nils-herrmann/clean_scirepeval huggingface dataset:
Psychiatric aspects of familicide.

Various types of killings occur within family matrices. The news media highlight the dramatic components, and even novels now use it as a theme. 1 However, a psychiatric understanding remains elusive. Not all killings within a family are familicidal. For want of a better term, I have called the killing of more than one member of a family by another family member "familicide." The destruction of the family unit appears to be the goal. Such behavior comes within the category of "mass murders" where a number of victims are killed in a short period of time by one person. However, in mass murders the victims are not exclusively family members. The case of one person committing a series of homicides over an extended period of time, such as months or years, also differs from familicide. The latter can result in the perpetrator getting killed or injured in the process, or subsequently attempting a suicidal act. However, neither injury, nor suicide, nor death of the perpetrator is an indispensable part of familicide. Fifteen different theories purport to explain physical violence within the nuclear family. 2 Varieties of killings within a family are subvarieties and familicide is yet a rarer event. Pedicide is the killing of a child by a parent. These are usually cases of one child being killed by one parent. If the child happens to be an infant, the act is infanticide. Many of the latter are situations where a mother kills her infant and is diagnosed schizophrenic or psychotic depressive. Child beating by a parent can result in inadvertent death. One sibling killing another is fratricide. A child killing a parent is parricide, or more specifically patricide or matricide. Uxoricide is one spouse killing another. Each of these behaviors has its own intrapsychic and interpersonal correlates. Such correlates often involve victimologic aspects. As a caveat, and based on this study, we should not assume that the perpetrators in familicide all bear one diagnosis even in a descriptive nosological sense. A distinction is needed between intra familial homicides related to psychiatric disturbance in one family member and collective types of violence in which families are destroyed. Extermination of families based on national, ethnic, racial or religious backgrounds are not
On-Road Motion Planning for Autonomous Vehicles

We present a motion planner for autonomous on-road driving, especially on highways. It adapts the idea of a on-road state lattice. A focused search is performed in the previously identified region in which the optimal trajectory is most likely to exist. The main contribution of this paper is a computationally efficient planner which handles dynamic environments generically. The Dynamic Programming algorithm is used to explore in spatiotemporal space and find a coarse trajectory solution first that encodes desirable maneuvers. Then a focused trajectory search is conducted using the ”generate-and-test” approach, and the best trajectory is selected based on the smoothness of the trajectory. Analysis shows that our scheme provides a principled way to focus trajectory sampling, thus greatly reduces the search space. Simulation results show robust performance in several challenging scenarios.
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text

The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features . Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text. The newly introduced text data is termed as YouTube-8M-Text. We present a classification framework for the joint use of text, visual and audio features, and conduct an extensive set of experiments to quantify the benefit that this additional mode brings. The inclusion of text yields state-of-the-art results, e.g. 86.7% GAP on the YouTube-8M-Text validation dataset.
Data Mining Application in Customer Relationship Management of Credit Card Business

First, we classify the selected customers into clusters using RFM model to identify high-profit, gold customers. Subsequently, we carry out data mining using association rules algorithm. We measure the similarity, difference and modified difference of mined association rules based on three rules, i.e. Emerging Patten Rule, Unexpected Change Rule, and Added/Perished Rule. In the meantime, we use rule matching threshold to derive all types of rules and explore the rules with significant change based on the degree of change measured. In this paper, we employ data mining tools and effectively discover the current spending pattern of customers and trends of behavioral change, which will allow management to detect in a large database potential changes of customer preference, and provide as early as possible products and services desired by the customers to expand the clientele base and prevent customer attrition.
Introspective classification for robot perception

In robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework which will make the best decisions, we should pay careful attention to the ways in which it generates scores. Precision and recall have been widely adopted as canonical metrics to quantify the performance of learning algorithms, but for robotics applications involving mission-critical decision making, good performance in relation to these metrics is insufficient. We introduce and motivate the importance of a classifier’s introspective capacity: the ability to associate an appropriate assessment of confidence with any test case. We propose that a key ingredient for introspection is a framework’s potential to increase its uncertainty with the distance between a test datum its training data. We compare the introspective capacities of a number of commonly used classification frameworks in both classification and detection tasks, and show that better introspection leads to improved decision-making in the context of tasks such as autonomous driving or semantic map generation.
Beyond Transaction Cost Economics: Towards an endogenous theory of Information Technology Outsourcing

In our review, we coded 73 empirical findings from 31 journal articles that applied Transaction Cost Economics (TCE) to study Information Technology Outsourcing (ITO). As Karimi-Alaghehband et al. (2011) note correctly, the empirical results of TCE tests in the context of ITO are mixed. We found that only 49% of the empirical ITO findings supported TCE logic. We found only slightly better support for TCE when it is used as a normative theory (54%) than when it is used as a predictive theory (47%). The main difference between Karimi-Alaghehband et al.’s (2011) contribution and our contribution to the debate focuses upon what to do next. Karimi-Alaghehband et al. (2011) argue that ITO researchers need to apply TCE more faithfully. We argue that we are asking too much of TCE—the ITO phenomenon is more complex than can be accommodated by TCE. We argue that ITO research has matured to the point that we should be building our own endogenous ITO theory. We offer observations and insights on what such a theory might entail. In moving ITO research forward, we first critique TCE assumptions and provide alternative assumptions that seem to fit ITO observations well. We draw on our review of 741 ITO empirical findings (Lacity et al., 2010) to provide a detailed theoretical framework to advance further study that suggests the most promising constructs to use in an endogenous ITO theory.
ModelHub : Lifecycle Management for Deep Learning

Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle contains a rich set of artifacts, such as learned parameters and training logs, and frequently conducted tasks, e.g., to understand the model behaviors and to try out new models. Dealing with such artifacts and tasks is cumbersome and left to the users. To address these issues in a comprehensive manner, we propose ModelHub, which includes a novel model versioning system (dlv); a domain specific language for searching through model space (DQL); and a hosted service (ModelHub) to store developed models, explore existing models, enumerate new models and share models with others.
A Context-Aware Routing Protocol on Internet of Things Based on Sea Computing Model

The paper syncretizes the fundamental concept of the Sea Computing model in Internet of Things and the routing protocol of the wireless sensor network, and proposes a new routing protocol CASCR (Context-Awareness in Sea Computing Routing Protocol) for Internet of Things, based on context-awareness which belongs to the key technologies of Internet of Things. Furthermore, the paper describes the details on the protocol in the work flow, data structure and quantitative algorithm and so on. Finally, the simulation is given to analyze the work performance of the protocol CASCR. Theoretical analysis and experiment verify that CASCR has higher energy efficient and longer lifetime than the congeneric protocols. The paper enriches the theoretical foundation and makes some contribution for wireless sensor network transiting to Internet of Things in this research phase.
A 480 mW 2.6 GS/s 10b Time-Interleaved ADC With 48.5 dB SNDR up to Nyquist in 65 nm CMOS

This paper presents a 64-times interleaved 2.6 GS/s 10b successive-approximation-register (SAR) ADC in 65 nm CMOS. The ADC combines interleaving hierarchy with an open-loop buffer array operated in feedforward-sampling and feedback-SAR mode. The sampling front-end consists of four interleaved T/Hs at 650 MS/s that are optimized for timing accuracy and sampling linearity, while the back-end consists of four ADC arrays, each consisting of 16 10b current-mode non-binary SAR ADCs. The interleaving hierarchy allows for many ADCs to be used per T/H and eliminates distortion stemming from open loop buffers interfacing between the front-end and back-end. Startup on-chip calibration deals with offset and gain mismatches as well as DAC linearity. Measurements show that the prototype ADC achieves an SNDR of 48.5 dB and a THD of less than 58 dB at Nyquist with an input signal of 1.4 . An estimated sampling clock skew spread of 400 fs is achieved by careful design and layout. Up to 4 GHz an SNR of more than 49 dB has been measured, enabled by the less than 110 fs rms clock jitter. The ADC consumes 480 mW from 1.2/1.3/1.6 V supplies and occupies an area of 5.1 mm.
Network design consideration for distributed control systems

This paper discusses the impact of network architecture on control performance in a class of distributed control systems called networked control systems (NCSs) and provides design considerations related to control quality of performance as well as network quality of service. The integrated network-control system changes the characteristics of time delays between application devices. This study first identifies several key components of the time delay through an analysis of network protocols and control dynamics. The analysis of network and control parameters is used to determine an acceptable working range of sampling periods in an NCS. A network-control simulator and an experimental networked machine tool have been developed to help validate and demonstrate the performance analysis results and identify the special performance characteristics in an NCS. These performance characteristics are useful guidelines for choosing the network and control parameters when designing an NCS.
Recognizing Question Entailment for Medical Question Answering

With the increasing heterogeneity and specialization of medical texts, automated question answering is becoming more and more challenging. In this context, answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution. In this paper, we propose a new approach for the detection of similar questions based on Recognizing Question Entailment (RQE). In particular, we consider Frequently Asked Question (FAQs) as a valuable and widespread source of information. Our final goal is to automatically provide an existing answer if FAQ similar to a consumer health question exists. We evaluate our approach using consumer health questions received by the National Library of Medicine and FAQs collected from NIH websites. Our first results are promising and suggest the feasibility of our approach as a valuable complement to classic question answering approaches.
Visual motion processing for the initiation of smooth-pursuit eye movements in humans.

We have used the initiation of pursuit eye movements as a tool to reveal properties of motion processing in the neural pathways that provide inputs to the human pursuit system. Horizontal and vertical eye position were recorded with a magnetic search coil in six normal adults. Stimuli were provided by individual trials of ramp target motion. Analysis was restricted to the first 100 ms of eye movement, which precedes the onset of corrective feedback. By recording the transient response to target motion at speeds the pursuit motor system can achieve, we investigated the visual properties of images that initiate pursuit. We have found effects of varying the retinal location, the direction, the velocity, the intensity, and the size of the stimulus. Eye acceleration in the first 100 ms of pursuit depended on both the direction of target motion and the initial position of the moving target. For horizontal target motion, eye acceleration was highest if the stimulus was close to the center of the visual field and moved toward the vertical meridian. For vertical target motion, eye acceleration was highest when the stimulus moved upward or downward within the lower visual field. The shape of the relationship between eye acceleration and initial target position was similar for target velocities ranging from 1.0 to 45 degrees/s. The initiation of pursuit showed two components that had different visual properties and were expressed early and late in the first 100 ms of pursuit. In the first 20 ms, instantaneous eye acceleration was in the direction of target motion but did not depend on other visual properties of the stimulus. At later times (e.g., 80-100 ms after pursuit initiation), instantaneous eye acceleration was strongly dependent on each property we tested. Targets that started close to and moved toward the position of fixation evoked the highest eye accelerations. For high-intensity targets, eye acceleration increased steadily as target velocity increased. For low-intensity targets, eye acceleration was selective for target velocities of 30-45 degrees/s. The properties of pursuit initiation in humans, including the differences between the early and late components, are remarkably similar to those reported by Lisberger and Westbrook (12) in monkeys. Our data provide evidence that the cell populations responsible for motion processing are similar in humans and monkeys and imply that the functional organization of the visual cortex is similar in the two species.
A Planar Electronically Steerable Patch Array Using Tunable PRI/NRI Phase Shifters

This paper presents a planar electronically steerable series-fed patch array for 2.4-GHz industrial, scientific, and medical band applications. The proposed steerable array uses 0deg tunable positive/negative-refractive-index (PRI/NRI) phase shifters to center its radiation about the broadside direction and allow scanning in both directions off the broadside. Using the PRI/NRI phase shifters also minimizes the squinting of the main beam across the operating bandwidth. The tunable PRI/NRI phase shifters employ 0.13-mum CMOS tunable active inductors, as well as varactors in order to extend their phase tuning range and maintain a low return loss across the entire phase tuning range. The feed network of the proposed array uses lambda/4 impedance transformers. This allows using identical interstage phase shifters, which share the same control voltages to tune all stages. Furthermore, using the impedance transformers in combination with the CMOS-based constant-impedance PRI/NRI phase shifters guarantees a low return loss for the antenna array across its entire scan angle range. The antenna array was fabricated, and is capable of continuously steering its main beam from -27deg to +22deg off the broadside direction with a gain of 8.4 dBi at 2.4 GHz. This is achieved by changing the varactors' control voltage from 3.5 to 15 V. Across the entire scan angle range, the array return loss is less than -10 dB across a bandwidth of 70 MHz, and the relative sidelobe level is always less than -10 dB. Furthermore, the proposed design achieves very low beam squinting of 1.3deg/100 MHz at broadside and a 1-dB compression point of 4.5 dBm.
Intelligent web-based learning system with personalized learning path guidance

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.
The physical impacts of microplastics on marine organisms: a review.

Plastic debris at the micro-, and potentially also the nano-scale, are widespread in the environment. Microplastics have accumulated in oceans and sediments worldwide in recent years, with maximum concentrations reaching 100 000 particles m(3). Due to their small size, microplastics may be ingested by low trophic fauna, with uncertain consequences for the health of the organism. This review focuses on marine invertebrates and their susceptibility to the physical impacts of microplastic uptake. Some of the main points discussed are (1) an evaluation of the factors contributing to the bioavailability of microplastics including size and density; (2) an assessment of the relative susceptibility of different feeding guilds; (3) an overview of the factors most likely to influence the physical impacts of microplastics such as accumulation and translocation; and (4) the trophic transfer of microplastics. These findings are important in guiding future marine litter research and management strategies.
Gender differences in extreme mathematical achievement: an international perspective on biological and social factors.

Genetic and other biological explanations have reemerged in recent scholarship on the underrepresentation of women in mathematics and the sciences. This study engages this debate by using international data-including math achievement scores from the Third International Mathematics and Sciences Study and country-level data from the World Bank, the United Nations, the International Labour Organization, the World Values Survey, and the International Social Survey Programme-to demonstrate the importance of social factors and to estimate an upper bound for the impact of genetic factors. The author argues that international variation provides a valuable opportunity to present simple and powerful arguments for the continued importance of social factors. In addition, where previous research has, by and large, focused on differences in population means, this work examines gender differences throughout the distribution. The article shows that there is considerable variation in gender differences internationally, a finding not easily explained by strictly biological theories. Modeling the cross-national variation in gender differences with country-level predictors reveals that differences among high achievers are related to gender inequality in the labor market and differences in the overall status of men and women.
Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale

Ensuring ultrareliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay, and average response time) is no longer an option but a necessity. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture and topology (across access, edge, and core), and decision-making under uncertainty is sorely lacking. The overarching goal of this paper is a first step to filling this void. Towards this vision, after providing definitions of latency and reliability, we closely examine various enablers of URLLC and their inherent tradeoffs. Subsequently, we focus our attention on a wide variety of techniques and methodologies pertaining to the requirements of URLLC, as well as their applications through selected use cases. These results provide crisp insights for the design of low-latency and high-reliability wireless networks.