE DEVONIAN SANDSTONES OF THE PADEHA FORMATION IN THE NORTH OF KERMAN, SE IRAN. IMPLICATIONS FOR PROVENANCE

abstract: his study presents a petrological and geochemical characterization of the Early-Middle Devonian sandstones of the Padeha Formation in SE Central Iran, north of Kerman in order to determine the provenance on the basis of two measured stratigraphic sections. The sandstones are classified as quartzarenite and sublitharenite, and subordinately as litharenite based on framework composition. Modal analysis data from 55 samples of the Sarashk and Gazuieh sections revealed that the sandstones contain quartz, a few percent feldspars and lithic fragments (sedimentary and volcanic rocks). Most of the quartz grains are monocrystalline with straight to undulate extinction with a few polycrystalline grains. Petrographic studies indicate that the Padeha Formation sandstones may have been derived from plutonic and other recycled orogen rocks. Chemically, the major oxides of the sandstones from the Sarashk section are depleted with respect to the upper continental crust, except SiO2, which is related to the high maturity (presence of quartz) and the absence of Al-bearing minerals (phyllosilicates). The trace element concentrations (La, Th, Sc and Zr) and La/Sc (5.50-16.57), Th/Sc (1.12-4.14), La/Co (1.97-20.66), and Th/Co (0.72-4.75) of the Sarashk section indicate felsic source rocks for the sandstones. Furthermore, high Zr/Sc (48-368) and Th/U (0.5-6.8) values are typical for recycling. The chondrite-normalized rare earth element (REE) patterns support this interpretation. Light REE enrichment, negative Eu anomaly and flat heavy REE pattern are very similar to the old upper continental crust. Combined petrological-geochemical data and local Devonian palaeogeographical models show that the Padeha Formation sandstones mostly derived from felsic source rocks and mature recycled continental sedimentary rocks in the craton interior of Central Iran and adjacent areas and were deposited on the passive margin along the coastal setting of the Paleo-Tethys Ocean.


36 F . E-Commerce Standard Users Interface : Design and Implementation


title: Semantic Matching using Kernel Methods

abstract: Semantic matching (SM) for textual information can be informally defined as the task of effectively modeling text matching using representations more complex than those based on simple and independent set of surface forms of words or stems (typically indicated as bag-of-words). In this perspective, matching named entities (NEs) implies that the associated model can both overcomes mismatch between different representations of the same entities, e.g., George H. W. Bush vs. George Bush, and carry out entity disambiguation to avoid incorrect matches between different but similar entities, e.g., the entity above with his son George W. Bush. This means that both the context and structure of NEs must be taken into account in the IR model. SM becomes even more complex when attempting to match the shared semantics between two larger pieces of text, e.g., phrases or clauses, as there is currently no theory indicating how words should be semantically composed for deriving the meaning of text. The complexity above has traditionally led to define IR models based on bag-of-word representations in the vector space model (VSM), where (i) the necessary structure is minimally taken into account by considering n-grams or phrases; and (ii) the matching coverage is increased by projecting text in latent semantic spaces or alternatively by applying query expansion. Such methods introduce a considerable amount of noise, which negatively balances the benefit of achieving better coverage in most cases, thus producing no IR system improvement. In the last decade, a new class of semantic matching approaches based on the so-called Kernel Methods (KMs) for structured data (see e.g., [4]) have been proposed. KMs also adopt scalar products (which, in this context, take the names of kernel functions) in VSM. However, KMs introduce two new important aspects: • the scalar product is implicitly computed using smart techniques, which enable the use of huge feature spaces, e.g., all possible skip n-grams; and • KMs are typically applied within supervised algorithms, e.g., SVMs, which, exploiting training data, can filter out irrelevant features and noise. In this talk, we will briefly introduce and summarize, the latest results on kernel methods for semantic matching by focusing on structural kernels. These can be applied to match syntactic and/or semantic representations of text shaped as trees. Several variants are available: the Syntactic Tree Kernels (STK), [2], the String Kernels (SK) [5] and the Partial Tree Kernels (PTK) [4]. Most interestingly, we will present tree kernels exploiting SM between words contained in a text structure, i.e., the Syntactic Semantic Tree Kernels (SSTK) [1] and the Smoothed Partial Tree Kernels (SPTK) [3]. These extend STK and PTK by allowing for soft matching (i.e., via similarity computation) between nodes associated with different but related labels, e.g., synonyms. The node similarity can be derived from manually annotated resources, e.g., WordNet or Wikipedia, as well as using corpus-based clustering approaches, e.g., latent semantic analysis (LSA). An example of the use of such kernels for question classification in the question answering domain will illustrate the potentials of their structural similarity approach.


title: Word Cloud Explorer: Text Analytics Based on Word Clouds

abstract: Word clouds have emerged as a straightforward and visually appealing visualization method for text. They are used in various contexts as a means to provide an overview by distilling text down to those words that appear with highest frequency. Typically, this is done in a static way as pure text summarization. We think, however, that there is a larger potential to this simple yet powerful visualization paradigm in text analytics. In this work, we explore the usefulness of word clouds for general text analysis tasks. We developed a prototypical system called the Word Cloud Explorer that relies entirely on word clouds as a visualization method. It equips them with advanced natural language processing, sophisticated interaction techniques, and context information. We show how this approach can be effectively used to solve text analysis tasks and evaluate it in a qualitative user study.


Multi-level partition of unity implicits


title: Cache revive: architecting volatile STT-RAM caches for enhanced performance in CMPs

abstract: High density, low leakage and non-volatility are the attractive features of Spin-Transfer-Torque-RAM (STT-RAM), which has made it a strong competitor against SRAM as a universal memory replacement in multi-core systems. However, STT-RAM suffers from high write latency and energy which has impeded its widespread adoption. To this end, we look at trading-off STT-RAM's non-volatility property (data-retention-time) to overcome these problems. We formulate the relationship between retention-time and write-latency, and find optimal retention-time for architecting an efficient cache hierarchy using STT-RAM. Our results show that, compared to SRAM-based design, our proposal can improve performance and energy consumption by 18% and 60%, respectively.


Vehicle Routing Problem


title: Partial Evaluation of Computation Process , Revisited

abstract: A1: After graduating from the Department of Mathematics at Hokkaido University (in Sapporo) in 1965, I engaged in developing an interpreter and a compiler for Lisp 1.5 at Hitachi Central Research Laboratory. The work was done based only on McCarthy’s manual [12]. When I was implementing the compiler, I realized that I was just looking at the interpreter written in the manual. Then the idea that a compiler could be generated from an interpreter came to me. I think this idea is fundamental. Other results such as partial evaluation and self-application follow the idea with good luck.


Human-Computer Interaction with Augmented Reality


title: Semantic Feature Augmentation in Few-shot Learning.

abstract: A fundamental problem with few-shot learning is the scarcity of data in training. A natural solution to alleviate this scarcity is to augment the existing images for each training class. However, directly augmenting samples in image space may not necessarily, nor sufficiently, explore the intra-class variation. To this end, we propose to directly synthesize instance features by leveraging the semantics of each class. Essentially, a novel auto-encoder network dual TriNet, is proposed for feature augmentation. The encoder TriNet projects multi-layer visual features of deep CNNs into the semantic space. In this space, data augmentation is induced, and the augmented instance representation is projected back into the image feature spaces by the decoder TriNet. Two data argumentation strategies in the semantic space are explored; notably these seemingly simple augmentations in semantic space result in complex augmented feature distributions in the image feature space, resulting in substantially better performance. The code and models of our paper will be published on: this https URL


title: Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship

abstract: Echo chambers, i.e., situations where one is exposed only to opinions that agree with their own, are an increasing concern for the political discourse in many democratic countries. This paper studies the phenomenon of political echo chambers on social media. We identify the two components in the phenomenon: the opinion that is shared ('echo'), and the place that allows its exposure ('chamber' --- the social network), and examine closely at how these two components interact. We define a production and consumption measure for social-media users, which captures the political leaning of the content shared and received by them. By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own. We also find that users who try to bridge the echo chambers, by sharing content with diverse leaning, have to pay a 'price of bipartisanship' in terms of their network centrality and content appreciation. In addition, we study the role of 'gatekeepers', users who consume content with diverse leaning but produce partisan content (with a single-sided leaning), in the formation of echo chambers. Finally, we apply these findings to the task of predicting partisans and gatekeepers from social and content features. While partisan users turn out relatively easy to identify, gatekeepers prove to be more challenging.


title: Dragon Curves Revisited

abstract: It has happened several times in recent history that a mathematical discovery of great beauty and importance was originally published in a journal that would not likely to be read by many a working mathematician. One famous example is the Penrose tilings [15]. Surely, Penrose tilings and the theory of quasicrystals is now a major area of research (see, e.g., [17]), and not only in mathematics but also in physics and chemistry, as witnessed by the 2011 Nobel Prize awarded to D. Shechtman for “the discovery of quasicrystals” in 1982. It is a pleasure to mention that this magazine played a role in popularizing Penrose tilings [16]. The topic of this column is another mathematical object of comparable beauty, the Dragon curves, whose theory was created by Chandler Davis and Donald Knuth [4]. The original articles are not easily available (they are reprinted in [10], along with previously unpublished addendum). Mathematical Intelligencer wrote about Dragon curves more than 30 years ago [5, 6, 7]. In spite of the existence of a Wikipedia article on the subject and in spite of their appearance in M. Crichton’s popular novel “Jurassic Park”, Dragon curves are not sufficiently well known to contemporary mathematicians, especially the younger ones who missed the original excitement of 40+ years ago. The goal of this article is to bring Dragon curves to spotlight again and to pay tribute to Chandler Davis, a co-author of an elegant theory that explains the striking features of these curves. This article is merely an invitation to the subject; the reader should not expect a thorough survey of the results or proofs.


title: Ramp loss linear programming support vector machine

abstract: The ramp loss is a robust but non-convex loss for classification. Compared with other non-convex losses, a local minimum of the ramp loss can be effectively found. The effectiveness of local search comes from the piecewise linearity of the ramp loss. Motivated by the fact that the l1-penalty is piecewise linear as well, the l1-penalty is applied for the ramp loss, resulting in a ramp loss linear programming support vector machine (ramp-LPSVM). The proposed ramp-LPSVM is a piecewise linear minimization problem and the related optimization techniques are applicable. Moreover, the l1-penalty can enhance the sparsity. In this paper, the corresponding misclassification error and convergence behavior are discussed. Generally, the ramp loss is a truncated hinge loss. Therefore ramp-LPSVM possesses some similar properties as hinge loss SVMs. A local minimization algorithm and a global search strategy are discussed. The good optimization capability of the proposed algorithms makes ramp-LPSVM perform well in numerical experiments: the result of ramp-LPSVM is more robust than that of hinge SVMs and is sparser than that of ramp-SVM, which consists of the || ċ || k-penalty and the ramp loss.


Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention


new/s/leak – Information Extraction and Visualization for Investigative Data Journalists


title: The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

abstract: The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge the interpretability of any result derived using it. In this article, we disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of MGR systems in GTZAN are still meaningfully comparable since they all face the same faults. We identify and analyze the contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN, but to use it with consideration of its contents.


title: Incremental-Segment-Based Localization in 3-D Point Clouds

abstract: Localization in 3-D point clouds is a highly challenging task due to the complexity associated with extracting information from 3-D data. This letter proposes an incremental approach addressing this problem efficiently. The presented method first accumulates the measurements in a dynamic voxel grid and selectively updates the point normals affected by the insertion. An incremental segmentation algorithm, based on region growing, tracks the evolution of single segments, which enables an efficient recognition strategy using partitioning and caching of geometric consistencies. We show that the incremental method can perform global localization at 10 Hz in an urban driving environment, a speedup of $\times$ 7.1 over the compared batch solution. The efficiency of the method makes it suitable for applications where real-time localization is required and enables its usage on cheaper low-energy systems. Our implementation is available open source along with instructions for running the system. (The implementation is available at https://github.com/ethz-asl/segmatch and a video demonstration is available at https://youtu.be/cHfs3HLzc2Y .)


title: A robotically-augmented walker for older adults

abstract: Many older adults use walkers to improve their stability and safety while walking. We have developed a robotically augmented walker to reduce fall risk and confusion, and to increase walker convenience and enjoyment. Using a modified version of the CARMEN navigation software suite [11], the walker is capable of parking itself and returning to the user when signaled by remote control. The system also supports navigation in large indoor environments by providing simple directions to target locations such as a cafeteria. The walker received positive reviews during informal testing with residents of a Pennsylvania residence facility for older adults. This research is sponsored by the National Science Foundation under the ITR Program, which is gratefully acknowledged. The views and concussions contained in this document are those of the authors and should not be interpreted as necessarily representing official policies of endorsements, either expressed or implied, of the United States Government of the National Science Foundation.


title: Radio jamming attacks against two popular mobile networks

abstract: The dependence on Mobile Networks is growing. The success of the Internet was followed by Denial of Service attacks. What if the same happens to Mobile Networks? This paper gives an introduction to the concept of Radio Jamming and explores jamming resistance of two popular mobile networks: GSM and WLAN. Radio interfaces of the two systems are analysed and effective jamming-to-noise ratios are calculated. Based on the results, suggestions on how to increase the jamming resistance of the networks are given.


title: Use of LEGO as a therapeutic medium for improving social competence.

abstract: A repeated-measures, waiting list control design was used to assess efficacy of a social skills intervention for autistic spectrum children focused on individual and group LEGO© play. The intervention combined aspects of behavior therapy, peer modeling and naturalistic communication strategies. Close interaction and joint attention to task play an important role in both group and individual therapy activities. The goal of treatment was to improve social competence (SC) which was construed as reflecting three components: (1) motivation to initiate social contact with peers; (2) ability to sustain interaction with peers for a period of time; and (3) overcoming autistic symptoms of aloofness and rigidity. Measures for the first two variables were based on observation of subjects in unstructured situations with peers; and the third variable was assessed using a structured rating scale, the SI subscale of the GARS. Results revealed significant improvement on all three measures at both 12 and 24 weeks with no evidence of gains during the waiting list period. No gender differences were found on outcome, and age of clients was not correlated with outcome. LEGO© play appears to be a particularly effective medium for social skills intervention, and other researchers and clinicians are encouraged to attempt replication of this work, as well as to explore use of LEGO© in other methodologies, or with different clinical populations.


title: Oh that's what you meant!: reducing emoji misunderstanding

abstract: Emoji provide a way to express nonverbal conversational cues in computer-mediated communication. However, people need to share the same understanding of what each emoji symbolises, otherwise communication can breakdown. We surveyed 436 people about their use of emoji and ran an interactive study using a two-dimensional emotion space to investigate (1) the variation in people's interpretation of emoji and (2) their interpretation of corresponding Android and iOS emoji. Our results show variations between people's ratings within and across platforms. We outline our solution to reduce misunderstandings that arise from different interpretations of emoji.


title: Cluster Analysis and Artificial Neural Networks: A Case Study in Credit Card Fraud Detection

abstract: Data normalization for use in Artificial Neural Networks often requires extensive statistical analysis. This paper presents an initial investigation of a case study involving credit card fraud detection, where Cluster Analysis was applied to data normalization. Early results obtained from the use of Artificial Neural Networks and Cluster Analysis on fraud detection has shown that neuronal inputs can be reduced by clustering attributes.


title: Opportunistic Cooperative Networking: To Relay or Not To Relay?

abstract: This paper considers opportunistic cooperative networking (OCN) in wireless ad hoc networks, with a focus on characterizing the desired tradeoff between the probing cost for establishing cooperative relaying and hence higher throughput via opportunistic cooperative networking. Specifically, opportunistic cooperative networking is treated as an optimal stopping problem with two-levels of incomplete information. Cases with or without dedicated relays are considered, and the existence of the optimal strategies for both cases are established. Then, it is shown that for the case with dedicated relays, the optimal strategy exhibits a threshold structure, in which it is optimal to probe the dedicated relay when the signal-to-noise ratio (SNR) of the source-relay link exceeds some threshold. For the case without dedicated relays, under more restrictive conditions, the optimal strategy is also threshold-based, in the sense that it is optimal to probe potential relays when the SNR of the source-destination link lies between two thresholds. Furthermore, these strategies can be implemented in a distributed manner.


title: The Impact of Analytical Assessment of Requirements Prioritization Models: An Empirical Study

abstract: Requirements prioritization is one of the important parts of managing requirements in software development process which plays its role in the success or failure of a software product. A software product can go wrong or fail if right requirements are not prioritized at right time. Thus, there is a need of a vast or complete requirements prioritization technique or model that spans all the factors that must be considered while prioritizing requirements whether it’s for a traditional software development or agile software development. There are several requirements prioritization methodologies that aid in decision making and in prioritizing requirements but importantly many lacks to account the important factors that have significant influence in prioritizing requirements. A requirement prioritization methodology that takes account of important factors such as time and human behavioral factors that has an influence in prioritizing requirements is required. This new model/ technique expected to overcome the lack that is in existing prioritization techniques because of not considering time gap factor and human behavioral factor. Extensive study on literature of agile methodology, requirements elicitation and prioritization has been done to find out factors that influence the decision making process of requirement prioritization. It is found that as agile methodologies such as XP, SCRUM deliver products in increments, there is a time gap between each increment of approximate 4 weeks or more, this time lapse could cause human behavioral to change either because of market demand or any other personal reason and, thus, influences the prioritization decision. These factors could be termed as time factor and human behavioral factors. Thus, a requirement prioritization technique or model is needed that takes account of all such factors while prioritizing requirements whether it’s for a traditional software development or agile software development.


A Comparison of Classifiers for Solar Energetic Events


title: Cross-domain video concept detection using adaptive svms

abstract: Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.


title: Capacities: From Information Theory to Extremal Set Theory

abstract: Abstract Generalizing the concept of zero-error capacity beyond its traditional links to any sort of information transmission we give an asymptotic solution to several hard problems in extremal set theory within a unified, formally information-theoretic framework. The results include the solution of far-reaching generalizations of Renyi's problem on qualitatively independent partitions.


title: A segmented claw-pole motor for traction applications considering recycling aspects

abstract: With the expansion of the fleet of electric and hybrid electric vehicles worldwide, it is of interest to consider recycling aspects of the parts that are introduced in these new vehicles. This paper focuses on the design of an electrical machine, a claw-pole motor, considering recycling of its components. The suggested design has segmented core parts of soft magnetic composites which yield a design very suitable for recycling as the core material is brittle and it thus simplifies the access of the copper winding. The windings are pre-formed ring-coils with a high fill-factor and with no end-windings. Through the use of direct water cooling, employing space between the claws, the current density can be high, and the machine can be compact. The results of finite element simulations of the electromagnetic properties (previously validated against measurements) show that a high performance recyclable motor with ratings above 10 kW can be made. In comparison with a commercial motor, the claw-pole motor has similar performance, a higher core and magnet weight and a lower copper weight. It also has an expected advantage regarding manufacturing cost and cost of recycling and it has lower copper loss.


title: Financial services business process outsourcing

abstract: Applying lessons learned from manufacturing to workflow processes in the financial services industry.


title: Graphulo: Linear Algebra Graph Kernels for NoSQL Databases

abstract: Big data and the Internet of Things era continue to challenge computational systems. Several technology solutions such as NoSQL databases have been developed to deal with this challenge. In order to generate meaningful results from large datasets, analysts often use a graph representation which provides an intuitive way to work with the data. Graph vertices can represent users and events, and edges can represent the relationship between vertices. Graph algorithms are used to extract meaningful information from these very large graphs. At MIT, the Graphulo initiative is an effort to perform graph algorithms directly in NoSQL databases such as Apache Accumulo or SciDB, which have an inherently sparse data storage scheme. Sparse matrix operations have a history of efficient implementations and the Graph Basic Linear Algebra Subprogram (Graph BLAS) community has developed a set of key kernels that can be used to develop efficient linear algebra operations. However, in order to use the Graph BLAS kernels, it is important that common graph algorithms be recast using the linear algebra building blocks. In this article, we look at common classes of graph algorithms and recast them into linear algebra operations using the Graph BLAS building blocks.


title: Algorithm Selection for Classification Problems via Cluster-based Meta-features

abstract: Meta-features describe the characteristics of the datasets to facilitate algorithm selection. This paper proposes a new set of meta-features based on clustering the instances within datasets. We propose the use of a greedy clustering algorithm, and evaluate the meta-features generated based on the learning curves produced by the Random Forest algorithm. We also compared the utility of the proposed meta-features against preexisting meta-features described in the literature, and evaluated the applicability of these meta-features over a sample of UCI datasets. Our results show that these meta-features do indeed improve the performance when applied to the algorithm selection task.


title: A Path to Big Data Readiness

abstract: Big Data readiness" begins at the source where data are first created and extends along a path through an organization to the outside world. This paper focuses on practical solutions to common problems experienced when integrating diverse datasets from disparate sources. Following the Introduction, Section 2 situates Big Data in the larger context of open government, open science, science integrity, and Standards, internationally and in Canada. Section 3 analyses the Big Data problem space, while Section 4 proposes a Big Data solution space. Section 5 proposes eight data checklist modules and suggests implementation strategies to effectively meet a variety of organizational needs. Section 6 summarizes conclusions and describes future work.


title: Speculations on the Evolution of Awareness

abstract: The "attention schema" theory provides one possible account of the biological basis of consciousness, tracing the evolution of awareness through steps from the advent of selective signal enhancement about half a billion years ago to the top-down control of attention, to an internal model of attention which allows a brain, for the first time, to attribute to itself that it has a mind that is aware of something, to the ability to attribute awareness to other beings, and from there to the human attribution of a rich spirit world surrounding us. Humans have been known to attribute awareness to plants, rocks, rivers, empty space, and the universe as a whole. Deities, ghosts, souls-the spirit world swirling around us is arguably the exuberant attribution of awareness.


title: Penile Girth Enhancement With Polymethylmethacrylate-Based Soft Tissue Fillers.

abstract: INTRODUCTION ::: An unknown percentage of men will take every risk to develop a larger penis. Thus far, most injectables have caused serious problems. Polymethylmethacrylate (PMMA) microspheres have been injected as a wrinkle filler and volumizer with increasing safety since 1989. ::: ::: ::: AIM ::: To report on a safe and permanently effective method to enhance penile girth and length with an approved dermal filler (ie, PMMA). ::: ::: ::: METHODS ::: Since 2007, the senior author has performed penile augmentation in 752 men mainly with Metacrill, a suspension of PMMA microspheres in carboxymethyl-cellulose. ::: ::: ::: MAIN OUTCOME MEASURES ::: The data of 729 patients and 203 completed questionnaires were evaluated statistically. ::: ::: ::: RESULTS ::: The overall satisfaction rate was 8.7 on a scale of 1 to 10. After one to three injection sessions, average girth increased by 3.5 cm, or 134% (10.2 to 13.7 cm = 134.31%). Penile length also increased by weight and stretching force of the implant from an average of 9.8 to 10.5 cm. Approximately half the patients perceived some irregularities of the implant, which caused no problems. Complications occurred in 0.4%, when PMMA nodules had to be surgically removed in three of the 24% of patients who had a non-circumcised penis. ::: ::: ::: CONCLUSION ::: After 5 years of development, penile augmentation with PMMA microspheres appears to be a natural, safe, and permanently effective method. The only complication of nodule formation and other irregularities can be overcome by an improved injection technique and better postimplantation care.


title: Using Multi-features and Ensemble Learning Method for Imbalanced Malware Classification

abstract: The ever-growing malware threats in the cyber spacecalls for techniques that are more effective than widely deployedsignature-based detection system. To counter large volumes ofmalware variants, machine learning techniques have been appliedfor automated malware classification. Despite these efforts haveachieved a certain success, the accuracy and efficiency stillremained inadequate to meet demand, especially when thesemachine learning techniques are used in the situation of multipleclass classification and imbalanced training data. Against thisbackdrop, the goal of this paper is to build a malware classificationsystem that could be used to improve the above mentionedsituation. Our system is based on multiple categories of staticfeatures and ensemble learning method. Compared to sometraditional systems it has the following advantages. Firstly, withmultiple categories of features, our system could classify malwareto their corresponding family effectively and efficiently and at thesame time avoid the influence of evasion in certain extent. Ourmethod don't need any unpacking process and extract featuresfrom the bytes file and disassembled asm file directly. Secondly, the system employed two efficient ensemble learning models, namely XGBoost and ExtraTreeClassifer, and also combinedstacking method to construct the final classifier. Finally, weexperimented our system with the dataset provided by Microsofthosted in Kaggle for malware classification competition, andthe final results show that our method could classify malwareto their family effectively and efficiently with the accuracy of0.9972 in training set and logloss of 0.00395 in testing set. Ourwork not only offer insights into how to use multiple features forclassification, but also shed light on how to develop a scalabletechniques for automated malware classification in practice.


title: A Stable Network-Aware VM Placement for Cloud Systems

abstract: Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.


title: Self-determination in a work organization.

abstract: Research testing self-determination theory was discussed in terms of recent work on intrinsic motivation, participative management, and leadership. On three occasions, managers' interpersonal orientations—toward supporting subordinates' self-determination versus controlling their behavior— were related to perceptions, affects, and satisfactions of the subordinates. Data from 23 managers and their subordinates in a major corporation showed that managers' orientations did correlate with the subordinate variables, although the magnitude of the relation varied, seemingly as a function of factors in the corporate climate. An organizational development intervention, focused on the concept of supporting subordinates' self-determination, was provided for the managers. Evaluation of the program showed a clearly positive impact on managers' orientations, though a less conclusive radiation to subordinates. To be self-determining means to experience a sense of choice in initiating and regulating one's own actions. Recent research linking self-determination to, enhanced creativity (Amabile, 1983), conceptual learning (Benware & Deci, 1984), self-esteem (Deci, Schwartz, Sheinman, & Ryan, 1981), and general well-being (Langer & Rodin, 1976) has stimulated psychologists to clarify the antecedent conditions that promote self-determination and to detail the relevance of self-determination to various applied settings. Concepts related to self-determination have been vigorously researched and discussed in the organizational literature for over a quarter century. Argyris (1957) and McGregor (1960), for example, stressed that organizational contexts providing workers the opportunity to satisfy their higher order needs (Maslow, 1943) promote effective performance. Furthermore, management styles (e.g., Likert, 1967; Marrow, Bowers, & Sea


title: Multi-Feature Beat Tracking

abstract: A recent trend in the field of beat tracking for musical audio signals has been to explore techniques for measuring the level of agreement and disagreement between a committee of beat tracking algorithms. By using beat tracking evaluation methods to compare all pairwise combinations of beat tracker outputs, it has been shown that selecting the beat tracker which most agrees with the remainder of the committee, on a song-by-song basis, leads to improved performance which surpasses the accuracy of any individual beat tracker used on its own. In this paper we extend this idea towards presenting a single, standalone beat tracking solution which can exploit the benefit of mutual agreement without the need to run multiple separate beat tracking algorithms. In contrast to existing work, we re-cast the problem as one of selecting between the beat outputs resulting from a single beat tracking model with multiple, diverse input features. Through extended evaluation on a large annotated database, we show that our multi-feature beat tracker can outperform the state of the art, and thereby demonstrate that there is sufficient diversity in input features for beat tracking, without the need for multiple tracking models.


title: Learning Program Embeddings to Propagate Feedback on Student Code

abstract: Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code.org Hour of Code and Stanford University's CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions.


title: A compact edge-fed filtering microstrip antenna with 0.2 dB equal-ripple response

abstract: A compact microstrip antenna with a second order 0.2 dB equal-ripple response and 4 % fractional bandwidth is proposed. This design follows the synthesis process of the bandpass filter. The radiating patch is fed by a new edge-fed gap-coupling mechanism. The coupling section excites the antenna, and it can be an admittance inverter for the filter design as well. Moreover, it does not occupy any additional circuit area. The proposed antenna has the size of 0.6λg × 0.6λ, where λ g is the guiding wavelength in the substrate. The antenna provides good selectivity, flat in-band antenna gain response, and good out-band rejection. The measurement results agree with the simulated ones.


title: Recurrent bilateral pneumothoraces: a rare complication of miliary tuberculosis.

abstract: Pneumothorax as a complication of adult cavitary pulmonary tuberculosis is well known and not at all rare, but its occurrence as a complication of miliary tuberculosis is extremely rare. We report a 22-year-old woman who had nonproductive cough and fever for 3 days. Chest radiography showed diffuse, symmetrical miliary nodulation throughout both lung fields. The patient was treated for a presumed diagnosis of miliary tuberculosis with standard antituberculous regimen. Bilateral pneumothorax occurred simultaneously during hospitalization and chest tube thoracostomy was performed. Three days later, recurrent right pneumothorax developed. Video-assisted thoracoscopic surgery (VATS) lung biopsy of the right lung was performed and pathology showed granulomatous interstitial pneumonia with acid-fast positive bacilli. Lung tissue culture was positive for Mycobacterium tuberculosis. In the following 2 months, bilateral pneumothorax recurred twice and chemical pleurodesis with minocycline was performed on both sides, but air leakage persisted. VATS pleurodesis was performed on both sides successfully without recurrence of pneumothorax on either side. Our experience highlights the fact that pneumothorax should be suspected in an adult with miliary tuberculosis who suddenly develops acute respiratory distress. Recurrent pneumothorax can be managed, apart from medical therapy of miliary tuberculosis, with surgical intervention.


title: CoFAR: Cognitive fully adaptive radar

abstract: A new and fully adaptive environmentally aware (cognitive) radar and signal processing architecture is introduced to meet the challenges of increasingly complex operating environments. The system features fully adaptive transmit, receive, and controller/scheduler functions. “Cognition”, i.e., learning/understanding the complete multidimensional radar channel (targets, clutter, interference, etc.) and operating environment is achieved via a sense-learn-adapt (SLA) approach, which is a radar centric application of the OOPDA (observe, orient, predict, decide, act) loop concept. Learning in turn is achieved via expert system, knowledge-aided (KA) supervised training. Lastly, a MIMO probing approach is introduced as a learning aid for signal dependent channel effects and illustrated with a MTI radar example where it is shown that a full rank estimate of the clutter covariance matrix is possible from the returns in a single range bin, thereby alleviating the so-called “sample starved” covariance estimation problem that arises in highly nonstationary environments.


title: A novel statistical chinese language model and its application in pinyin-to-character conversion

abstract: In this paper, we present a novel Chinese language model, and study its applications, in particular in Chinese pinyin-to-character conversion. In the new model, each word is associated with supporting context constructed by mining the frequent sets of nearby phrases and their distances to the word. Such information was usually overlooked in previous n-gram model and its variants. We apply the model to Chinese pinyin-to-character conversion and find that it offers a better solution to Chinese input. The model has lower perplexity in our evaluation and higher prediction accuracy than the state-of-the-art n-gram Markov model for Chinese language.


title: The Compulsive Online Shopping Scale (COSS): Development and Validation Using Panel Data

abstract: The Internet is one of the most influential forms of mass media having revolutionized human behavior, with people spending more and more time online. However, excessive Internet use, which is also termed as “Internet Addiction”, can have negative consequences for an individual as well as the society in which they reside. This type of addiction is classified as behavioral addiction (DSM-5, American Psychiatric Association 2013). However, a group of American Psychiatric Association (APA) working members deemed insufficient research to consider additional behavioral addictions (e.g., compulsive buying) to be included in psychiatric nosology. Henceforth, one of the goals of this study is to shed light on what is considered “compulsive online shopping” to further support future DSM behavioral addiction classification modifications. The second goal of this study was to develop a compulsive online shopping scale (COSS) that is consistent with addiction criteria established in the DSM-5. And finally, we explored a few characteristic features related to compulsive online shoppers; both demographically and psychologically.


title: Bitcoin Mining as a Contest

abstract: This paper presents a simple game theoretic framework, assuming complete information, to model Bitcoin mining activity. It does so by formalizing the activity as an all-pay contest: a competition where participants contend with each other to win a prize by investing in computational power, and victory is probabilistic. With at least two active miners, the unique pure strategy Nash equilibrium of the game suggests the following interesting insights on the motivation for being a miner: while the optimal amount of energy consumption depends also on the reward for solving the puzzle, as long as the reward is positive the decision to be an active miner depends only on the mining costs. Moreover, the intrinsic structure of the mining activity seems to prevent the formation of a monopoly, because in an equilibrium with two miners, both of them will have positive expected profits for any level of the opponent’s costs. A monopoly could only form if the rate of return on investment were higher outside bitcoin.


DeepPavlov: Open-Source Library for Dialogue Systems


title: MUSIC, maximum likelihood, and Cramer-Rao bound

abstract: The performance of the MUSIC and ML methods is studied, and their statistical efficiency is analyzed. The Cramer-Rao bound (CRB) for the estimation problems is derived, and some useful properties of the CRB covariance matrix are established. The relationship between the MUSIC and ML estimators is investigated as well. A numerical study is reported of the statistical efficiency of the MUSIC estimator for the problem of finding the directions of two plane waves using a uniform linear array. An exact description of the results is included. >


title: ONLINE INFORMATION PRIVACY : MEASURING THE COST-BENEFIT TRADE-OFF

abstract: Concern over information privacy is widespread and rising. However, prior research is silent about the value of information privacy and the benefit of privacy protection. We conducted a conjoint analysis to explore individuals’ trade-offs between the benefits and costs of providing personal information to Websites. We find that economic incentives (monetary reward and future convenience) do affect individuals’ preferences over Websites with differing privacy policies. For instance, the disallowance of secondary use of personal information is worth between $39.83 and $49.78. Surprisingly, we find that cost-benefit trade-offs did not vary with personal characteristics including gender, contextual knowledge, individualism, and trust propensity.


title: BER ANALYSIS OF MITIGATION OF ICI THROUGH ICI SELF CANCELLATION SCHEME IN OFDM SYSTEMS

abstract: OFDM is a multicarrier modulation technique in which a high rate bitstream is split into N parallel bitstreams of lower rate and each of these are modulated using one of N orthogonal sub-carriers. In a basic communication system, the date is modulated onto a single carrier frequency. The available bandwidth is then totally occupied by each symbol. This kind of system can lead to intersymbol-interference (ISI) in case of frequency selective channel. The basic idea of OFDM is to divide the available spectrum into several orthogonal sub channels so that each narrowband subchannels experiences almost flat fading. Orthogonal frequency division multiplexing (OFDM) is becoming the chosen modulation technique for wireless communications. A well known problem of OFDM is its sensitivity to frequency offset between the transmitted and received signals, which may be caused by Doppler shift in the channel, or by the difference between the transmitter and receiver local oscillator frequencies[2]. This carrier frequency offset causes loss of orthogonality between subcarriers and the signals transmitted on each carrier are not independent of each other. The orthogonality of the carriers is no longer maintained, which results in inter-carrier interference (ICI). The undesired ICI degrades the performance of the system[6]. This paper investigates an efficient ICI cancellation method termed ICI selfcancellation scheme for combating the impact of ICI on OFDM systems.


EHR BUSINESS PROCESS MANAGEMENT: FROM PROCESS MINING TO PROCESS IMPROVEMENT TO PROCESS USABILITY


title: Flying Objects Detection from a Single Moving Camera

abstract: We propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. Solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach to motion stabilization of local image patches that allows us to achieve effective classification on spatio-temporal image cubes and outperform state-of-the-art techniques. As the problem is relatively new, we collected two challenging datasets for UAVs and Aircrafts, which can be used as benchmarks for flying objects detection and vision-guided collision avoidance.


Short-term mindfulness intervention reduces the negative attentional effects associated with heavy media multitasking


title: Minimax Optimal Procedures for Locally Private Estimation

abstract: Working under a model of privacy in which data remains private even from the statistician, we study the tradeoff between privacy guarantees and the risk of the resulting statistical estimators. We develop private versions of classical information-theoretic bounds, in particular those due to Le Cam, Fano, and Assouad. These inequalities allow for a precise characterization of statistical rates under local privacy constraints and the development of provably (minimax) optimal estimation procedures. We provide a treatment of several canonical families of problems: mean estimation and median estimation, generalized linear models, and nonparametric density estimation. For all of these families, we provide lower and upper bounds that match up to constant factors, and exhibit new (optimal) privacy-preserving mechanisms and computationally efficient estimators that achieve the bounds. Additionally, we present a variety of experimental results for estimation problems involving sensitive data, including salaries, censored blog posts and articles, and drug abuse; these experiments demonstrate the importance of deriving optimal procedures.


title: Effective Discriminative Feature Selection With Nontrivial Solution

abstract: Feature selection and feature transformation, the two main ways to reduce dimensionality, are often presented separately. In this paper, a feature selection method is proposed by combining the popular transformation-based dimensionality reduction method linear discriminant analysis (LDA) and sparsity regularization. We impose row sparsity on the transformation matrix of LDA through l ::: 2,1 ::: -norm regularization to achieve feature selection, and the resultant formulation optimizes for selecting the most discriminative features and removing the redundant ones simultaneously. The formulation is extended to the l ::: 2,p ::: -norm regularized case, which is more likely to offer better sparsity when 0 <; p <; 1. Thus, the formulation is a better approximation to the feature selection problem. An efficient algorithm is developed to solve the l ::: 2,p ::: -norm-based optimization problem and it is proved that the algorithm converges when 0<; p <; 2. Systematical experiments are conducted to understand the work of the proposed method. Promising experimental results on various types of real-world data sets demonstrate the effectiveness of our algorithm.


title: CS Teacher Experiences with Educational Technology, Problem-BasedLearning, and a CS Principles Curriculum

abstract: Little is known about how K-12 Computer Science (CS) teachers use technology and problem-based learning (PBL) to teach CS content in the context of CS Principles curricula. Significantly, little qualitative research has been conducted in these areas in computer science education, so we lack an in-depth understanding of the complicated realities of CS teachers' experiences. This paper describes the practices and experiences of six teachers' use of technology that was implemented to support PBL in the context of a dual enrollment CS Principles course. Results from an early offering of this course suggest that (1) while CS teachers used technology, they did not appear to use it to support student inquiry, (2) local adaptations to the curriculum were largely teacher-centric, and (3) the simultaneous adoption of new instructional practices, technologies, and curricula was overwhelming to teachers. This paper then describes how these results were used to modify the curriculum and professional development, leading to increased teacher satisfaction and student success in the course.


title: The evolution of general intelligence

abstract: Among Richard Lynn’s numerous significant contributions to science is his cold winters theory of the evolution of general intelligence. The cold winters of Eurasia presented novel adaptive problems for our ancestors to solve, such as obtaining food by hunting large animals and keeping warm by building clothing, shelter and fire, and they functioned as strong selection pressures for higher intelligence. Empirical analyses support both Lynn’s cold winters theory and my evolutionary novelty theory of the evolution of general intelligence. Mean annual temperature and the degree of evolutionary novelty in the environment independently predict the average intelligence of the population. Both theories can also account for the observed race difference in intelligence.


title: Sensory Processing, Physiological Stress, and Sleep Behaviors in Children with and without Autism Spectrum Disorders

abstract: Sleep problems have been frequently identified in children with autism spectrum disorders (ASD). It has been proposed that some sleep problems are due to sensory sensitivity. The purpose of this study was to examine the relationship between physiologic responses to sensation and sleep in children with and without ASD. Fifty-five children participated in the study (ASD, n = 27; typical, n = 28). All children participated in a sensory challenge laboratory protocol. Electrodermal reactivity and salivary cortisol were used as physiological indicators of sensory responsivity. Behavioral data were collected using the Sensory Profile and the Child Behavior Checklist. Results confirmed that children with ASD have a higher prevalence of atypical sensory behaviors and sleep disturbances than typical children. Behavioral and physiological measures were able to predict good sleepers versus poor sleepers with 85.7% accuracy, suggesting that atypical sensory behaviors are important to consider in relation to sleep defi...


DeepMD : Transforming How We Diagnose Heart Disease Using Convolutional Neural Networks


title: Recognizing objects in adversarial clutter: breaking a visual CAPTCHA

abstract: In this paper we explore object recognition in clutter. We test our object recognition techniques on Gimpy and EZ-Gimpy, examples of visual CAPTCHAs. A CAPTCHA ("Completely Automated Public Turing test to Tell Computers and Humans Apart") is a program that can generate and grade tests that most humans can pass, yet current computer programs can't pass. EZ-Gimpy, currently used by Yahoo, and Gimpy are CAPTCHAs based on word recognition in the presence of clutter. These CAPTCHAs provide excellent test sets since the clutter they contain is adversarial; it is designed to confuse computer programs. We have developed efficient methods based on shape context matching that can identify the word in an EZ-Gimpy image with a success rate of 92%, and the requisite 3 words in a Gimpy image 33% of the time. The problem of identifying words in such severe clutter provides valuable insight into the more general problem of object recognition in scenes. The methods that we present are instances of a framework designed to tackle this general problem.


title: Privacy and contextual integrity: framework and applications

abstract: Contextual integrity is a conceptual framework for understanding privacy expectations and their implications developed in the literature on law, public policy, and political philosophy. We formalize some aspects of contextual integrity in a logical framework for expressing and reasoning about norms of transmission of personal information. In comparison with access control and privacy policy frameworks such as RBAC, EPAL, and P3P, these norms focus on who personal information is about, how it is transmitted, and past and future actions by both the subject and the users of the information. Norms can be positive or negative depending on whether they refer to actions that are allowed or disallowed. Our model is expressive enough to capture naturally many notions of privacy found in legislation, including those found in HIPAA, COPPA, and GLBA. A number of important problems regarding compliance with privacy norms, future requirements associated with specific actions, and relations between policies and legal standards reduce to standard decision procedures for temporal logic.


title: FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNN) are verycomputation-intensive. Recently, a lot of CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we observed that there is a big mismatch between the parallel types supported by computing engine and the dominant parallel types of CNN workloads. This mismatch seriously degrades resource utilization of existing accelerators. In this paper, we propose aflexible dataflow architecture (FlexFlow) that can leverage the complementary effects among feature map, neuron, and synapse parallelism to mitigate the mismatch. We evaluated our design with six typical practical workloads, it acquires 2-10x performance speedup and 2.5-10x power efficiency improvement compared with three state-of-the-art accelerator architectures. Meanwhile, FlexFlow is highly scalable with growing computing engine scale.


title: Spatially Adaptive Computation Time for Residual Networks

abstract: This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.


title: Social Media Use in the United States: Implications for Health Communication

abstract: Background: Given the rapid changes in the communication landscape brought about by participative Internet use and social media, it is important to develop a better understanding of these technologies and their impact on health communication. The first step in this effort is to identify the characteristics of current social media users. Up-to-date reporting of current social media use will help monitor the growth of social media and inform health promotion/communication efforts aiming to effectively utilize social media. Objective: The purpose of the study is to identify the sociodemographic and health-related factors associated with current adult social media users in the United States. Methods: Data came from the 2007 iteration of the Health Information National Trends Study (HINTS, N = 7674). HINTS is a nationally representative cross-sectional survey on health-related communication trends and practices. Survey respondents who reported having accessed the Internet (N = 5078) were asked whether, over the past year, they had (1) participated in an online support group, (2) written in a blog, (3) visited a social networking site. Bivariate and multivariate logistic regression analyses were conducted to identify predictors of each type of social media use. Results: Approximately 69% of US adults reported having access to the Internet in 2007. Among Internet users, 5% participated in an online support group, 7% reported blogging, and 23% used a social networking site. Multivariate analysis found that younger age was the only significant predictor of blogging and social networking site participation; a statistically significant linear relationship was observed, with younger categories reporting more frequent use. Younger age, poorer subjective health, and a personal cancer experience predicted support group participation. In general, social media are penetrating the US population independent of education, race/ethnicity, or health care access. Conclusions: Recent growth of social media is not uniformly distributed across age groups; therefore, health communication programs utilizing social media must first consider the age of the targeted population to help ensure that messages reach the intended audience. While racial/ethnic and health status–related disparities exist in Internet access, among those with Internet access, these characteristics do not affect social media use. This finding suggests that the new technologies, represented by social media, may be changing the communication pattern throughout the United States. [J Med Internet Res 2009;11(4):e48]


title: Mondrian memory protection

abstract: Mondrian memory protection (MMP) is a fine-grained protection scheme that allows multiple protection domains to flexibly share memory and export protected services. In contrast to earlier page-based systems, MMP allows arbitrary permissions control at the granularity of individual words. We use a compressed permissions table to reduce space overheads and employ two levels of permissions caching to reduce run-time overheads. The protection tables in our implementation add less than 9% overhead to the memory space used by the application. Accessing the protection tables adds than 8% additional memory references to the accesses made by the application. Although it can be layered on top of demand-paged virtual memory, MMP is also well-suited to embedded systems with a single physical address space. We extend MMP to support segment translation which allows a memory segment to appear at another location in the address space. We use this translation to implement zero-copy networking underneath the standard read system call interface, where packet payload fragments are connected together by the translation system to avoid data copying. This saves 52% of the memory references used by a traditional copying network stack.


title: A 10-Gb/s receiver with a continuous-time linear equalizer and 1-tap decision-feedback equalizer

abstract: This paper presents a wireline receiver design of CMOS I/O at 10-Gb/s data rate. A power efficient continuous-time linear equalizer (CTLE) and 1-tap lookahead decision feedback equalizer (DFE) are designed and implemented in a 45 nm CMOS technology. The DFE employs a sampler including a current injection section that makes no use of summer as a separated block. In addition, cascode structure increases kick-back noise immunity and reduces power consumption by 11%. The PLL used in the proposed receiver drives 5 GHz clock frequency with 12.62 ps pk-pk jitter. The core receiver circuit consumes 14.3 mW at a 1.1 V supply voltage when processing 10 Gb/s data rate with 15 dB of channel loss at Nyquist frequency.


title: Factorized Graph Matching

abstract: Graph matching plays a central role in solving correspondence problems in computer vision. Graph matching problems that incorporate pair-wise constraints can be cast as a quadratic assignment problem (QAP). Unfortunately, QAP is NP-hard and many algorithms have been proposed to solve different relaxations. This paper presents factorized graph matching (FGM), a novel framework for interpreting and optimizing graph matching problems. In this work we show that the affinity matrix can be factorized as a Kronecker product of smaller matrices. There are three main benefits of using this factorization in graph matching: (1) There is no need to compute the costly (in space and time) pair-wise affinity matrix; (2) The factorization provides a taxonomy for graph matching and reveals the connection among several methods; (3) Using the factorization we derive a new approximation of the original problem that improves state-of-the-art algorithms in graph matching. Experimental results in synthetic and real databases illustrate the benefits of FGM. The code is available at http://humansensing.cs.cmu.edu/fgm.


Sharding PoW-based Blockchains via Proofs of Knowledge


title: Absorption-enhancing mechanism of sodium caprate and decanoylcarnitine in Caco-2 cells.

abstract: The mechanism of action of the absorption enhancers such as sodium caprate (C10) and decanoylcarnitine (DC) was examined. Both C10 and DC increased the epithelial permeability of fluorescein isothiocyanate dextran 4000 and decreased the transepithelial electrical resistance in Caco-2 cell monolayer. Irrespective of the presence or absence of mucosal calcium, C10 rapidly increased intracellular calcium levels dose-dependently. Compound 48/80, a phospholipase C inhibitor, prevented the increases of the intracellular calcium level and permeability of fluorescein isothiocyanate dextran 4000 by C10. Furthermore, N-(6-aminohexyl)-5-chloro-1-naphthalenesulfonamide hydrochloride, a strong calmodulin inhibitor, also significantly decreased the enhancing effect of C10. These results suggest that C10 releases calcium from intracellular stores via activation of phospholipase C in plasma membrane. The increase of the calcium levels was considered to induce the contraction of calmodulin-dependent actin microfilament, followed by dilatation of the paracellular route. Although DC also increased intracellular calcium levels, neither compound 48/80 nor N-(6-aminohexyl)-5-chloro-1-naphthalenesulfonamide hydrochloride decreased the enhancing effect of DC. The enhancing mechanisms were different for C10 and DC.


title: A Convolutional Attention Network for Extreme Summarization of Source Code

abstract: Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariant and would be valuable for directing the model's attention, but previous attentional architectures are not constructed to learn such features specifically. We introduce an attentional neural network that employs convolution on the input tokens to detect local time-invariant and long-range topical attention features in a context-dependent way. We apply this architecture to the problem of extreme summarization of source code snippets into short, descriptive function name-like summaries. Using those features, the model sequentially generates a summary by marginalizing over two attention mechanisms: one that predicts the next summary token based on the attention weights of the input tokens and another that is able to copy a code token as-is directly into the summary. We demonstrate our convolutional attention neural network's performance on 10 popular Java projects showing that it achieves better performance compared to previous attentional mechanisms.


title: Learning Contour-Fragment-based Shape Model with And-Or Tree Representation

abstract: This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the form of an And-Or tree. This model extends the traditional hierarchical tree structures by introducing the “switch” variables (i.e. the or-nodes) that explicitly specify production rules to capture shape variations. We thus define the model with three layers: the leaf-nodes for detecting local contour fragments, the or-nodes specifying selection of leaf-nodes, and the root-node encoding the holistic distortion. In the training stage, for optimization of the And-Or tree learning, we extend the concave-convex procedure (CCCP) by embedding the structural clustering during the iterative learning steps. The inference of shape detection is consistent with the model optimization, which integrates the local testings via the leaf-nodes and or-nodes with the global verification via the root-node. The advantages of our approach are validated on the challenging shape databases (i.e., ETHZ and INRIA Horse) and summarized as follows. (1) The proposed method is able to accurately localize shape contours against unreliable edge detection and edge tracing. (2) The And-Or tree model enables us to well capture the intraclass variance.


title: A study of the scaling up capabilities of stratified prototype generation

abstract: Prototype generation is an appropriate data reduction process for improving the efficiency and the efficacy of the nearest neighbor rule. Specifically, evolutionary prototype generation techniques have been highlighted as the best performing methods. However, these methods can sometimes be inefficient when the data scale up. In other data reduction techniques, such as prototype selection, an stratification procedure has been successfully developed to deal with large data sets. In this study, we test the combination of stratification with prototype generation techniques, considering data sets with more than 10000 instances. We compare some of the most representative prototype reduction methods and perform a study of the effects of stratification in their behavior. The results, contrasted with nonparametric statistical tests, show that several prototype generation techniques present a better performance than previously analyzed methods.


title: Mobility Load Balancing Scheme based on Cell Reselection

abstract: Mobility Load Balancing (MLB) is one of the most important functions of Self-Organizing Networks (SON) in Long Term Evolution (LTE). The conventional MLB schemes based on handover (HO) conflict with Mobility Robustness Optimization (MRO) because both operations adjust the same HO parameters. The simulation results show that the conventional MLB scheme cannot achieve load balancing gain without some degradation in HO performance. In order to solve the conflict problem, this paper proposes an MLB scheme based on cell reselection (CR) that works in coordination with MRO. The proposed scheme adjusts the CR parameters and not the HO parameters, and never conflicts with MRO. Through computer simulations, it is shown that the proposed scheme can realize effective load balancing on a par with conventional schemes without any degradation in HO performance. The simulation results show that the proposed scheme is especially effective in an environment where a lot of small-size data packets are transmitted by a large number of users, which is highly applicable to current mobile networks with explosive diffusion of smart phones. In such case, more than 10% and 90% gains can be obtained in the total throughput and 5 th percentile user throughput, respectively.


title: A Key Power Trade-off in Wireless EEG Headset Design

abstract: The development of wireless ambulatory EEG is crucial in enabling the longer term monitoring of a patient in their everyday environment. The analysis presented here will aid the designer of a wireless EEG headset in improving the ratio of battery lifetime to battery size, with the aim of minimising the size and weight of the device. Data compression is proposed as a method to reduce the power used by the wireless transceiver, shown to dominate the system power budget. Graphs are presented which show the power available to perform varying degrees of compression in order to achieve the required lifetime or battery volume


title: A workload-driven logical design approach for NoSQL document databases

abstract: NoSQL databases are designed to manage large volumes of data. Although they do not require a default schema associated with the data, they are categorized by data models. Because of this, data organization in NoSQL databases needs significant design decisions because they affect quality requirements such as scalability, consistency and performance. In traditional database design, on the logical modeling phase, a conceptual schema is transformed into a schema with lower abstraction and suitable to the target database data model. In this context, the contribution of this paper is an approach for logical design of NoSQL document databases. Our approach consists in a process that converts a conceptual modeling into efficient logical representations for a NoSQL document database. Workload information is considered to determine an optimized logical schema, providing a better access performance for the application. We evaluate our approach through a case study in the e-commerce domain and demonstrate that the NoSQL logical structure generated by our approach reduces the amount of items accessed by the application queries.


title: Genetics-based Machine Learning

abstract: This is a survey of the field of Genetics-based Machine Learning (GBML): the application of evolutionary algorithms to machine learning. We assume readers are familiar with evolutionary algorithms and their application to optimisation problems, but not necessarily with machine learning. We briefly outline the scope of machine learning, introduce the more specific area of supervised learning, contrast it with optimisation and present arguments for and against GBML. Next we introduce a framework for GBML which includes ways of classifying GBML algorithms and a discussion of the interaction between learning and evolution. We then review the following areas with emphasis on their evolutionary aspects: GBML for sub-problems of learning, genetic programming, evolving ensembles, evolving neural networks, learning classifier systems, and genetic fuzzy systems.


title: Person Re-Identification by Dual-Regularized KISS Metric Learning

abstract: Person re-identification aims to match the images of pedestrians across different camera views from different locations. This is a challenging intelligent video surveillance problem that remains an active area of research due to the need for performance improvement. Person re-identification involves two main steps: feature representation and metric learning. Although the keep it simple and straightforward (KISS) metric learning method for discriminative distance metric learning has been shown to be effective for the person re-identification, the estimation of the inverse of a covariance matrix is unstable and indeed may not exist when the training set is small, resulting in poor performance. Here, we present dual-regularized KISS (DR-KISS) metric learning. By regularizing the two covariance matrices, DR-KISS improves on KISS by reducing overestimation of large eigenvalues of the two estimated covariance matrices and, in doing so, guarantees that the covariance matrix is irreversible. Furthermore, we provide theoretical analyses for supporting the motivations. Specifically, we first prove why the regularization is necessary. Then, we prove that the proposed method is robust for generalization. We conduct extensive experiments on three challenging person re-identification datasets, VIPeR, GRID, and CUHK 01, and show that DR-KISS achieves new state-of-the-art performance.


Influence of heat-treatment on Selective Laser Melting products


title: Realtime performance-based facial animation

abstract: This paper presents a system for performance-based character animation that enables any user to control the facial expressions of a digital avatar in realtime. The user is recorded in a natural environment using a non-intrusive, commercially available 3D sensor. The simplicity of this acquisition device comes at the cost of high noise levels in the acquired data. To effectively map low-quality 2D images and 3D depth maps to realistic facial expressions, we introduce a novel face tracking algorithm that combines geometry and texture registration with pre-recorded animation priors in a single optimization. Formulated as a maximum a posteriori estimation in a reduced parameter space, our method implicitly exploits temporal coherence to stabilize the tracking. We demonstrate that compelling 3D facial dynamics can be reconstructed in realtime without the use of face markers, intrusive lighting, or complex scanning hardware. This makes our system easy to deploy and facilitates a range of new applications, e.g. in digital gameplay or social interactions.


title: Hybrid Particle Swarm Optimiser with Breeding and Subpopulations

abstract: In this paper we present two hybrid Particle Swarm Optimisers combining the idea of the particle swarm with concepts from Evolutionary Algorithms. The hybrid PSOs combine the traditional velocity and position update rules with the ideas of breeding and subpopulations. Both hybrid models were tested and compared with the standard PSO and standard GA models. This is done to illustrate that PSOs with breeding strategies have the potential to achieve faster convergence and the potential to find a better solution. The objective of this paper is to describe how to make the hybrids benefit from genetic methods and to test their potential and competetiveness on function optimisation.


Crafting Adversarial Attacks on Recurrent Neural Networks


title: A 23pW, 780ppm/°C resistor-less current reference using subthreshold MOSFETs

abstract: This paper proposes a MOSFET-only, 20pA, 780ppm/°C current reference that consumes 23pW. The ultra-low power circuit exploits subthreshold-biased MOSFETs and a complementary-to-absolute temperature (CTAT) gate voltage to compensate for temperature dependency. The design shows low supply voltage sensitivity of 0.58%/V and a load sensitivity of 0.25%/V.


title: A PSO based method for detection of brain tumors from MRI

abstract: Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained for three performance measures are compared with those obtained from Support Vector Machine (SVM) and Ada Boost. The performance analysis shows that qualitative results obtained from the proposed model are comparable with those obtained by SVM. However, to obtain better results from the proposed algorithm we need to carefully select the different values of PSO control parameters.


title: Image contrast enhancement by automatic multi-histogram equalization for satellite images

abstract: In this paper, a new automatic histogram equalization algorithm which is based on Bi-Histogram Equalization (BHE) is proposed. The proposed method preserves the brightness and also improves the contrast. Mean value of the intensity is used for selecting the thresholds to avoid over enhancement also improving contrast of the image. The calculated mean limits the valley points to divide the histogram into small parts which guarantee equal input and output mean brightness. In addition, normalization of image brightness is applied to assure less Absolute Mean Brightness Error (AMBE). The performances of proposed algorithm, Global Histogram equalization algorithm and Bi-Histogram equalization algorithm are measured by calculating another validity parameter called Peak Signal to Noise Ratio (PSNR). The proposed method confirms the improvement in qualitative parameters as compared with the other two algorithms considered.


Cognitive-behavioral theory


title: High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description

abstract: A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.


title: Jaya a novel optimization algorithm: What, how and why?

abstract: This paper presents a comprehensive discussion of the Jaya algorithm, a novel approach for the optimization. There exist two broad categories of heuristic algorithms are: evolutionary algorithms and swarm intelligence. These algorithms' performance vastly depends on the parameters used need extensive tuning during the computational experiments to achieve the superior performance. The Jaya algorithm is a new optimization algorithm has been proposed recently is parameter less and therefore parameters tuning is not needed for it. The primary aim of this paper is to discuss the Jaya algorithm based on the rational aspects outlined as: (a) what is Jaya algorithm; (b) How it works and (c) why one should use it. The author believes that this discussion might be useful to explore the potential of the Jaya to the general audience working for the optimization.


title: Corrections to "Complex Permittivity Measurements of Common Plastics Over Variable Temperatures"

abstract: Despite our best efforts to present error-free measurements to the IEEE Microwave Theory and Techniques Society (IEEE MTT-S), one of the figures in the above paper [1] contains an incorrect scaling factor. In [1, Fig. 11], the loss tangent data for polycarbonate is low by a factor of ten. The correct data is shown in Fig. 1 in this paper. We apologize for any confusion this error may have caused.


title: Early diagnosis of Alzheimer's disease: A multi-class deep learning framework with modified k-sparse autoencoder classification

abstract: Successful, timely diagnosis of neuropsychiatry diseases is key to management. Research efforts in the area of diagnosis of Alzheimer's disease have used various aspects of computer-aided multi-class diagnosis approaches with varied degrees of success. However, there is still need for more efficient and reliable approaches to successful diagnosis of the disease. This research used deep learning framework with modified k-sparse autoencoder (oKSA) classification to locate neutrally degenerated areas of the brain magnetic resonance imaging (MRI), low amyloid beta 1–42 imaging in cerebrospinal fluid (CSF) and positron emission tomography (PET) imaging of amyloid; each with a sample of 150 images. Results show a correlation between computational demarcation of infected regions and the images. Degeneration in the studied areas was evidenced by high phosphorylated t-/p-tau levels in CSF, regional fluorodeoxyglucose PET, and the presence of atrophy patterns. The use of oKSA algorithm in boosting classification helped to improve the classifier performance. The KSA method with deep learning framework is used for the first time to produce accurate results in diagnosis of Alzheimer's disease.


title: Tourists’ Environmentally Responsible Behavior in Response to Climate Change and Tourist Experiences in Nature-Based Tourism

abstract: Nature-based tourism destinations—locations in which economic viability and environmental responsibility are sought—are sensitive to climate change and its effects on important environmental components of the tourism areas. To meet the dual roles, it is important for destination marketers and resources managers to provide quality experiences for tourists and to induce tourists’ environmentally responsible behavior in such destinations. This study documents the importance of perceptions toward climate change and tourist experiences in determining tourists’ environmentally responsible behavior while enjoying holidays at nature-based tourism destinations in Jeju Island, South Korea. Two hundred and eleven Korean and 204 Chinese tourists marked dominant tourist arrivals to the island, and responded to the survey questionnaire. Results showed that perceptions toward climate change and tourist experiences affect Korean tourists’ environmentally responsible behavior intentions, whereas tourist experiences—not perceptions toward climate change—only significantly affect Chinese tourists’ behavior intention. In a nature-based tourism context under the pressure of climate change and adverse environmental effects as consequences of tourism activities, resources managers and destination marketers need to develop environmental campaigns or informative tourist programs to formulate environmentally responsible behavior as well as to increase tourist quality experiences among domestic and international tourists.


title: Adaptive diffusion flow active contours for image segmentation

abstract: Gradient vector flow (GVF) active contour model shows good performance at concavity convergence and initialization insensitivity, yet it is susceptible to weak edges as well as deep and narrow concavity. This paper proposes a novel external force, called adaptive diffusion flow (ADF), with adaptive diffusion strategies according to the characteristics of an image region in the parametric active contour model framework for image segmentation. We exploit a harmonic hypersurface minimal functional to substitute smoothness energy term in GVF for alleviating the possible leakage. We make use of the p(x) harmonic maps, in which p(x) ranges from 1 to 2, such that the diffusion process of the flow field can be adjusted adaptively according to image characteristics. We also incorporate an infinity laplacian functional to ADF active contour model to drive the active contours onto deep and narrow concave regions of objects. The experimental results demonstrate that ADF active contour model possesses several good properties, including noise robustness, weak edge preserving and concavity convergence.


title: Detection and Classification of Plant Leaf Diseases using ANN

abstract: Plant diseases problem can cause significant reduction in both quality and quantity of agricultural products. Automatic detection of plant leaf diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. The proposed system is a software solution for automatic detection and computation of plant leaf diseases. The developed processing scheme consists of five main steps, first a color transformation structure for the input RGB image is created, then the noise i.e. unnecessary part is removed using specific threshold value, then the image is segmented with connected component labeling and the useful segments are extracted, finally the ANN classification is computed by giving different features i.e. size, color, proximity and average centroid distance. Experimental results on a database of 4 different diseases confirms the robustness of the proposed approach.


title: A 4 kb Metal-Fuse OTP-ROM Macro Featuring a 2 V Programmable 1.37 $\mu$ m$^{2}$ 1T1R Bit Cell in 32 nm High-k Metal-Gate CMOS

abstract: A 4 kb high-density PROM array featuring the first high-volume manufacturable metal-fuse technology in 32 nm high-k metal-gate CMOS is introduced. In contrast to traditional salicided polysilicon based 2-D fuse cells, the metal-fuse technology enables a 3-D cell topology with program device and fuse element stacked on each other, achieving a 1.37 μm2 cell footprint. The 128-row by 32-column array with an asymmetric tunable static sense scheme can operate down to 0.5 V and provides multi-bit programming capability. Programming success using a 2 V-1 μs pulse condition is demonstrated. The technology is scalable and maintains full compatibility with modern high-k metal-gate processes.