nils-herrmann/clean_scirepeval huggingface dataset:
The Molecular Architecture of Odor and Pheromone Sensing in Mammals

exposure of a female to a male that is genetically different from the inseminating male (the Bruce Effect). In the Boston, Massachusetts 02115 Bruce Effect, it appears that a pheromone effect can be coupled with the detection of " individuality cues " that result from genetic variation within a species, including Olfaction is an ancient sense. Its precursors can be differences at major histocompatibility loci. found in the most primitive single-celled organisms, reflecting the need of every organism to sense its chemical milieu. In mammals, the olfactory system can detect and Odor and Pheromone Circuits distinguish a vast number of volatile chemicals with a In mammals, odorants are detected in the olfactory epi-large variety of structures (Beets, 1970; Shepherd, 1988). thelium (OE) that lines the nasal cavity (Shepherd, 1988) It seems to have evolved to sense almost any volatile . Signals generated in olfactory sensory neu-chemical it might encounter. However, the olfactory sys-rons in the OE in response to odorants are relayed tem is also responsible for the sensing of pheromones, through the main olfactory bulb (MOB) to the olfactory chemicals released by animals that act on conspecifics cortex (OC) and then to other brain areas. Via these to regulate populations of animals and their social inter-pathways, odor signals ultimately reach higher cortical actions (Wilson, 1963; Shepherd, 1988). Pheromones areas involved in the conscious perception of odors, as elicit programmed neuroendocrine changes and innate well as limbic areas, such as the amygdala and hypothal-behaviors, suggesting a need for a very precise recogni-amus, that are involved in emotional and motivational tion process. responses. How does the olfactory system meet these dual re-Most mammals have a second olfactory sense organ, quirements for general odor sensing and the generation called the vomeronasal organ (VNO) . The VNO of stereotyped responses to specific pheromones? is a tubular structure in the nasal septum that is con-Studies in both insects and mammals suggest that it nected to the nasal cavity by a small duct. Removal of does it by segregating odor and pheromone detection the VNO, or severing its connection to the brain, inter-in different sensory neurons and in different neural path-feres with pheromone effects but not with general odor, these observations have sug-functional studies of these pathways have provided in-gested that the VNO may be specialized to detect phero-sight into many aspects of odor and pheromone sensing In recent years, the there, signals …
PDF folding for stochastic flash ADCs

A circuit is presented that halves the number of comparators required for a two-group stochastic flash ADC. In a two-group stochastic flash ADC, two Gaussian PDFs are shifted left and right by applying a global offset reference to the two groups of comparators. This creates a virtual uniform distribution of comparator offset between the two global offsets. Over half of the comparators fall outside of the virtual uniform distribution and are not used. The proposed circuit inverts the polarity of each comparator offset until it is within the useful range, creating a virtual uniform distribution of comparator offsets with few comparators wasted.
Preliminary Guidelines for Empirical Research in Software Engineering

Empirical software engineering research needs research guidelines to improve the research and reporting processes. We propose a preliminary set of research guidelines aimed at stimulating discussion among software researchers. They are based on a review of research guidelines developed for medical researchers and on our own experience in doing and reviewing software engineering research. The guidelines are intended to assist researchers, reviewers and meta-analysts in designing, conducting and evaluating empirical studies. Editorial boards of software engineering journals may wish to use our recommendations as a basis for developing guidelines for reviewers and for framing policies for dealing with the design, data collection and analysis and reporting of empirical studies.
Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing

Biomedical research and clinical practice are entering a data-driven era. One of the major applications of biomedical big data research is to utilize inexpensive and unobtrusive mobile biomedical sensors and cloud computing for pervasive health monitoring. However, real-world user experiences with mobile cloud-based health monitoring were poor, due to the factors such as excessive networking latency and longer response time. On the other hand, fog computing, a newly proposed computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, storage, communication, and etc. This new computing paradigm, if successfully applied for pervasive health monitoring, has great potential to accelerate the discovery of early predictors and novel biomarkers to support smart care decision making in a connected health scenarios. In this paper, we employ a real-world pervasive health monitoring application (pervasive fall detection for stroke mitigation) to demonstrate the effectiveness and efficacy of fog computing paradigm in health monitoring. Fall is a major source of morbidity and mortality among stroke patients. Hence, detecting falls automatically and in a timely manner becomes crucial for stroke mitigation in daily life. In this paper, we set to (1) investigate and develop new fall detection algorithms and (2) design and employ a real-time fall detection system employing fog computing paradigm (e.g., distributed analytics and edge intelligence), which split the detection task between the edge devices (e.g., smartphones attached to the user) and the server (e.g., servers in the cloud). Experimental results show that distributed analytics and edge intelligence, supported by fog computing paradigm, are very promising solutions for pervasive health monitoring.
Narcolepsy in orexin Knockout Mice Molecular Genetics of Sleep Regulation

Neurons containing the neuropeptide orexin (hypocretin) are located exclusively in the lateral hypothalamus and send axons to numerous regions throughout the central nervous system, including the major nuclei implicated in sleep regulation. Here, we report that, by behavioral and electroencephalographic criteria, orexin knockout mice exhibit a phenotype strikingly similar to human narcolepsy patients, as well as canarc-1 mutant dogs, the only known monogenic model of narcolepsy. Moreover, modafinil, an anti-narcoleptic drug with ill-defined mechanisms of action, activates orexin-containing neurons. We propose that orexin regulates sleep/wakefulness states, and that orexin knockout mice are a model of human narcolepsy, a disorder characterized primarily by rapid eye movement (REM) sleep dysregulation.
Crowdfunding: Motivations and deterrents for participation

Crowdfunding is changing how, why, and which ideas are brought into existence. With the increasing number of crowdfunded projects, it is important to understand what drives people to either create or fund these projects. To shed light on this new social phenomenon, we present a grounded theory of motivation informed by the first cross-platform qualitative study of the crowdfunding community. By performing 83 semistructured interviews, we uncover creator motivations, which include the desire to raise funds, expand awareness of work, connect with others, gain approval, maintain control, and learn; and supporter motivations, which include the desire to collect rewards, help others, support causes, and be part of a community. We also explore deterrents to crowdfunding participation, including, among creators, fear of failure, and, for supporters, lack of trust. Based on these findings, we provide three emergent design principles to inform the design of effective crowdfunding platforms and support tools.
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

Anomaly detection (AD) task corresponds to identifying the true anomalies from a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies, which are then analyzed by a human to discover the true anomalies. However, this process can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered. We make four main contributions towards this goal. First, we present an important insight that explains the practical successes of AD ensembles and how ensembles are naturally suited for active learning. This insight also allows us to relate the greedy query selection strategy to uncertainty sampling, with implications for label-efficient learning. Second, we present several algorithms for active learning with tree-based AD ensembles. A novel formalism called compact description (CD) is developed to describe the discovered anomalies. We propose algorithms based on the CD formalism to improve the diversity of discovered anomalies and to generate rule sets for improved interpretability of anomalous instances. To handle streaming data setting, we present a novel data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the detector in a principled manner. Third, we present a novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning with generic AD ensembles and an approach to generate succinct explanations from the resulting models. GLAD allows end-users to retain the use of simple and understandable global anomaly detectors by automatically learning their local relevance to specific data instances using label feedback. Fourth, we present extensive experiments to evaluate our insights and algorithms with tree-based AD ensembles in both batch and streaming settings. Our results show that in addition to discovering significantly more anomalies than state-of-the-art unsupervised baselines, our active learning algorithms under the streaming-data setup are competitive with the batch setup. Experiments using GLAD show its effectiveness in learning the local relevance of ensemble members to discover more anomalies when compared to baseline methods.
Reliable Beamspace Channel Estimation for Millimeter-Wave Massive MIMO Systems with Lens Antenna Array

Millimeter-wave (mm-wave) massive MIMO with lens antenna array can considerably reduce the number of required radio-frequency (RF) chains by beam selection. However, beam selection requires the base station to acquire the accurate information of beamspace channel. This is a challenging task as the size of beamspace channel is large, while the number of RF chains is limited. In this paper, we investigate the beamspace channel estimation problem in mm-wave massive MIMO systems with lens antenna array. Specifically, we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beamspace channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beamspace channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beamspace channel can be exploited.
Survey of multi-objective optimization methods for engineering

Asurveyofcurrentcontinuousnonlinearmultiobjective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software.
Projective dynamics: fusing constraint projections for fast simulation

We present a new method for implicit time integration of physical systems. Our approach builds a bridge between nodal Finite Element methods and Position Based Dynamics, leading to a simple, efficient, robust, yet accurate solver that supports many different types of constraints. We propose specially designed energy potentials that can be solved efficiently using an alternating optimization approach. Inspired by continuum mechanics, we derive a set of continuum-based potentials that can be efficiently incorporated within our solver. We demonstrate the generality and robustness of our approach in many different applications ranging from the simulation of solids, cloths, and shells, to example-based simulation. Comparisons to Newton-based and Position Based Dynamics solvers highlight the benefits of our formulation.
Max-Margin Markov Networks

In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees. In this paper, we present a new framework that combines the advantages of both approaches: Maximum margin Markov (M ) networks incorporate both kernels, which efficiently deal with highdimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm for learning M networks based on a compact quadratic program formulation. We provide a new theoretical bound for generalization in structured domains. Experiments on the task of handwritten character recognition, demonstrate very significant gains over previous approaches.
Parallel and distributed simulation

Research and development efforts in the parallel and distributed simulation field over the last 15 years has progressed, largely independently, in two separate camps: the largely academic high performance parallel and distributed (discrete event) simulation (PADS) community, and the DoD-centered distributed interactive simulation (DIS) community. This tutorial gives an overview and comparison of work in these two areas, emphasizing issues related to distributed execution where these fields have the most overlap. Differences in the fundamental assumptions routinely used within each community are contrasted, followed by overviews of work in each community.
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding

The encoder-decoder framework has achieved promising progress for many sequence generation tasks, including machine translation, text summarization, dialog system, image captioning, etc. Such a framework adopts an one-pass forward process while decoding and generating a sequence, but lacks the deliberation process: A generated sequence is directly used as final output without further polishing. However, deliberation is a common behavior in human’s daily life like reading news and writing papers/articles/books. In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation. A deliberation network has two levels of decoders, where the first-pass decoder generates a raw sequence and the second-pass decoder polishes and refines the raw sentence with deliberation. Since the second-pass deliberation decoder has global information about what the sequence to be generated might be, it has the potential to generate a better sequence by looking into future words in the raw sentence. Experiments on neural machine translation and text summarization demonstrate the effectiveness of the proposed deliberation networks. On the WMT 2014 English-to-French translation task, our model establishes a new state-of-the-art BLEU score of 41.5.
Algorithmic improvements for fast concurrent Cuckoo hashing

Fast concurrent hash tables are an increasingly important building block as we scale systems to greater numbers of cores and threads. This paper presents the design, implementation, and evaluation of a high-throughput and memory-efficient concurrent hash table that supports multiple readers and writers. The design arises from careful attention to systems-level optimizations such as minimizing critical section length and reducing interprocessor coherence traffic through algorithm re-engineering. As part of the architectural basis for this engineering, we include a discussion of our experience and results adopting Intel's recent hardware transactional memory (HTM) support to this critical building block. We find that naively allowing concurrent access using a coarse-grained lock on existing data structures reduces overall performance with more threads. While HTM mitigates this slowdown somewhat, it does not eliminate it. Algorithmic optimizations that benefit both HTM and designs for fine-grained locking are needed to achieve high performance.
 Our performance results demonstrate that our new hash table design---based around optimistic cuckoo hashing---outperforms other optimized concurrent hash tables by up to 2.5x for write-heavy workloads, even while using substantially less memory for small key-value items. On a 16-core machine, our hash table executes almost 40 million insert and more than 70 million lookup operations per second.
Algorithm selection by rational metareasoning as a model of human strategy selection

Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People’s choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people select between cognitive strategies and translating the results into better solutions to the algorithm selection problem.
Deep Temporal Models using Identity Skip-Connections for Speech Emotion Recognition

Deep architectures using identity skip-connections have demonstrated groundbreaking performance in the field of image classification. Recently, empirical studies suggested that identity skip-connections enable ensemble-like behaviour of shallow networks, and that depth is not a solo ingredient for their success. Therefore, we examine the potential of identity skip-connections for the task of Speech Emotion Recognition (SER) where moderately deep temporal architectures are often employed. To this end, we propose a novel architecture which regulates unimpeded feature flows and captures long-term dependencies via gate-based skip-connections and a memory mechanism. Our proposed architecture is compared to other state-of-the-art methods of SER and is evaluated on large aggregated corpora recorded in different contexts. Our proposed architecture outperforms the state-of-the-art methods by 9 - 15% and achieves an Unweighted Accuracy of 80.5% in an imbalanced class distribution. In addition, we examine a variant adopting simplified skip-connections of Residual Networks (ResNet) and show that gate-based skip-connections are more effective than simplified skip-connections.
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory

Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.
The Cognitive Process of Decision Making

Decision making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of actions is chosen from among a set of alternatives based on certain criteria. Decision theories are widely applied in many disciplines encompassing cognitive informatics, computer science, management science, economics, sociology, psychology, political science, and statistics. A number of decision strategies have been proposed from different angles and application domains such as the maximum expected utility and Bayesian method. However, there is still a lack of a fundamental and mathematical decision model and a rigorous cognitive process for decision making. This article presents a fundamental cognitive decision making process and its mathematical model, which is described as a sequence of Cartesian-product based selections. A rigorous description of the decision process in real-time process algebra (RTPA) is provided. Real-world decisions are perceived as a repetitive application of the fundamental cognitive process. The result shows that all categories of decision strategies fit in the formally described decision process. The cognitive process of decision making may be applied in a wide range of decision-based systems such as cognitive informatics, software agent systems, expert systems, and decision support systems.