nils-herrmann/clean_scirepeval huggingface dataset:
A critical examination of 3D stackable NAND Flash memory architectures by simulation study of the scaling capability

Various 3D NAND Flash array architectures including P-BiCS, TCAT, VSAT, and VG are critically examined in this work by extensive 3D TCAD simulations. All structures have X,Y lateral scaling limitation since the minimal ONO thickness (∼20 nm) and poly channel thickness (∼10nm) can not be scaled further. Among them VG may have the best X-direction scalability to F∼2X nm node, and no penalty of increasing Z layer number since the channel current flows horizontally. We propose a buried-channel junction-free NAND to improve the read current for all 3D NAND arrays and our simulation results well support this structure. For the first time, “Z-interference” in 3D NAND Flash is examined and it indicates a new Z-direction scaling limitation. The present work is of crucial importance in understanding various 3D NAND Flash approaches.
Neural Combinatorial Optimization with Reinforcement Learning

This paper presents a framework to tackle combinatorial opt imization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent network that, g iven a set of city coordinates, predicts a distribution over different city p ermutations. Using negative tour length as the reward signal, we optimize the parame ters of the recurrent network using a policy gradient method. We compare learning the network parameters on a set of training graphs against learning them on ind ividual test graphs. The best results are obtained when the network is first optimi zed on a training set and then refined on individual test graphs. Without any super vision and with minimal engineering, Neural Combinatorial Optimization achi eves close to optimal results on 2D Euclidean graphs with up to 100 nodes.
You are what you like! Information leakage through users' Interests

Suppose that a Facebook user, whose age is hidden or missing, likes Britney Spears. Can you guess his/her age? Knowing that most Britney fans are teenagers, it is fairly easy for humans to answer this question. Interests (or “likes”) of users is one of the highly-available on-line information. In this paper, we show how these seemingly harmless interests (e.g., music interests) can leak privacysensitive information about users. In particular, we infer their undisclosed (private) attributes using the public attributes of other users sharing similar interests. In order to compare user-defined interest names, we extract their semantics using an ontologized version of Wikipedia and measure their similarity by applying a statistical learning method. Besides self-declared interests in music, our technique does not rely on any further information about users such as friend relationships or group belongings. Our experiments, based on more than 104K public profiles collected from Facebook and more than 2000 private profiles provided by volunteers, show that our inference technique efficiently predicts attributes that are very often hidden by users. To the best of our knowledge, this is the first time that user interests are used for profiling, and more generally, semantics-driven inference of private data is addressed.
The sil Locus in Streptococcus Anginosus Group: Interspecies Competition and a Hotspot of Genetic Diversity

The Streptococcus Invasion Locus (Sil) was first described in Streptococcus pyogenes and Streptococcus pneumoniae, where it has been implicated in virulence. The two-component peptide signaling system consists of the SilA response regulator and SilB histidine kinase along with the SilCR signaling peptide and SilD/E export/processing proteins. The presence of an associated bacteriocin region suggests this system may play a role in competitive interactions with other microbes. Comparative analysis of 42 Streptococcus Anginosus/Milleri Group (SAG) genomes reveals this to be a hot spot for genomic variability. A cluster of bacteriocin/immunity genes is found adjacent to the sil system in most SAG isolates (typically 6-10 per strain). In addition, there were two distinct SilCR peptides identified in this group, denoted here as SilCRSAG-A and SilCRSAG-B, with corresponding alleles in silB. Our analysis of the 42 sil loci showed that SilCRSAG-A is only found in Streptococcus intermedius while all three species can carry SilCRSAG-B. In S. intermedius B196, a putative SilA operator is located upstream of bacteriocin gene clusters, implicating the sil system in regulation of microbe-microbe interactions at mucosal surfaces where the group resides. We demonstrate that S. intermedius B196 responds to its cognate SilCRSAG-A, and, less effectively, to SilCRSAG-B released by other Anginosus group members, to produce putative bacteriocins and inhibit the growth of a sensitive strain of S. constellatus.
Elliptic operator for shape analysis

Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator. In this paper, we propose to adapt a classical operator from quantum mechanics to the field of shape analysis where we suggest to integrate a scalar function through a unified elliptical Hamiltonian operator. We study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. Then, we evaluate the resulting spectral basis for different applications such as mesh compression and shape matching. The suggested operator is shown to produce better functional spaces to operate with, as demonstrated by the proposed framework that outperforms existing spectral methods, for example, when applied to shape matching benchmarks.
ScreenerNet: Learning Curriculum for Neural Networks

We propose to learn a curriculum or a syllabus for supervised learning with deep neural networks. Specifically, we learn weights for each sample in training by an attached neural network, called ScreenerNet, to the original network and jointly train them in an end-to-end fashion. We show the networks augmented with our ScreenerNet achieve early convergence with better accuracy than the state-of-the-art rule-based curricular learning methods in extensive experiments using three popular vision datasets including MNIST, CIFAR10 and Pascal VOC2012, and a Cartpole task using Deep Q-learning.
Anonymous Identities for Permissioned Blockchains ( DRAFT v 05 – 1 / 20 / 2016 )

In this paper we address the issue of retaining user anonymity within a permissioned blockchain. We present the ChainAnchor architecture that adds an identity and privacy-preserving layer above the blockchain, either the private blockchain or the public Blockchain in Bitcoin. ChainAnchor adds an anonymous identity verification step such that anyone can read and verify transactions from the blockchain but only anonymous verified identities can have transactions processed. We refer to such blockchains as semi-permissioned blockchains. ChainAnchor builds upon and makes use of the zero knowledge proof mechanisms of the EPID scheme, which has the advantage of an optional cryptographic binding to a TPM tamper-resistant hardware. The use of tamper-resistant hardware provides a significant increase in security, not only for identity-related information but also for the protection of keys used by Bitcoin wallet applications.
A Survey of Research on Cloud Robotics and Automation

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data; 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning; 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes; and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to: a) datasets, publications, models, benchmarks, and simulation tools; b) open competitions for designs and systems; and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: http://goldberg.berkeley.edu/cloud-robotics/.
The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage

Most theories of choice assume that decisions derive from an assessment of the future outcomes of various options and alternatives through some type of cost-benefit analyses. The influence of emotions on decision-making is largely ignored. The studies of decision-making in neurological patients who can no longer process emotional information normally suggest that people make judgments not only by evaluating the consequences and their probability of occurring, but also and even sometimes primarily at a gut or emotional level. Lesions of the ventromedial (which includes the orbitofrontal) sector of the prefrontal cortex interfere with the normal processing of "somatic" or emotional signals, while sparing most basic cognitive functions. Such damage leads to impairments in the decision-making process, which seriously compromise the quality of decisions in daily life. The aim of this paper is to review evidence in support of "The Somatic Marker Hypothesis," which provides a systems-level neuroanatomical and cognitive framework for decision-making and suggests that the process of decision-making depends in many important ways on neural substrates that regulate homeostasis, emotion, and feeling. The implications of this theoretical framework for the normal and abnormal development of the orbitofrontal cortex are also discussed.
Learning Rapid-Temporal Adaptations

A hallmark of human intelligence and cognition is its flexibility. One of the longstanding goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation – the ability to adapt quickly to new environments or tasks – that we call adaptive neurons. Adaptive neurons modify their activations with task-specific values retrieved from a working memory. On standard metalearning and few-shot learning benchmarks in both vision and language domains, models augmented with adaptive neurons achieve state-of-the-art results.
CNN-RNN: A Unified Framework for Multi-label Image Classification

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, we utilize recurrent neural networks (RNNs) to address this problem. Combined with CNNs, the proposed CNN-RNN framework learns a joint image-label embedding to characterize the semantic label dependency as well as the image-label relevance, and it can be trained end-to-end from scratch to integrate both information in a unified framework. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than the state-of-the-art multi-label classification models.
Compact Hash Tables for High-Performance Traffic Classification on Multi-core Processors

Traffic classification is one of the kernel applications in network management. Many Machine Learning (ML) traffic classification algorithms are based on decision-trees. While most of the existing implementations of decision-trees are hardware-based, a new trend in network applications is to use software-based solutions. The decision-tree used for traffic classification is highly unbalanced; it is challenging to achieve high performance on multi-core platforms. In this paper, we present a high-throughput and low-latency traffic classification engine on multi-core platforms. We convert the decision-tree used in the C4.5 algorithm into multiple compact tables. All the compact tables are searched in parallel; efficient hashing techniques are employed to reduce the processing latency. The outcomes from all the tables are merged into the final classification result. High throughput can be sustained even if we scale up (1) the number of concurrent traffic classifiers, (2) the number of decision-tree leaves and (3) the number of features examined during the classification process. We prototype our design on state-of-the-art AMD and Intel multi-core platforms. For a typical C4.5 decision-tree consisting of 92 leaf nodes and 7 flow-level features, we achieve 134.15 Million Lookups Per Second (MLPS) throughput and 238.53 ns processing latency per lookup. Even for highly unbalanced decision-trees or large decision-trees consisting of up to 2 K leaf nodes, our traffic classification engine sustains high throughput and low latency without sacrificing classification accuracy (≥ 98.15%). We achieve 2.7× throughput compared with the classic C4.5 decision-tree-based implementations, and at least 12× speed-up compared with the existing traffic classifiers on multi-core platforms.
Rudiments of rough sets

Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough setbased research directions and applications.
State of the Art in Example-based Texture Synthesis

Recent years have witnessed significant progress in example-based texture synthesis algorithms. Given an example texture, these methods produce a larger texture that is tailored to the user’s needs. In this state-of-the-art report, we aim to achieve three goals: (1) provide a tutorial that is easy to follow for readers who are not already familiar with the subject, (2) make a comprehensive survey and comparisons of different methods, and (3) sketch a vision for future work that can help motivate and guide readers that are interested in texture synthesis research. We cover fundamental algorithms as well as extensions and applications of texture synthesis.
The Perceptual Organization of Texture Flow: A Contextual Inference Approach

Locally parallel dense patterns—sometimes called texture flows—define a perceptually coherent structure of particular significance to perceptual organization. We argue that with applications ranging from image segmentation and edge classification to shading analysis and shape interpretation, texture flows deserve attention equal to edge segment grouping and curve completion. This paper develops the notion of texture flow from a geometrical point of view to argue that local measurements of such structures must incorporate two curvatures. We show how basic theoretical considerations lead to a unique model for the local behavior of the flow and to a notion of texture flow “good continuation.” This, in turn, translates to a specification of consistency constraints between nearby flow measurements which we use for the computation of globally (piecewise) coherent structure through the contextual framework of relaxation labeling. We demonstrate the results on synthetic and natural images.
Case Reports of Adipose-derived Stem Cell Therapy for Nasal Skin Necrosis after Filler Injection

With the gradual increase of cases using fillers, cases of patients treated by non-medical professionals or inexperienced physicians resulting in complications are also increasing. We herein report 2 patients who experienced acute complications after receiving filler injections and were successfully treated with adipose-derived stem cell (ADSCs) therapy. Case 1 was a 23-year-old female patient who received a filler (Restylane) injection in her forehead, glabella, and nose by a non-medical professional. The day after her injection, inflammation was observed with a 3×3 cm skin necrosis. Case 2 was a 30-year-old woman who received a filler injection of hyaluronic acid gel (Juvederm) on her nasal dorsum and tip at a private clinic. She developed erythema and swelling in the filler-injected area A solution containing ADSCs harvested from each patient's abdominal subcutaneous tissue was injected into the lesion at the subcutaneous and dermis levels. The wounds healed without additional treatment. With continuous follow-up, both patients experienced only fine linear scars 6 months postoperatively. By using adipose-derived stem cells, we successfully treated the acute complications of skin necrosis after the filler injection, resulting in much less scarring, and more satisfactory results were achieved not only in wound healing, but also in esthetics.
BTCSpark : Scalable Analysis of the Bitcoin Blockchain using Spark

There is a large demand in the Bitcoin research ecosystem for high quality, scalable analytic software. Analysis can help developers quantify the risks and benefits of modifications to the Bitcoin protocol, as well as monitor for errant behavior. Historians might use Blockchain analysis to understand how various events impacted on chain activity. Corporations can use analysis to understand their customers better. To quote Madars Virza, Co-Inventor of Zerocash and researcher at MIT CSAIL, “I need to quickly prototype ideas for my research, but parsing the Blockchain for each project is an arduous task, so I’m forced to speculate. The research world is in great need for programmable Blockchain analysis tools.” In order to serve this need, I have developed BTCSpark for my Large Scale Systems (6.S897) final project under Matei Zaharia. BTCSpark is a layer on top of Apache Spark for analyzing the Bitcoin Blockchain. It provides an easy to use, flexible, and good performance environment for researchers and developers to query the Blockchain and to build Blockchain analysis tools. BTCSpark is open source software, in contrast to almost all other user-friendly Blockchain analysis tools available today. BTCSpark can be said to be a forward thinking project. Currently, the Bitcoin Blockchain is 50 GB – certainly feasible to fit it all into memory. However, as the Blockchain grows, and as users of the Blockchain call for it to grow at a faster rate, there will be more and more data. Not only that, but the Blockchain data is in a minimal data format. Fully expanding all of the connections and richness of the data set can require much more memory, even when the underlying set is only 50GB (considering each transaction as a node with potentially
The Fog computing paradigm: Scenarios and security issues

Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.
The Assessment of Goal Commitment: A Measurement Model Meta-Analysis.

Goals are central to current treatments of work motivation, and goal commitment is a critical construct in understanding the relationship between goals and performance. Inconsistency in the measurement of goal commitment hindered early research in this area but the nine-item, self-report scale developed by Hollenbeck, Williams, and Klein (1989b), and derivatives of that scale, have become the most commonly used measures of goal commitment. Despite this convergence, a few authors, based on small sample studies, have raised questions about the dimensionality of this measure. To address the conflicting recommendations in the literature regarding what items to use in assessing goal commitment, the current study combines the results of 17 independent samples and 2918 subjects to provide a more conclusive assessment by combining meta-analytic and multisample confirmatory factor analytic techniques. This effort reflects the first combined use of these techniques to test a measurement model and allowed for the creation of a database substantially larger than that of previously factor analyzed samples containing these scale items. By mitigating sampling error, the results clarified a number of debated issues that have arisen out of previous small sample factor analyses and revealed a five-item scale that is unidimensional and equivalent across measurement timing, goal origin, and task complexity. It is recommended that this five-item scale be used in future research assessing goal commitment.
Detection of buried objects in FLIR imaging using mathematical morphology and SVM

In this paper we describe a method for detecting buried objects of interest using a forward looking infrared camera (FLIR) installed on a moving vehicle. Infrared (IR) detection of buried targets is based on the thermal gradient between the object and the surrounding soil. The processing of FILR images consists in a spot-finding procedure that includes edge detection, opening and closing. Each spot is then described using texture features such as histogram of gradients (HOG) and local binary patterns (LBP) and assigned a target confidence using a support vector machine (SVM) classifier. Next, each spot together with its confidence is projected and summed in the UTM space. To validate our approach, we present results obtained on 6 one mile long runs recorded with a long wave IR (LWIR) camera installed on a moving vehicle.