nils-herrmann/clean_scirepeval huggingface dataset:
Optimal charging operation of battery swapping stations with QoS guarantee

Vehicle (EV) drivers have an urgent demand for fast battery refueling methods for long distance trip and emergency drive. A well-planned battery swapping station (BSS) network can be a promising solution to offer timely refueling services. However, an inappropriate battery recharging process in the BSS may not only violate the stabilization of the power grid by their large power consumption, but also increase the charging cost from the BSS operators' point of view. In this paper, we aim to obtain the optimal charging policy to minimize the charging cost while ensuring the quality of service (QoS) of the BSS. A novel queueing network model is proposed to capture the operation nature for an individual BSS. Based on practical assumptions, we formulate the charging schedule problem as a stochastic control problem and achieve the optimal charging policy by dynamic programming. Monte Carlo simulation is used to evaluate the performance of different policies for both stationary and non-stationary EV arrival cases. Numerical results show the importance of determining the number of total batteries and charging outlets held in the BSS. Our work gives insight for the future infrastructure planning and operational management of BSS network.
Controls of hair follicle cycling.

Nearly 50 years ago, Chase published a review of hair cycling in which he detailed hair growth in the mouse and integrated hair biology with the biology of his day. In this review we have used Chase as our model and tried to put the adult hair follicle growth cycle in perspective. We have tried to sketch the adult hair follicle cycle, as we know it today and what needs to be known. Above all, we hope that this work will serve as an introduction to basic biologists who are looking for a defined biological system that illustrates many of the challenges of modern biology: cell differentiation, epithelial-mesenchymal interactions, stem cell biology, pattern formation, apoptosis, cell and organ growth cycles, and pigmentation. The most important theme in studying the cycling hair follicle is that the follicle is a regenerating system. By traversing the phases of the cycle (growth, regression, resting, shedding, then growth again), the follicle demonstrates the unusual ability to completely regenerate itself. The basis for this regeneration rests in the unique follicular epithelial and mesenchymal components and their interactions. Recently, some of the molecular signals making up these interactions have been defined. They involve gene families also found in other regenerating systems such as fibroblast growth factor, transforming growth factor-beta, Wnt pathway, Sonic hedgehog, neurotrophins, and homeobox. For the immediate future, our challenge is to define the molecular basis for hair follicle growth control, to regenerate a mature hair follicle in vitro from defined populations, and to offer real solutions to our patients' problems.
Dynamic program analysis of Microsoft Windows applications

Software instrumentation is a powerful and flexible technique for analyzing the dynamic behavior of programs. By inserting extra code in an application, it is possible to study the performance and correctness of programs and systems. Pin is a software system that performs run-time binary instrumentation of unmodified applications. Pin provides an API for writing custom instrumentation, enabling its use in a wide variety of performance analysis tasks such as workload characterization, program tracing, cache modeling, and simulation. Most of the prior work on instrumentation systems has focused on executing Unix applications, despite the ubiquity and importance of Windows applications. This paper identifies the Windows-specific obstacles for implementing a process-level instrumentation system, describes a comprehensive, robust solution, and discusses some of the alternatives. The challenges lie in managing the kernel/application transitions, injecting the runtime agent into the process, and isolating the instrumentation from the application. We examine Pin's overhead on typical Windows applications being instrumented with simple tools up to commercial program analysis products. The biggest factor affecting performance is the type of analysis performed by the tool. While the proprietary nature of Windows makes measurement and analysis difficult, Pin opens the door to understanding program behavior.
A Semantically Compositional Annotation Scheme for Time Normalization

We present a new annotation scheme for normalizing time expressions, such as three days ago, to computer-readable forms, such as 2016-03-07. The annotation scheme addresses several weaknesses of the existing TimeML standard, allowing the representation of time expressions that align to more than one calendar unit (e.g., the past three summers), that are defined relative to events (e.g., three weeks postoperative), and that are unions or intersections of smaller time expressions (e.g., Tuesdays and Thursdays). It achieves this by modeling time expression interpretation as the semantic composition of temporal operators like UNION, NEXT, and AFTER. We have applied the annotation scheme to 34 documents so far, producing 1104 annotations, and achieving inter-annotator agreement of 0.821.
Median Filter Approach for Removal of Baseline Wander in Photoplethysmography Signals

Removal of baseline wander is a crucial step in the signal conditioning stage of photoplethysmography signals. Hence, a method for removing the baseline wander from photoplethysmography based on two-stages of median filtering is proposed in this paper. Recordings from Physionet database are used to validate the proposed method. In this paper, the two-stage moving average filtering is also applied to remove baseline wander in photoplethysmography signals for comparison with our novel two-stage median filtering method. Our experiment results show that the performance of two-stage median filtering method is more effective in removing baseline wander from photoplethysmography signals. This median filtering method effectively improves the cross correlation with minimal distortion of the signal of interest. Although the method is proposed for baseline wander in photoplethysmography signals, it can be applied to other biomedical signals as well.
Towards a formalization of multi-touch gestures

Multi-touch is a technology which offers new styles of interaction compared to traditional input devices like keyboard and mouse. Users can quickly manipulate objects or execute commands by means of their fingers and hands. Current multi-touch frameworks offer a set of standard gestures that are easy to use when developing an application. In contrast, defining new gestures requires a lot of work involving low-level recognition of touch data. To address this problem, we contribute a discussion of strategies towards a formalization of gestural interaction on multi-touch surfaces. A test environment is presented, showing the applicability and benefit within multi-touch frameworks.
ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal

Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.
Modeling crowd evacuation of a building based on seven methodological approaches

Crowd evacuation of a building has been studied over the last decades. In this paper, seven methodological approaches for crowd evacuation have been identified. These approaches include cellular automata models, lattice gas models, social force models, fluid-dynamic models, agent-based models, game theoretic models, and approaches based on experiments with animals. According to available literatures, we discuss the advantages and disadvantages of these approaches, and conclude that a variety of different kinds of approaches should be combined to study crowd evacuation. Psychological and physiological elements affecting individual and collective behaviors should be also incorporated into the evacuation models. & 2008 Elsevier Ltd. All rights reserved.
Solving economic dispatch problem using hybrid GA-PS-SQP method

This study presents a new approach to solve the well-known power system Economic Load Dispatch problem (ED) using a hybrid algorithm consisting of Genetic Algorithm (GA), Pattern Search (PS) and Sequential Quadratic Programming (SQP). GA is the main optimizer of this algorithm, whereas PS and SQP are used to fine-tune the results obtained from the GA, thereby increasing solution confidence. To test the effectiveness of this approach it was applied to various test systems. Furthermore, the convergence characteristics and robustness of the proposed method have been explored through comparisons with results reported in literature. The outcome is very encouraging and suggests that the hybrid GA-PS-SQP algorithm is very effective in solving the power system economic load dispatch problem.
Anserini: Enabling the Use of Lucene for Information Retrieval Research

Software toolkits play an essential role in information retrieval research. Most open-source toolkits developed by academics are designed to facilitate the evaluation of retrieval models over standard test collections. Efforts are generally directed toward better ranking and less attention is usually given to scalability and other operational considerations. On the other hand, Lucene has become the de facto platform in industry for building search applications (outside a small number of companies that deploy custom infrastructure). Compared to academic IR toolkits, Lucene can handle heterogeneous web collections at scale, but lacks systematic support for evaluation over standard test collections. This paper introduces Anserini, a new information retrieval toolkit that aims to provide the best of both worlds, to better align information retrieval practice and research. Anserini provides wrappers and extensions on top of core Lucene libraries that allow researchers to use more intuitive APIs to accomplish common research tasks. Our initial efforts have focused on three functionalities: scalable, multi-threaded inverted indexing to handle modern web-scale collections, streamlined IR evaluation for ad hoc retrieval on standard test collections, and an extensible architecture for multi-stage ranking. Anserini ships with support for many TREC test collections, providing a convenient way to replicate competitive baselines right out of the box. Experiments verify that our system is both efficient and effective, providing a solid foundation to support future research.
Lambda Obfuscation

With the rise of increasingly advanced reverse engineering technique, especially more scalable symbolic execution tools, software obfuscation faces great challenges. Branch conditions contain important control flow logic of a program. Adversaries can use powerful program analysis tools to collect sensitive program properties and recover a program’s internal logic, stealing intellectual properties from the original owner. In this paper, we propose a novel control obfuscation technique that uses lambda calculus to hide the original computation semantics and makes the original program more obscure to understand and reverse engineer. Our obfuscator replaces the conditional instructions with lambda calculus function calls that simulate the same behavior with a more complicated execution model. Our experiment result shows that our obfuscation method can protect sensitive branch conditions from stateof-the-art symbolic execution techniques, with only modest overhead.
When nurse emotional intelligence matters: How transformational leadership influences intent to stay.

The purpose of this study was to examine the role of staff nurse emotional intelligence between transformational leadership and nurse intent to stay.



Nurse intent to stay and transformational leadership are widely recognized as vital components of nurse retention. Staff nurse emotional intelligence that has been confirmed improvable has been recently recognized in the nursing literature as correlated with retention. Yet, the nature of the relationships among these three variables is not known.



Cross-sectional data for 535 Chinese nurses were analysed using Structural Equation Modelling.



Transformational leadership and staff nurse emotional intelligence were significant predictors of nurse intent to stay, accounting for 34.3% of the variance in nurse intent to stay. Staff nurse emotional intelligence partially mediates the relationship between transformational leadership and nurse intent to stay.



The findings of the study emphasized the importance of transformational leadership in enhancing nurse emotional intelligence and to provide a deeper understanding of the mediating role of emotional intelligence in the relationship between nurse manager's transformational leadership and nurse's intent to stay.



Nurse leaders should develop training programmes to improve nursing manager transformational leadership and staff nurse emotional intelligence in the workplace.
Toward new vital signs : tools and methods for dense physiologic data capture, analysis, and decision support in critical care.

SIMON (Signal Interpretation and MONitoring) continuously collects, permanently stores, and processes bedside medical device data. Since 1998 SIMON has monitored over 3500 trauma intensive care unit (TICU) patients, representing approximately 250,000 hours of continuous monitoring and two billion data points, and is currently operational on all 14 TICU beds at Vanderbilt University Medical Center. This repository of dense physiologic data (heart rate, arterial, pulmonary, central venous, intracranial, and cerebral perfusion pressures, arterial and venous oxygen saturations, and other parameters sampled second-by-second) supports research to identify “new vital signs” features of patient physiology only observable through dense data capture and analysis more predictive of patient status than current measures. SIMON’s alerting and reporting capabilities, including web-based display, sentinel event notification via alphanumeric pagers, and daily summary reports of vital sign statistics, allow these discoveries to be rapidly tested and implemented in a working clinical environment.
User's Web Page Aesthetics Opinion: A Matter of Low-Level Image Descriptors Based on MPEG-7

Analyzing a user's first impression of a Web site is essential for interface designers, as it is tightly related to their overall opinion of a site. In fact, this early evaluation affects user navigation behavior. Perceived usability and user interest (e.g., revisiting and recommending the site) are parameters influenced by first opinions. Thus, predicting the latter when creating a Web site is vital to ensure users’ acceptance. In this regard, Web aesthetics is one of the most influential factors in this early perception. We propose the use of low-level image parameters for modeling Web aesthetics in an objective manner, which is an innovative research field. Our model, obtained by applying a stepwise multiple regression algorithm, infers a user's first impression by analyzing three different visual characteristics of Web site screenshots—texture, luminance, and color—which are directly derived from MPEG-7 descriptors. The results obtained over three wide Web site datasets (composed by 415, 42, and 6 Web sites, respectively) reveal a high correlation between low-level parameters and the users’ evaluation, thus allowing a more precise and objective prediction of users’ opinion than previous models that are based on other image characteristics with fewer predictors. Therefore, our model is meant to support a rapid assessment of Web sites in early stages of the design process to maximize the likelihood of the users’ final approval.
Package embedded decoupling capacitor impact on core power delivery network for ARM SoC application

In this paper, a BGA package having a ARM SoC chip is introduced, which has component-type embedded decoupling capacitors (decaps) for good power integrity performance of core power. To evaluate and confirm the impact of embedded decap on core PDN (power distribution network), two different packages were manufactured with and without the embedded decaps. The self impedances of system-level core PDN were simulated in frequency-domain and On-chip DvD (Dynamic Voltage Drop) simulations were performed in time-domain in order to verify the system-level impact of package embedded decap. There was clear improvement of system-level core PDN performance in middle frequency range when package embedded decaps were employed. In conclusion, the overall system-level core PDN for ARM SoC could meet the target impedance in frequency-domain as well as the target On-chip DvD level by having package embedded decaps.
Friend or Foe: Fine-Grained Categorization With Weak Supervision

Multi-instance learning (MIL) is widely acknowledged as a fundamental method to solve weakly supervised problems. While MIL is usually effective in standard weakly supervised object recognition tasks, in this paper, we investigate the applicability of MIL on an extreme case of weakly supervised learning on the task of fine-grained visual categorization, in which intra-class variance could be larger than inter-class due to the subtle differences between subordinate categories. For this challenging task, we propose a new method that generalizes the standard multi-instance learning framework, for which a novel multi-task co-localization algorithm is proposed to take advantage of the relationship among fine-grained categories and meanwhile performs as an effective initialization strategy for the non-convex multi-instance objective. The localization results also enable object-level domain-specific fine-tuning of deep neural networks, which significantly boosts the performance. Experimental results on three fine-grained datasets reveal the effectiveness of the proposed method, especially the importance of exploiting inter-class relationships between object categories in weakly supervised fine-grained recognition.
Inflammaging and Cancer: A Challenge for the Mediterranean Diet

Aging is considered the major risk factor for cancer, one of the most important mortality causes in the western world. Inflammaging, a state of chronic, low-level systemic inflammation, is a pervasive feature of human aging. Chronic inflammation increases cancer risk and affects all cancer stages, triggering the initial genetic mutation or epigenetic mechanism, promoting cancer initiation, progression and metastatic diffusion. Thus, inflammaging is a strong candidate to connect age and cancer. A corollary of this hypothesis is that interventions aiming to decrease inflammaging should protect against cancer, as well as most/all age-related diseases. Epidemiological data are concordant in suggesting that the Mediterranean Diet (MD) decreases the risk of a variety of cancers but the underpinning mechanism(s) is (are) still unclear. Here we review data indicating that the MD (as a whole diet or single bioactive nutrients typical of the MD) modulates multiple interconnected processes involved in carcinogenesis and inflammatory response such as free radical production, NF-κB activation and expression of inflammatory mediators, and the eicosanoids pathway. Particular attention is devoted to the capability of MD to affect the balance between pro- and anti-inflammaging as well as to emerging topics such as maintenance of gut microbiota (GM) homeostasis and epigenetic modulation of oncogenesis through specific microRNAs.
A Hierarchical Algorithm for Extreme Clustering

Many modern clustering methods scale well to a large number of data points, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy, incremental algorithm for hierarchical clustering that scales to both massive N and K---a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivated by the desire for both accuracy and speed, our approach performs tree rotations for the sake of enhancing subtree purity and encouraging balancedness. We prove that, under a natural separability assumption, our non-greedy algorithm will produce trees with perfect dendrogram purity regardless of data arrival order. Our experiments demonstrate that PERCH constructs more accurate trees than other tree-building clustering algorithms and scales well with both N and K, achieving a higher quality clustering than the strongest flat clustering competitor in nearly half the time.
NaturaSketch: Modeling from Images and Natural Sketches

Sketching on paper is a quick and easy way to communicate ideas. However, many sketch-based systems require people to draw in contrived ways instead of sketching freely as they would on paper. NaturaSketch affords a more natural interface through multiple strokes that overlap, cross, and connect. It also features a meshing algorithm to support multiple strokes of different classifications, which lets users design complex 3D shapes from sketches drawn over existing images. To provide a familiar workflow for object design, a set of sketch annotations can also specify modeling and editing operations. NaturaSketch empowers designers to produce a variety of models quickly and easily.
13.56 MHz high voltage multi-level resonant DC-DC converter

This paper demonstrates a dc-dc converter that combines resonant rectifiers in a way to achieve impedance matching and significant voltage gains without the need for a high quality factor (Q) impedance matching network. Multiple stages are stacked in a way that allows for higher output voltages without suffering efficiency penalties common to traditional serial type high-voltage converter topologies such as the Crockroft-Walton Multiplier. Application of this design results in an isolated resonant dc-dc converter design that promises high efficiency, fast transient response, and high power density while providing a high-voltage isolated output from a lower-voltage input. Specifically, we demonstrate a 2000 W, 2000 Vdc output proof of concept prototype dc-dc converter operating at a switching frequency of 13.56 MHz from an input voltage of 275 Vdc. Capacitive isolation provides an efficient means of allowing for multiple rectified outputs to be combined serially without exceeding semiconductor device limits. Further, it allows for implementation of a more efficient method of impedance matching at frequencies of 10s of MHz than traditional methods such as passive matching networks or transformers by the reduction of or elimination of those stages altogether.