nils-herrmann/clean_scirepeval huggingface dataset:
Charge equalization converter with parallel primary winding for series connected Lithium-Ion battery strings in HEV

In this paper, a charge equalization converter with parallel-connected primary windings of transformers is proposed. The proposed work effectively balances the voltage among Lithium-Ion battery cells despite each battery cell has low voltage gap compared with its SOC. The principle of the proposed work is that the equalizing energy from all battery strings moves to the lowest voltage battery through the isolated dc/dc converter controlled by the corresponding solid state relay switch. In this paper, a prototype of four Lithium-Ion battery cells is optimally designed and implemented, and experimental results show that the proposed method has excellent cell balancing performance.
Blast injuries

Health-care providers are increasingly faced with the possibility of needing to care for people injured in explosions, but can often, however, feel undertrained for the unique aspects of the patient's presentation and management. Although most blast-related injuries (eg, fragmentation injuries from improvised explosive devices and standard military explosives) can be managed in a similar manner to typical penetrating or blunt traumatic injuries, injuries caused by the blast pressure wave itself cannot. The blast pressure wave exerts forces mainly at air-tissue interfaces within the body, and the pulmonary, gastrointestinal, and auditory systems are at greatest risk. Arterial air emboli arising from severe pulmonary injury can cause ischaemic complications-especially in the brain, heart, and intestinal tract. Attributable, in part, to the scene chaos that undoubtedly exists, poor triage and missed diagnosis of blast injuries are substantial concerns because injuries can be subtle or their presentation can be delayed. Management of these injuries can be a challenge, compounded by potentially conflicting treatment goals. This Seminar aims to provide a thorough overview of these unique primary blast injuries and their management.
The molecular mechanisms of signaling by cooperative assembly formation in innate immunity pathways.

The innate immune system is the first line of defense against infection and responses are initiated by pattern recognition receptors (PRRs) that detect pathogen-associated molecular patterns (PAMPs). PRRs also detect endogenous danger-associated molecular patterns (DAMPs) that are released by damaged or dying cells. The major PRRs include the Toll-like receptor (TLR) family members, the nucleotide binding and oligomerization domain, leucine-rich repeat containing (NLR) family, the PYHIN (ALR) family, the RIG-1-like receptors (RLRs), C-type lectin receptors (CLRs) and the oligoadenylate synthase (OAS)-like receptors and the related protein cyclic GMP-AMP synthase (cGAS). The different PRRs activate specific signaling pathways to collectively elicit responses including the induction of cytokine expression, processing of pro-inflammatory cytokines and cell-death responses. These responses control a pathogenic infection, initiate tissue repair and stimulate the adaptive immune system. A central theme of many innate immune signaling pathways is the clustering of activated PRRs followed by sequential recruitment and oligomerization of adaptors and downstream effector enzymes, to form higher-order arrangements that amplify the response and provide a scaffold for proximity-induced activation of the effector enzymes. Underlying the formation of these complexes are co-operative assembly mechanisms, whereby association of preceding components increases the affinity for downstream components. This ensures a rapid immune response to a low-level stimulus. Structural and biochemical studies have given key insights into the assembly of these complexes. Here we review the current understanding of assembly of immune signaling complexes, including inflammasomes initiated by NLR and PYHIN receptors, the myddosomes initiated by TLRs, and the MAVS CARD filament initiated by RIG-1. We highlight the co-operative assembly mechanisms during assembly of each of these complexes.
Cytological features of "noninvasive follicular thyroid neoplasm with papillary-like nuclear features" and their correlation with tumor histology.

Among thyroid papillary carcinomas (PTCs), the follicular variant is the most common and includes encapsulated forms (EFVPTCs). Noninvasive EFVPTCs have very low risk of recurrence or other adverse events and have been recently proposed to be designated as noninvasive follicular thyroid neoplasm with papillary-like nuclear features or NIFTP, thus eliminating the term carcinoma. This proposal is expected to significantly impact the risk of malignancy associated with the currently used diagnostic categories of thyroid cytology. In this study, we analyzed the fine needle aspiration biopsy (FNAB) cytology features of 96 histologically proven NIFTPs and determined how the main nuclear features of NIFTP correlate between cytological and histological samples. Blind review of FNAB cytology from NIFTP nodules yielded the diagnosis of "follicular neoplasm" (Bethesda category IV) in 56% of cases, "suspicious for malignancy" (category V) in 27%, "atypia of undetermined significance/follicular lesion of undetermined significance" (category III) in 15%, and "malignant" (category VI) in 2%. We found good correlation (κ=0.62) of nuclear features between histological and cytological specimens. NIFTP nuclear features (size, irregularities of contours, and chromatin clearing) were significantly different from those of benign nodules but not from those of invasive EFVPTC. Our data indicate that most of the NIFTP nodules yield an indeterminate cytological diagnosis in FNAB cytology and nuclear features found in cytology samples are reproducibly identified in corresponding histology samples. Because of the overlapping nuclear features with invasive EFVPTC, NIFTP cannot be reliably diagnosed preoperatively but should be listed in differential diagnosis of all indeterminate categories of thyroid cytology.
ARREST: A RSSI Based Approach for Mobile Sensing and Tracking of a Moving Object

We present Autonomous Rssi based RElative poSitioning and Tracking (ARREST), a new robotic sensing system for tracking and following a moving, RF-emitting object, which we refer to as the Leader, solely based on signal strength information. Our proposed tracking agent, which we refer to as the TrackBot, uses a single rotating, off-the-shelf, directional antenna, novel angle and relative speed estimation algorithms, and Kalman filtering to continually estimate the relative position of the Leader with decimeter level accuracy (which is comparable to a state-of-the-art multiple access point based RF-localization system) and the relative speed of the Leader with accuracy on the order of 1 m/s. The TrackBot feeds the relative position and speed estimates into a Linear Quadratic Gaussian (LQG) controller to generate a set of control outputs to control the orientation and the movement of the TrackBot. We perform an extensive set of real world experiments with a full-fledged prototype to demonstrate that the TrackBot is able to stay within 5m of the Leader with: (1) more than 99% probability in line of sight scenarios, and (2) more than 75% probability in no line of sight scenarios, when it moves 1.8X faster than the Leader.
The significance of heavy pornography involvement for romantic partners: research and clinical implications.

This article presents a paradigm case portrait of female romantic partners of heavy pornography users. Based on a sample of 100 personal letters, this portrait focuses on their often traumatic discovery of the pornography usage and the significance they attach to this usage for (a) their relationships, (b) their own worth and desirability, and (c) the character of their partners. Finally, we provide a number of therapeutic recommendations for helping these women to think and act more effectively in their very difficult circumstances.
Mining gene expression data by interpreting principal components

There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis. We present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation. We provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially valuable in instances where there are many diverse conditions (10's to hundreds of different tissues or cell types), a situation in which many clustering and ordering algorithms become problematic. This approach also shows promise in other topic domains such as multi-spectral imaging datasets.
Evolution of Consciousness According to Jean Gebser

This article introduces and summarizes The Ever-Present Origin, the magnum opus of cultural historian and evolutionary philosopher Jean Gebser, largely in his own words. According to Gebser, human consciousness underwent a series of mutations each of which has enriched reality by a new (qualitative) dimension. At present humanity is again undergoing such a mutation: this time from the mental, perspectival structure of consciousness to the integral, aperspectival structure or, using the terminology of Sri Aurobindo, from mind to supermind. The integrality of this consciousness consists in part in its ability to integrate the preceding consciousness structures, rather than suppressing them (as the mental structure does) and hence being adversely affected by them. The article concludes with a brief account of the Mother’s personal experience of this mutation.
Bayesian Nonparametric Feature Construction for Inverse Reinforcement Learning

Most of the algorithms for inverse reinforcement learning (IRL) assume that the reward function is a linear function of the pre-defined state and action features. However, it is often difficult to manually specify the set of features that can make the true reward function representable as a linear function. We propose a Bayesian nonparametric approach to identifying useful composite features for learning the reward function. The composite features are assumed to be the logical conjunctions of the predefined atomic features so that we can represent the reward function as a linear function of the composite features. We empirically show that our approach is able to learn composite features that capture important aspects of the reward function on synthetic domains, and predict taxi drivers’ behaviour with high accuracy on a real GPS trace dataset.
Synthetic Sampling for Multi-Class Malignancy Prediction

We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to optimize classifiers for overall accuracy without considering the relative distribution of each class, we look into using synthetic sampling to increase perclass performance when predicting the degree of malignancy. Using low-level image features and a random forest classifier, we show that using synthetic oversampling techniques increases the sensitivity of the minority classes by an average of 7.22% points, with as much as a 19.88% point increase in sensitivity for a particular minority class. Furthermore, the analysis of low-level image feature distributions for the synthetic nodules reveals that these nodules can provide insights on how to preprocess image data for better classification performance or how to supplement the original datasets when more data acquisition is feasible.
Fabio Carbone International tourism and cultural diplomacy : A new conceptual approach towards global mutual understanding and peace through tourism

Peace is commonly considered as 'absence of war'. Nevertheless, peace the so called 'positive peace' (Galtung, 1996) – implies a lot more than this. It implies the creation of a society based on social justice through equal opportunity, a fair distribution of power and resources, equal protection and impartial enforcement of law, and above all, mutual cultural understanding and respect. Th us, far from the pessimistic view of classical realists, which assume that the confl ict is an intrinsic part of the human nature, we claim that peace is an architecture requiring fi rm, specifi c foundations such as a widespread education to peace and the promotion of intercultural dialogue. In this context, social scientists speculate on the causal relation between tourism and peace. In the present article we deepen this topic to propose more concrete arguments about the existence of a relationship between tourism and the construction of a positive peace. We thus off er a pioneering approach by proposing an association between international tourism and the practices of cultural diplomacy. We also analyse one of the most important conditions for this alliance to be created, that is, the implementation of cultural heritage management policies based on public participation and, at the same time, the promotion of intercultural dialogue (paideia approach to cultural heritage management). In this sense, we fi nally propose a defi nition for 'cultural heritage quality management'.
Fibromyalgia Syndrome: A Case Report on Controlled Remission of Symptoms by a Dietary Strategy

A 34-year-old woman suffered from significant chronic pain, depression, non-restorative sleep, chronic fatigue, severe morning stiffness, leg cramps, irritable bowel syndrome, hypersensitivity to cold, concentration difficulties, and forgetfulness. Blood tests were negative for rheumatic disorders. The patient was diagnosed with Fibromyalgia syndrome (FMS). Due to the lack of effectiveness of pharmacological therapies in FMS, she approached a novel metabolic proposal for the symptomatic remission. Its core idea is supporting serotonin synthesis by allowing a proper absorption of tryptophan assumed with food, while avoiding, or at least minimizing the presence of interfering non-absorbed molecules, such as fructose and sorbitol. Such a strategy resulted in a rapid improvement of symptoms after only few days on diet, up to the remission of most symptoms in 2 months. Depression, widespread chronic pain, chronic fatigue, non-restorative sleep, morning stiffness, and the majority of the comorbidities remitted. Energy and vitality were recovered by the patient as prior to the onset of the disease, reverting the occupational and social disabilities. The patient episodically challenged herself breaking the dietary protocol leading to its negative test and to the evaluation of its benefit. These breaks correlated with the recurrence of the symptoms, supporting the correctness of the biochemical hypothesis underlying the diet design toward remission of symptoms, but not as a final cure. We propose this as a low risk and accessible therapeutic protocol for the symptomatic remission in FMS with virtually no costs other than those related to vitamin and mineral salt supplements in case of deficiencies. A pilot study is required to further ground this metabolic approach, and to finally evaluate its inclusion in the guidelines for clinical management of FMS.
Estimation of battery parameters of the equivalent circuit model using Grey Wolf Optimization

For dynamic simulation of battery electric vehicles, it is vital to estimate accurately battery parameters, to use battery effectively. The estimation of parameters deploys experimental methods that are expensive, require high computational power and are time-consuming. Hence to overcome this problem, a methodology based on meta-heuristic techniques (Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and recently proposed Grey Wolf Optimization (GWO)) has used. These techniques are simple to use and require less computational power. Estimation has done by how close the model estimated voltage curve is to the known catalogue voltage curve and feasibility of techniques evaluated by accuracy (minimizing error) and it's the rate of convergence. Investigation showed that GWO has the best accuracy among meta-heuristic techniques for estimation of the battery parameters.
Popularity and Novelty Dynamics in Evolving Networks

Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task. Some items gain popularity repeatedly over time while some become popular and novel only once. This work aims to identify the key-factors: popularity and novelty. To do so, we consider two types of novelty predictions: items appearing in the popular ranking list for the first time; and items which were not in the popular list in the past time window, but might have been popular before the recent past time window. In order to identify the popular items, a careful consideration of macro-level analysis is needed. In this work we propose a model, which exploits item level information over a span of time to rank the importance of the item. We considered ageing or decay effect along with the recent link-gain of the items. We test our proposed model on four various real-world datasets using four information retrieval based metrics.
7-DOF horseback riding simulator based on a crank mechanism with variable radius and its inverse kinematics solution

This paper proposes a robotic simulator for horseback riding that incorporates a crank mechanism with a variable radius. In horseback riding simulations, it is necessary to efficiently generate vertical sinusoid motion. A novel crank mechanism is proposed to change the unidirectional rotation of a motor to sinusoid wave motion with an arbitrary magnitude and frequency. On the basis of the results of a kinematic analysis, the singularity and redundancy issues of the system are presented. To avoid the singularity problem, an inverse kinematics solution is presented and the result of the simulation shows stable solution of the inverse kinematics.
A Tangled Mass: The Android Root Certificate Stores

The security of today's Web rests in part on the set of X.509 certificate authorities trusted by each user's browser. Users generally do not themselves configure their browser's root store but instead rely upon decisions made by the suppliers of either the browsers or the devices upon which they run. In this work we explore the nature and implications of these trust decisions for Android users. Drawing upon datasets collected by Netalyzr for Android and ICSI's Certificate Notary, we characterize the certificate root store population present in mobile devices in the wild. Motivated by concerns that bloated root stores increase the attack surface of mobile users, we report on the interplay of certificate sets deployed by the device manufacturers, mobile operators, and the Android OS. We identify certificates installed exclusively by apps on rooted devices, thus breaking the audited and supervised root store model, and also discover use of TLS interception via HTTPS proxies employed by a market research company.
Ratiometric BJT-based thermal sensor in 32nm and 22nm technologies

Thermal sensors are used in modern microprocessors to provide information for: 1) throttling at the maximum temperature of operation, and 2) fan regulation at temperatures down to 50°C. Today's microprocessors are thermally limited in many applications, so accurate temperature readings are essential in order to maximize performance. There are fairly large thermal gradients across the core, which vary for different instructions, so it is necessary to position thermal sensors near hot-spots. In addition, the locations of the hot-spots may not be predictable during the design phase. Thus it is necessary for hot-spot sensors to be small enough to be moved late in the design cycle or even after first Silicon.
Measuring the Performance of User Traffic in Home Wireless Networks

This paper studies how home wireless performance characteristics affect the performance of user traffic in real homes. Previous studies have focused either on wireless metrics exclusively, without connection to the performance of user traffic; or on the performance of the home network at higher layers. In contrast, we deploy a passive measurement tool on commodity access points to correlate wireless performance metrics with TCP performance of user traffic. We implement our measurement tool, deploy it on commodity routers in 66 homes for one month, and study the relationship between wireless metrics and TCP performance of user traffic. We find that, most of the time, TCP flows from devices in the home achieve only a small fraction of available access link throughput; as the throughput of user traffic approaches the access link throughput, the characteristics of the home wireless network more directly affect performance. We also find that the 5 GHz band offers users better performance better than the 2.4 GHz band, and although the performance of devices varies within the same home, many homes do not have multiple devices sending high traffic volumes, implying that certain types of wireless contention may be uncommon in practice.
ML Confidential: Machine Learning on Encrypted Data

We demonstrate that, by using a recently proposed leveled homomorphic encryption scheme, it is possible to delegate the execution of a machine learning algorithm to a computing service while retaining confidentiality of the training and test data. Since the computational complexity of the homomorphic encryption scheme depends primarily on the number of levels of multiplications to be carried out on the encrypted data, we define a new class of machine learning algorithms in which the algorithm’s predictions, viewed as functions of the input data, can be expressed as polynomials of bounded degree. We propose confidential algorithms for binary classification based on polynomial approximations to least-squares solutions obtained by a small number of gradient descent steps. We present experimental validation of the confidential machine learning pipeline and discuss the trade-offs regarding computational complexity, prediction accuracy and cryptographic security.