nils-herrmann/clean_scirepeval huggingface dataset:
Transmit Subaperturing for MIMO Radars With Co-Located Antennas

We present a transmit subaperturing (TS) approach for multiple-input multiple-output (MIMO) radars with co-located antennas. The proposed scheme divides the transmit array elements into multiple groups, each group forms a directional beam and modulates a distinct waveform, and all beams are steerable and point to the same direction. The resulting system is referred to as a TS-MIMO radar. A TS-MIMO radar is a tunable system that offers a continuum of operating modes from the phased-array radar, which achieves the maximum directional gain but the least interference rejection ability, to the omnidirectional transmission based MIMO radar, which can handle the largest number of interference sources but offers no directional gain. Tuning of the TS-MIMO system can be easily made by changing the configuration of the transmit subapertures, which provides a direct tradeoff between the directional gain and interference rejection power of the system. The performance of the TS-MIMO radar is examined in terms of the output signal-to-interference-plus-noise ratio (SINR) of an adaptive beamformer in an interference and training limited environment, where we show analytically how the output SINR is affected by several key design parameters, including the size/number of the subapertures and the number of training signals. Our results are verified by computer simulation and comparisons are made among various operating modes of the proposed TS-MIMO system.
Variable selection with neural networks

In this paper, we present 3 different neural network-based methods to perform variable selection. OCD Optimal Cell Damage is a pruning method, which evaluates the usefulness of a variable and prunes the least useful ones (it is related to the Optimal Brain Damage method of J_.e Cun et al.). Regularization theory proposes to constrain estimators by adding a term to the cost function used to train a neural network. In the Bayesian framework, this additional term can be interpreted as the log prior to the weights distribution. We propose to use two priors (a Gaussian and a Gaussian mixture) and show that this regularization approach allows to select efficient subsets of variables. Our methods are compared to conventional statistical selection procedures and are shown to significantly improve on that.
Modeling Mention, Context and Entity with Neural Networks for Entity Disambiguation

Given a query consisting of a mention (name string) and a background document, entity disambiguation calls for linking the mention to an entity from reference knowledge base like Wikipedia. Existing studies typically use hand-crafted features to represent mention, context and entity, which is laborintensive and weak to discover explanatory factors of data. In this paper, we address this problem by presenting a new neural network approach. The model takes consideration of the semantic representations of mention, context and entity, encodes them in continuous vector space and effectively leverages them for entity disambiguation. Specifically, we model variable-sized contexts with convolutional neural network, and embed the positions of context words to factor in the distance between context word and mention. Furthermore, we employ neural tensor network to model the semantic interactions between context and mention. We conduct experiments for entity disambiguation on two benchmark datasets from TAC-KBP 2009 and 2010. Experimental results show that our method yields state-of-the-art performances on both datasets.
Explicit and implicit stereotype activation effects on memory: do age and awareness moderate the impact of priming?

Two studies examined the effects of implicit and explicit priming of aging stereotypes. Implicit primes had a significant effect on older adults' memory, with positive primes associated with greater recall than negative primes. With explicit primes, older adults were able to counteract the impact of negative stereotypes when the cues were relatively subtle, but blatant stereotype primes suppressed performance regardless of prime type. No priming effects under either presentation condition were obtained for younger adults, indicating that the observed implicit effects are specific to those for whom the stereotype is self-relevant. Findings emphasize the importance of social-situational factors in determining older adults' memory performance and contribute to the delineation of situations under which stereotypes are most influential.
Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology.

Research literature on problematic smartphone use, or smartphone addiction, has proliferated. However, relationships with existing categories of psychopathology are not well defined. We discuss the concept of problematic smartphone use, including possible causal pathways to such use.



We conducted a systematic review of the relationship between problematic use with psychopathology. Using scholarly bibliographic databases, we screened 117 total citations, resulting in 23 peer-reviewer papers examining statistical relations between standardized measures of problematic smartphone use/use severity and the severity of psychopathology.



Most papers examined problematic use in relation to depression, anxiety, chronic stress and/or low self-esteem. Across this literature, without statistically adjusting for other relevant variables, depression severity was consistently related to problematic smartphone use, demonstrating at least medium effect sizes. Anxiety was also consistently related to problem use, but with small effect sizes. Stress was somewhat consistently related, with small to medium effects. Self-esteem was inconsistently related, with small to medium effects when found. Statistically adjusting for other relevant variables yielded similar but somewhat smaller effects.



We only included correlational studies in our systematic review, but address the few relevant experimental studies also.



We discuss causal explanations for relationships between problem smartphone use and psychopathology.
Integrating Memories in the Human Brain: Hippocampal-Midbrain Encoding of Overlapping Events

Decisions are often guided by generalizing from past experiences. Fundamental questions remain regarding the cognitive and neural mechanisms by which generalization takes place. Prior data suggest that generalization may stem from inference-based processes at the time of generalization. By contrast, generalization may emerge from mnemonic processes occurring while premise events are encoded. Here, participants engaged in a two-phase learning and generalization task, wherein they learned a series of overlapping associations and subsequently generalized what they learned to novel stimulus combinations. Functional MRI revealed that successful generalization was associated with coupled changes in learning-phase activity in the hippocampus and midbrain (ventral tegmental area/substantia nigra). These findings provide evidence for generalization based on integrative encoding, whereby overlapping past events are integrated into a linked mnemonic representation. Hippocampal-midbrain interactions support the dynamic integration of experiences, providing a powerful mechanism for building a rich associative history that extends beyond individual events.
ASSESSING TRENDS OVER TIME IN PERFORMANCE , COSTS , AND ENERGY USE FOR SERVERS

Data centers are the heart of the global economy. In the mid-1990s, the costs of these large computing facilities were dominated by the costs of the information technology (IT) equipment that they housed, but no longer. As the electrical power used by IT equipment per dollar of equipment cost has increased, the annualized facility costs associated with powering and cooling IT equipment has in some cases grown to equal the annualized capital costs of the IT equipment itself. The trend towards ever more electricity-intensive IT equipment continues, which means that direct IT equipment acquisition costs will be a less important determinant of the economics of computing services in the future. Consider Figure ES-1, which shows the importance of different data center cost components as a function of power use per thousand dollars of server cost. If power per server cost continues to increase, the indirect power-related infrastructure costs will soon exceed the annualized direct cost of purchasing the IT equipment in the data center. Ken Brill of the Uptime Institute has called these trends " the economic breakdown of Moore's Law " , highlighting the growing importance of power-related indirect costs to the overall economics of information technology. The industry has in general assumed that the cost reductions and growth in computing speed related to Moore's law would continue unabated for years to come, and this may be true at the level of individual server systems. Unfortunately, far too little attention has been paid to the true total costs for data center facilities, in which the power-related indirect costs threaten to slow the cost reductions from Moore's law. These trends have important implications for the design, construction and operation of data centers. The companies delivering so-called " cloud computing " services have been aware of these economic trends for years, though the sophistication of their responses to them has varied. Most other companies that own data centers, for which computing is not their core business, have significantly lagged behind the vertically organized large-scale computing providers in addressing these issues. There are technical solutions for improving data center efficiency but the most important and most neglected solutions relate to institutional changes that can help companies focus on reducing the total costs of computing services. The first steps, of course, are to measure costs in a comprehensive way, eliminate institutional impediments, and reward those who successfully reduce these costs. This article assesses …
Intention-Based EMG Control for Powered Exoskeletons

Electromyographical (EMG) signals have been frequently used to estimate human muscular torques. In the field of human-assistive robotics, these methods provide valuable information to provide effectively support to the user. However, their usability is strongly limited by the necessity of complex user-dependent and session-dependent calibration procedures, which confine their use to the laboratory environment. Nonetheless, an accurate estimate of muscle torque could be unnecessary to provide effective movement assistance to users. The natural ability of human central nervous system of adapting to external disturbances could compensate for a lower accuracy of the torque provided by the robot and maintain the movement accuracy unaltered, while the effort is reduced. In order to explore this possibility, in this paper we study the reaction of ten healthy subjects to the assistance provided through a proportional EMG control applied by an elbow powered exoskeleton. This system gives only a rough estimate of the user muscular torque but does not require any specific calibration. Experimental results clearly show that subjects adapt almost instantaneously to the assistance provided by the robot and can reduce their effort while keeping full control of the movement under different dynamic conditions (i.e., no alterations of movement accuracy are observed).
Signalling Control Table Generation and Verification

Queenland Rail (QR) and the Software Verification Research Centre (SVRC) from The University of Queensland are investigating a suite of tools to assist in the production of signalling control tables. Altogether there are four tools, a graphical track layout editor, a tool to generate control tables automatically, a tool to enable manual editing of tables, and a verifier to show that tables satisfy signalling safety principles. This paper provides an overview of the toolset. It gives a fuller description of two of the key parts of the toolset design: the algorithms to generate control table entries and the formalisation of signalling safety principles for verification.
Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks

Abslract Bayesian probabilily theory provides a unifying framework for dara modelling. In this framework the overall aims are to find models that are well-matched to, the &a, and to use &se models to make optimal predictions. Neural network laming is interpreted as an inference of the most probable parameters for Ihe model, given the training data The search in model space (i.e., the space of architectures, noise models, preprocessings, regularizes and weight decay constants) can then also be treated as an inference problem, in which we infer the relative probability of alternative models, given the data. This review describes practical techniques based on G ~ ~ s s ~ M approximations for implementation of these powerful methods for controlling, comparing and using adaptive network$.
ShouldeRO, an alignment-free two-DOF rehabilitation robot for the shoulder complex

This paper presents a robot aimed to assist the shoulder movements of stroke patients during their rehabilitation process. This robot has the general form of an exoskeleton, but is characterized by an action principle on the patient no longer requiring a tedious and accurate alignment of the robot and patient's joints. It is constituted of a poly-articulated structure whose actuation is deported and transmission is ensured by Bowden cables. It manages two of the three rotational degrees of freedom (DOFs) of the shoulder. Quite light and compact, its proximal end can be rigidly fixed to the patient's back on a rucksack structure. As for its distal end, it is connected to the arm through passive joints and a splint guaranteeing the robot action principle, i.e. exert a force perpendicular to the patient's arm, whatever its configuration. This paper also presents a first prototype of this robot and some experimental results such as the arm angular excursions reached with the robot in the three joint planes.
Origami tubes assembled into stiff, yet reconfigurable structures and metamaterials.

Thin sheets have long been known to experience an increase in stiffness when they are bent, buckled, or assembled into smaller interlocking structures. We introduce a unique orientation for coupling rigidly foldable origami tubes in a "zipper" fashion that substantially increases the system stiffness and permits only one flexible deformation mode through which the structure can deploy. The flexible deployment of the tubular structures is permitted by localized bending of the origami along prescribed fold lines. All other deformation modes, such as global bending and twisting of the structural system, are substantially stiffer because the tubular assemblages are overconstrained and the thin sheets become engaged in tension and compression. The zipper-coupled tubes yield an unusually large eigenvalue bandgap that represents the unique difference in stiffness between deformation modes. Furthermore, we couple compatible origami tubes into a variety of cellular assemblages that can enhance mechanical characteristics and geometric versatility, leading to a potential design paradigm for structures and metamaterials that can be deployed, stiffened, and tuned. The enhanced mechanical properties, versatility, and adaptivity of these thin sheet systems can provide practical solutions of varying geometric scales in science and engineering.
Asset-Management of Transformers Based on Condition Monitoring and Standard Diagnosis [Feature Article]

In this paper, a methodology is developed to use data acquisition derived from condition monitoring and standard diagnosis for rehabilitation purposes of transformers. The interpretation and understanding of the test data are obtained from international test standards to determine the current condition of transformers. In an attempt to ascertain monitoring priorities, the effective test methods are selected for transformer diagnosis. In particular, the standardization of diagnostic and analytical techniques are being improved that will enable field personnel to more easily use the test results and will reduce the need for interpretation by experts. In addition, the advanced method has the potential to reduce the time greatly and increase the accuracy of diagnostics. The important aim of the standardization is to develop the multiple diagnostic models that combine results from the different tests and give an overall assessment of reliability and maintenance for transformers.
Blinkering surveillance: Enabling video privacy through computer vision

In this paper we describe a technology for protecting privacy in video systems. The paper presents a review of privacy in video surveillance and describes how a computer vision approach to understanding the video can be used to represent “just enough” of the information contained in a video stream to allow video-based tasks (including both surveillance and other “person aware” applications) to be accomplished, while hiding superfluous details, particularly identity, that can contain privacyintrusive information. The technology has been implemented in the form of a privacy console that manages operator access to different versions of the video-derived data according to access control lists. We have also built PrivacyCam—a smart camera that produces a video stream with the privacy-intrusive information already removed.
Scalability Challenges in Web Search Engines

Continuous growth of the Web and user bases forces web search engine companies to make costly investments on very large compute infrastructures. The scalability of these infrastructures require careful performance optimizations in every major component of the search engine. Herein, we try to provide a fairly comprehensive coverage of literature on scalability challenges in large-scale web search engines. We present the identified challenges through an architectural classification, starting from a simple single-node search system and moving towards a hypothetical multi-site web search architecture. We also discuss a number of open research problems and provide recommendations to researchers in the field.
Robotic mapping: a survey

This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping. It describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems. The history of robotic mapping is also described, along with an extensive list of open research problems. This research is sponsored by by DARPA’s MARS Program (Contract number N66001-01-C-6018) and the National Science Foundation (CAREER grant number IIS-9876136 and regular grant number IIS-9877033), all of which is gratefully acknowledged. The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of the United States Government or any of the sponsoring institutions.
How Does the Valence of Online Consumer Reviews Matter in Consumer Decision Making? Differences between Search Goods and Experience Goods

Existing empirical studies have drawn inconsistent conclusions about the effect of electronic word-of-mouth valence on consumer decision making. Based on attribution theory and prospect theory, this study attempts to explain this discrepancy through exploring how product type moderates the impact of online consumer reviews valence. Our results from a 2 (Positive reviews vs. Negative reviews) 2 (Search goods vs. Experience goods) experiment design show that the effect of online consumer reviews valence is asymmetrically moderated by product type: The effect of positive reviews is greater for search goods than that for experience goods, whereas the effects of negative reviews have no significant difference between these two types of goods; And the impact difference between negative reviews and positive reviews is greater for experience goods than for search goods. Our study not only confirms the moderating role of product type, but also further explores how product type moderates the effect of reviews valence. We also provide implications for e-marketers.
SQL on Structurally-Encrypted Databases

We show how to encrypt a relational database in such a way that it can efficiently support a large class of SQL queries. Our construction is based solely on structured encryption and does not make use of any property-preserving encryption (PPE) schemes such as deterministic and order-preserving encryption. As such, our approach leaks considerably less than PPE-based solutions which have recently been shown to reveal a lot of information in certain settings (Naveed et al., CCS ’15 ). Our construction achieves asymptotically optimal query complexity under very natural conditions on the database and queries.
Reviewing Enterprise Content Management: a functional framework

22 October 2009  16 September 2011 Abstract Enterprise Content Management (ECM) focuses on managing all types of content being used in organizations. It is a convergence of previous approaches that focus on managing only particular types of content, as for example documents or web pages. In this paper, we present an overview of previous research by categorizing the existing literature. We show that scientific literature on ECM is limited and there is no consensus on the definition of ECM. Therefore, the literature review surfaced several ECM definitions that we merge into a more consistent and comprehensive definition of ECM. The Functional ECM Framework (FEF) provides an overview of the potential functionalities of ECM systems (ECMSs). We apply the FEF in three case studies. The FEF can serve to communicate about ECMSs, to understand them and to direct future research. It can also be the basis for a more formal reference architecture and it can be used as an assessment tool by practitioners for comparing the functionalities provided by existing ECMSs.  (2011) advance online publication, 25 October 2011; doi:10.1057/ejis.2011.41
YOLO3D: End-to-End Real-Time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

Object detection and classification in 3D is a key task in Automated Driving (AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding environment, while the task of 3D object bounding box detection in real time remains a strong algorithmic challenge. In this paper, we build on the success of the oneshot regression meta-architecture in the 2D perspective image space and extend it to generate oriented 3D object bounding boxes from LiDAR point cloud. Our main contribution is in extending the loss function of YOLO v2 to include the yaw angle, the 3D box center in Cartesian coordinates and the height of the box as a direct regression problem. This formulation enables real-time performance, which is essential for automated driving. Our results are showing promising figures on KITTI benchmark, achieving real-time performance (40 fps) on Titan X GPU.