nils-herrmann/clean_scirepeval huggingface dataset:
The Significance of the Default Mode Network (DMN) in Neurological and Neuropsychiatric Disorders: A Review

The relationship of cortical structure and specific neuronal circuitry to global brain function, particularly its perturbations related to the development and progression of neuropathology, is an area of great interest in neurobehavioral science. Disruption of these neural networks can be associated with a wide range of neurological and neuropsychiatric disorders. Herein we review activity of the Default Mode Network (DMN) in neurological and neuropsychiatric disorders, including Alzheimer's disease, Parkinson's disease, Epilepsy (Temporal Lobe Epilepsy - TLE), attention deficit hyperactivity disorder (ADHD), and mood disorders. We discuss the implications of DMN disruptions and their relationship to the neurocognitive model of each disease entity, the utility of DMN assessment in clinical evaluation, and the changes of the DMN following treatment.
Endocannabinoids — at the crossroads between the gut microbiota and host metabolism

Various metabolic disorders are associated with changes in inflammatory tone. Among the latest advances in the metabolism field, the discovery that gut microorganisms have a major role in host metabolism has revealed the possibility of a plethora of associations between gut bacteria and numerous diseases. However, to date, few mechanisms have been clearly established. Accumulating evidence indicates that the endocannabinoid system and related bioactive lipids strongly contribute to several physiological processes and are a characteristic of obesity, type 2 diabetes mellitus and inflammation. In this Review, we briefly define the gut microbiota as well as the endocannabinoid system and associated bioactive lipids. We discuss existing literature regarding interactions between gut microorganisms and the endocannabinoid system, focusing specifically on the triad of adipose tissue, gut bacteria and the endocannabinoid system in the context of obesity and the development of fat mass. We highlight gut-barrier function by discussing the role of specific factors considered to be putative 'gate keepers' or 'gate openers', and their role in the gut microbiota–endocannabinoid system axis. Finally, we briefly discuss data related to the different pharmacological strategies currently used to target the endocannabinoid system, in the context of cardiometabolic disorders and intestinal inflammation.
Study on application modes of military Internet of Things (MIOT)

Recently, the Internet of Things (IOT) has obtained rapid development and has a significant impact on the military field. This paper first proposes a conception of military internet of things (MIOT) and analyzes the architecture of MIOT in detail. Then, three modes of MIOT, i.e., information sensing, information transmission and information serving, are respectively studied to show various military domain applications. Finally, an application assumption of MIOT from the weapon control aspect is given to validate the proposed application modes.
A multinational examination of weight bias: predictors of anti-fat attitudes across four countries

As rates of obesity have increased throughout much of the world, so too have bias and prejudice toward people with higher body weight (that is, weight bias). Despite considerable evidence of weight bias in the United States, little work has examined its extent and antecedents across different nations. The present study conducted a multinational examination of weight bias in four Western countries with comparable prevalence rates of adult overweight and obesity.Using comprehensive self-report measures with 2866 individuals in Canada, the United States, Iceland and Australia, the authors assessed (1) levels of explicit weight bias (using the Fat Phobia Scale and the Universal Measure of Bias) and multiple sociodemographic predictors (for example, sex, age, race/ethnicity and educational attainment) of weight-biased attitudes and (2) the extent to which weight-related variables, including participants’ own body weight, personal experiences with weight bias and causal attributions of obesity, play a role in expressions of weight bias in different countries.The extent of weight bias was consistent across countries, and in each nation attributions of behavioral causes of obesity predicted stronger weight bias, as did beliefs that obesity is attributable to lack of willpower and personal responsibility. In addition, across all countries the magnitude of weight bias was stronger among men and among individuals without family or friends who had experienced this form of bias.These findings offer new insights and important implications regarding sociocultural factors that may fuel weight bias across different cultural contexts, and for targets of stigma-reduction efforts in different countries.
Tilt sensor based on three electrodes dielectric liquid capacitive sensor

This paper presents a design, fabrication and characterization of a low-cost capacitive tilt sensor. The proposed sensor consists of a three-electrode capacitor, which contains two-phase of the air and liquid as the dielectric media. The three electrodes hold a plastic tube and the tube is positioned on a printed circuit board (PCB) which consists of a 127 kHz sine wave generator, a pre-amplifier, a rectifier and a low pass filter. The proposed sensor structure can measure tilt angles in the rage of 0° to 75°, where the linear relationship between the angle to be measured and the output signal was observed in the range of 0° to 50°. The sensitivity and resolution of the sensor are measured to be 40mV/degree and 0.5 degree, respectively.
Comparative Analysis of Semiconductor Device Architectures for 5-nm Node and Beyond

This letter, for the first time, investigates interactive logic cell schemes and transistor architecture scaling options for 5-nm technology node (N5) and beyond. The proposed novel transistors, such as Hexagonal NanoWire (NW) and NanoRing (NR) architectures, are introduced having higher current drivability and lower parasitic capacitance than conventional NW or NanoSlab devices. The standard cell sizing options, including a 1-fin-per-device version and a 2-fin-per-device design, are systematically evaluated. Each device flavor has multiple vertical stacks when wire-like or slab-like structure is used. Comprehensive transistor and logic cell studies demonstrate that the novel NR is the optimal structure for N5 and beyond.
Split-Gate-Enhanced UMOSFET With an Optimized Layout of Trench Surrounding Mesa

An optimized split-gate-enhanced UMOSFET (SGE-UMOS) layout design is proposed, and its mechanism is investigated by 2-D and 3-D simulations. The layout features trench surrounding mesa (TSM): First, it optimizes the distribution of electric field density in the outer active mesa, reduces the electric-field crowding effect, and improves the breakdown voltage of the SGE-UMOS device. Second, it is unnecessary to design the layout corner with a large diameter in the termination region for the TSM structure as the conventional mesa surrounding trench (MST) structure, which is more efficient in terms of silicon usage. Rsp.on is reduced when compared with the MST structure within the same rectangular chip area. The BV of SGE-UMOS is increased from 72 to 115 V, and Rsp.on is reduced by approximately 3.5% as compared with the MST structure, due to the application of the TSM. Finally, it needs five masks in the process, and the trenches in active and termination regions are formed with the same processing steps; hence, the manufacturing process is simplified, and the cost is reduced as well.
Background Subtraction via 3D Convolutional Neural Networks

Background subtraction can be treated as the binary classification problem of highlighting the foreground region in a video whilst masking the background region, and has been broadly applied in various vision tasks such as video surveillance and traffic monitoring. However, it still remains a challenging task due to complex scenes and for lack of the prior knowledge about the temporal information. In this paper, we propose a novel background subtraction model based on 3D convolutional neural networks (3D CNNs) which combines temporal and spatial information to effectively separate the foreground from all the sequences in an end-to-end manner. Different from conventional models, we view background subtraction as three-class classification problem, i.e., the foreground, the background and the boundary. This design can obtain more reasonable results than existing baseline models. Experiments on the Change Detection 2012 dataset verify the potential of our model in both quantity and quality.
Why do narcissists take more risks ? Testing the roles of perceived risks and benefits of risky behaviors

0191-8869/$ see front matter 2009  A doi:10.1016/j.paid.2009.07.008 * Corresponding author.  E-mail address: foster@usouthal.edu (J.D. Foster). 1 Narcissistic personality is best thought of as a dim (Foster & Campbell, 2007). We use the term ‘‘narciss matter of convenience to refer to individuals who sco measures of narcissism, such as a the NPI. Much prior research demonstrates that narcissists take more risks than others, but almost no research has examined what motivates this behavior. The present study tested two potential driving mechanisms of risk-taking by narcissists (i.e., heightened perceptions of benefits and diminished perceptions of risks stemming from risky behaviors) by administering survey measures of narcissism and risk-taking to a sample of 605 undergraduate college students. Contrary to what might be expected, the results suggest that narcissists appreciate the risks associated with risky behaviors just as much as do less narcissistic individuals. Their risk-taking appears to instead be fueled by heightened perceptions of benefits stemming from risky behaviors. These results are consistent with a growing body of evidence suggesting that narcissists engage in some forms of potentially problematic behaviors, such as risk-taking, because of a surplus of eagerness rather than a deficit of inhibition.
Mobile Forensic Images and Videos Signature Pattern Matching using M-Aho-Corasick

Mobile forensics is an exciting new field of research. An increasing number of Open source and commercial digital forensics tools are focusing on less time during digital forensic examination. There is a major issue affecting some mobile forensic tools that allow the tools to spend much time during the forensic examination. It is caused by implementation of poor file searching algorithms by some forensic tool developers. This research is focusing on reducing the time taken to search for a file by proposing a novel, multi-pattern signature matching algorithm called M-Aho-Corasick which is adapted from the original Aho-Corasick algorithm. Experiments are conducted on five different datasets which one of the data sets is obtained from Digital Forensic Research Workshop (DFRWS 2010). Comparisons are made between M-Aho-Corasick using M_Triage with Dec0de, Lifter, XRY, and Xaver. The result shows that M-Aho-Corasick using M_Triage has reduced the searching time by 75% as compared to Dec0de, 36% as compared to Lifter, 28% as compared to XRY, and 71% as compared to Xaver. Thus, M-Aho-Corasick using M_Triage tool is more efficient than Dec0de, Lifter, XRY, and Xaver in avoiding the extraction of high number of false positive results. mobile forensics; Images; Videos; M-AhoCorasick; (File Signature Pattern Matching)
Urban Tracker: Multiple object tracking in urban mixed traffic

In this paper, we study the problem of detecting and tracking multiple objects of various types in outdoor urban traffic scenes. This problem is especially challenging due to the large variation of road user appearances. To handle that variation, our system uses background subtraction to detect moving objects. In order to build the object tracks, an object model is built and updated through time inside a state machine using feature points and spatial information. When an occlusion occurs between multiple objects, the positions of feature points at previous observations are used to estimate the positions and sizes of the individual occluded objects. Our Urban Tracker algorithm is validated on four outdoor urban videos involving mixed traffic that includes pedestrians, cars, large vehicles, etc. Our method compares favorably to a current state of the art feature-based tracker for urban traffic scenes on pedestrians and mixed traffic.
CMOS Circuits to Measure Timing Jitter Using a Self-Referenced Clock and a Cascaded Time Difference Amplifier With Duty-Cycle Compensation

This paper describes a reference-clock-free, high-time-resolution on-chip timing jitter measurement circuit using a self-referenced clock and a cascaded time difference amplifier (TDA) with duty-cycle compensation. A self-referenced clock with multiples of the clock period removes the necessity for a reference clock. In addition, a cascaded TDA with duty-cycle compensation improves the time resolution while maintaining the operational speed. Test chips were designed and fabricated using 65 nm and 40 nm CMOS technologies. The areas occupied by the circuits are 1350 μm2 (with TDA, 65 nm), 490 μm2 (without TDA, 65 nm), 470 μm2 (with TDA, 40 nm), and 112 μm2 (without TDA, 40 nm). Time resolutions of 31 fs (with TDA) and 2.8 ps (without TDA) were achieved. The proposed new architecture provides all-digital timing jitter measurement with fine-time-resolution measurement capability, without requiring a reference clock.
Designing a display unit to drive the 8×8 LED dot-matrix displays

This paper presents a field programmable gate array (FPGA) prototype of a display unit to drive the eight-by-eight LED dot-matrix displays of two colors. The circuit design was Verilog-based and downloaded to the Spartan-3 FPGA chip of the Spartan-3 Starter Board. The display unit was able to generate four character patterns to rotate in four directions successfully. The design itself suggests a wide variety of the small-sized display application domains with a single red-green eight-by-eight LED dot-matrix display.
Compact lumped-element 5-way wilkinson power divider with broadband operation

This paper describes a lumped-element 5-way Wilkinson power divider with broadband characteristics. The circuit contains multi-section LC-ladder circuits between input and output ports, and each output port is connected through series RLC circuits. By designing the divider based on multi-section matching transformer and L-section matching network techniques, the proposed 5-way divider can achieve broadband characteristics. In order to verify the design procedure, the proposed divider was designed and fabricated at a center frequency of 300MHz. The fabricated divider exhibited broadband characteristics with a relative bandwidth of about 75%.
Implementing an Intrusion Detection System using a Decision Tree

As the Internet becomes more and more accessible to people the world over, the realm of network security faces increasingly daunting problems. From the point of view of a defender, we now have to thwart the attempts of an increased number of malicious users; in the face of an attack, a larger consumer base left unserved turns out to be a larger margin of lost revenue. The value of being able to catch these attacks before they actually happen is thus going up with each passing day. This is where Intrusion Detection Systems (IDSs) come into play. This report describes over work in designing a Network IDS based on a canonical Decision Tree based approach. The system is trained and evaluated on pseudo-random subsets of the KDD-99 data set, which has been widely used to evaluate past and present day research in the domain.
Review paper: critical issues in tissue engineering: biomaterials, cell sources, angiogenesis, and drug delivery systems.

Tissue engineering is a newly emerging biomedical technology, which aids and increases the repair and regeneration of deficient and injured tissues. It employs the principles from the fields of materials science, cell biology, transplantation, and engineering in an effort to treat or replace damaged tissues. Tissue engineering and development of complex tissues or organs, such as heart, muscle, kidney, liver, and lung, are still a distant milestone in twenty-first century. Generally, there are four main challenges in tissue engineering which need optimization. These include biomaterials, cell sources, vascularization of engineered tissues, and design of drug delivery systems. Biomaterials and cell sources should be specific for the engineering of each tissue or organ. On the other hand, angiogenesis is required not only for the treatment of a variety of ischemic conditions, but it is also a critical component of virtually all tissue-engineering strategies. Therefore, controlling the dose, location, and duration of releasing angiogenic factors via polymeric delivery systems, in order to ultimately better mimic the stem cell niche through scaffolds, will dictate the utility of a variety of biomaterials in tissue regeneration. This review focuses on the use of polymeric vehicles that are made of synthetic and/or natural biomaterials as scaffolds for three-dimensional cell cultures and for locally delivering the inductive growth factors in various formats to provide a method of controlled, localized delivery for the desired time frame and for vascularized tissue-engineering therapies.
Hypergeometric Functions in Exact Geometric Computation

Most problems in computational geometry are algebraic. A general approach to address nonrobustness in such problems is Exact Geometric Computation (EGC). There are now general libraries that support EGC for the general programmer (e.g., Core Library, LEDA Real). Many applications require non-algebraic functions as well. In this paper, we describe how to provide non-algebraic functions in the context of other EGC capabilities. We implemented a multiprecision hypergeometric series package which can be used to evaluate common elementary math functions to an arbitrary precision. This can be achieved relatively easily using the Core Library which supports a guaranteed precision level of accuracy. We address several issues of efficiency in such a hypergeometric package: automatic error analysis, argument reduction, preprocessing of hypergeometric parameters, and precomputed constants. Some preliminary experimental results are reported.
Brief Announcement: MIC++: Accelerating Maximal Information Coefficient Calculation with GPUs and FPGAs

To discover relationships and associations between pairs of variables in large data sets have become one of the most significant challenges for bioinformatics scientists. To tackle this problem, maximal information coefficient (MIC) is widely applied as a measure of the linear or non-linear association between two variables. To improve the performance of MIC calculation, in this work we present MIC++, a parallel approach based on the heterogeneous accelerators including Graphic Processing Unit (GPU) and Field Programmable Gate Array (FPGA) engines, focusing on both coarse-grained and fine-grained parallelism. As the evaluation of MIC++, we have demonstrated the performance on the state-of-the-art GPU accelerators and the FPGA-based accelerators. Preliminary estimated results show that the proposed parallel implementation can significantly achieve more than 6X-14X speedup using GPU, and 4X-13X using FPGA-based accelerators.
Tics and Tourette: a clinical, pathophysiological and etiological review.

Describe developments in the etiological understanding of Tourette syndrome.



Tourette syndrome is a complex heterogenous clinical syndrome, which is not a unitary entity. Pathophysiological models describe gamma-aminobutyric acid-ergic-associated disinhibition of cortico-basal ganglia motor, sensory and limbic loops. MRI studies support basal ganglia volume loss, with additional white matter and cerebellar changes. Tourette syndrome cause likely involves multiple vulnerability genes and environmental factors. Only recently have some vulnerability gene findings been replicated, including histidine decarboxylase and neurexin 1, yet these rare variants only explain a small proportion of patients. Planned large genetic studies will improve genetic understanding. The role of inflammation as a contributor to disease expression is now supported by large epidemiological studies showing an association with maternal autoimmunity and childhood infection. Investigation of blood cytokines, blood mRNA and brain mRNA expression support the role of a persistent immune activation, and there are similarities with the immune literature of autistic spectrum disorder. Current treatment is symptomatic, although there is a better appreciation of factors that influence treatment response.



At present, therapeutics is focused on symptom-based treatments, yet with improved etiological understanding, we will move toward disease-modifying therapies in the future.
Gender Classification with Deep Learning

For our project, we consider the task of classifying the gender of an author of a blog, novel, tweet, post or comment. Previous attempts have considered traditional NLP models such as bag of words and n-grams to capture gender differences in authorship, and apply it to a specific media (e.g. formal writing, books, tweets, or blogs). Our project takes a novel approach by applying deep learning models developed by Lai et al to directly learn the gender of blog authors. We further refine their models and present a new deep learning model, the Windowed Recurrent Convolutional Neural Network (WRCNN), for gender classification. Our approaches are tested and trained on several datasets: a blog dataset used by Mukherjee et al, and two datasets representing 19th and 20th century authors, respectively. We report an accuracy of 86% on the blog dataset with our WRCNN model, comparable with state-of-the-art implementations.
A case-based reasoning framework for workflow model management

In order to support efficient workflow design, recent commercial workflow systems are providing templates of common business processes. These templates, called cases, can be modified individually or collectively into a new workflow to meet the business specification. However, little research has been done on how to manage workflow models, including issues such as model storage, model retrieval, model reuse and assembly. In this paper, we propose a novel framework to support workflow modeling and design by adapting workflow cases from a repository of process models. Our approach to workflow model management is based on a structured workflow lifecycle and leverages recent advances in model management and case-based reasoning techniques. Our contributions include a conceptual model of workflow cases, a similarity flooding algorithm for workflow case retrieval, and a domain-independent AI planning approach to workflow case composition. We illustrate the workflow model management framework with a prototype system called Case-Oriented Design Assistant for Workflow Modeling (CODAW).