nils-herrmann/clean_scirepeval huggingface dataset:
BRACHIAL PLEXOPATHIES : CLASSIFICATION , CAUSES

The brachial plexus, which is the most complex structure of the peripheral nervous system, supplies most of the upper extremity and shoulder. The high incidence of brachial plexopathies reflects its vulnerability to trauma and the tendency of disorders involving adjacent structures to affect it secondarily. The combination of anatomic, pathophysiologic, and neuromuscular knowledge with detailed clinical and ancillary study evaluations provides diagnostic and prognostic information that is important to clinical management. Since most brachial plexus disorders do not involve the entire brachial plexus but, rather, show a regional predilection, a regional approach to assessment of plexopathies is necessary. Muscle Nerve 30: 547–568, 2004
FHM + : Faster High-Utility Itemset Mining Using Length Upper-Bound Reduction

High-utility itemset (HUI) mining is a popular data mining task, consisting of enumerating all groups of items that yield a high profit in a customer transaction database. However, an important issue with traditional HUI mining algorithms is that they tend to find itemsets having many items. But those itemsets are often rare, and thus may be less interesting than smaller itemsets for users. In this paper, we address this issue by presenting a novel algorithm named FHM+ for mining HUIs, while considering length constraints. To discover HUIs efficiently with length constraints, FHM+ introduces the concept of Length UpperBound Reduction (LUR), and two novel upper-bounds on the utility of itemsets. An extensive experimental evaluation shows that length constraints are effective at reducing the number of patterns, and the novel upper-bounds can greatly decrease the execution time, and memory usage for HUI mining.
Three-year evaluation of posterior vertical bite reconstruction using direct composite resin – A case series

The use of resin composite materials to restore the complete occlusion of worn teeth is controversial and data are scarce. In this case series, the authors report on seven cases of progressive mixed erosive/abrasive worn dentition (85 posterior teeth) that have been reconstructed with direct resin composite restorations. In all patients, either one or both tooth arches was completely restored using direct resin composite restorations. All patients were treated with standardized materials and protocols. In five patients, a wax-up-based template was used to avoid freehand build-up techniques and to ensure optimal anatomy and function. All patients were re-assessed after a mean service time of three years (mean 35 +/5 months) using USPHS criteria. Subjective patient satisfaction was measured using visual analogue scales (VAS). The overall quality of the restorations was good, with predominantly determined "Alpha"-scores. Only the marginal quality showed small deteriorations, with "Beta" scores of 37% and 45% for marginal discoloration and integrity, respectively. In general, the composite showed signs of wear facets that resulted in 46% "Beta" scores within the anatomy scores. Small restoration fractures were only seen in two restorations, which were reparable. Two teeth were excluded from the evaluation, as they have been previously repaired due to fracture after biting on a nut. The results were very favorable, and the patients were satisfied with this non-invasive and economic treatment option, which still has the characteristic of a medium-term rehabilitation. The outcomes were comparable to other direct composite restorations successfully applied in adhesive dentistry.
A Cost and Power Efficient Image Compressor VLSI Design With Fuzzy Decision and Block Partition for Wireless Sensor Networks

This paper presents a novel hardware-oriented image compression algorithm and its very large-scale integration (VLSI) implementation for wireless sensor networks. The proposed novel image compression algorithm consists of a fuzzy decision, block partition, digital halftoning, and block truncation coding (BTC) techniques. A novel variable-size block partition technique was used in the proposed algorithm to improve image quality and compression performance. In addition, eight different types of blocks were encoded by Huffman coding according to probability to increase the compression ratio further. In order to achieve the low-cost and low-power characteristics, a novel iteration-based BTC training module was created to get representative levels and meet the requirement of wireless sensor networks. A prediction and a modified Golomb–Rice coding modules were designed to encode the information of representative levels to achieve higher compression performance. The proposed algorithm was realized by a VLSI technique with an UMC 0.18- $\mu \text{m}$  CMOS process. The synthesized gate counts and core area of this design were 6.4 k gate counts and 60 000  $\mu \text{m}^{2}$ , respectively. The operating frequency and power consumption were 100 MHz and 3.11 mW respectively. Compared with previous JPEG, JPEG-LS, and fixed-size BTC-based designs, this work reduced 20.9% gate counts more than previous designs. Moreover, the proposed design required only a one-line-buffer memory rather than a frame-buffer memory required by previous designs.
Experimental addition of Eleutherococcus senticosus and probiotic to the canine diet

There is a current trend to support pet health through the addition of natural supplements to their diet, taking into account the high incidence of medical conditions related to their immune system and gastrointestinal tract. This study investigates effects of the plant Eleutherococcus senticosus as a dietary additive on faecal microbiota, faecal characteristics, blood serum biochemistry and selected parameters of cellular immunity in healthy dogs. A combination of the plant with the canine-derived probiotic strain Lactobacillus fermentum CCM 7421 was also evaluated. Thirty-two dogs were devided into 4 treatment groups; receiving no additive (control), dry root extract of E. senticosus (8 mg/kg of body weight), probiotic strain (108 CFU/mL, 0.1 mL/kg bw) and the combination of both additives. The trial lasted 49 days with 14 days supplementation period. Results confirm no antimicrobial effect of the plant on the probiotic abundance either in vitro (cultivation test) or in vivo. The numbers of clostridia, lactic acid bacteria and Gram-negative bacteria as well as the concentration of serum total protein, triglyceride, glucose and aspartate aminotransferase were significantly altered according to the treatment group. Leukocyte phagocytosis was significantly stimulated by the addition of probiotic while application of plant alone led to a significant decrease.
Foot shape classification using 3D scanning data

3D foot anthropometric data was collected and classified into several foot shapes. A 3D foot anthropometric database which contains 1835 male right foot scanning images was used. The subjects aged from 18 to 60 years old. A 3D foot scanner was used to collect 12 foot dimensions including foot length, ball of foot length, outside ball of foot length, foot breadth, heel breadth, ball circumference, instep circumference, toe height, navicular height, instep height, toe 1 angle and toe 5 angle. The principle component analysis (PCA) and K-means cluster analysis was applied to classify male subjects' foot shapes. The PCA results indicated that foot breadth, foot length and navicular height were selected as three principle components. The percentage of total variance explained by the 3 principle components was 72.96%. The use of K-means clustering can classify male subjects' foot into 6 foot types. In addition, a new foot sizing system was developed for Taiwanese males. Comparing the new sizing system with the current CNS 4800-S1093 sizing system, the new sizing system can reduce the size numbers, and provide updated foot dimensions. Thus, the manufacturer can apply these results for shoe last design and footwear production with better fitness and lower cost.
Bayesian Abductive Logic Programs: A Probabilistic Logic for Abductive Reasoning

In this proposal, we introduce Bayesian Abductive Logic Programs (BALP), a probabilistic logic that adapts Bayesian Logic Programs (BLPs) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayes nets. However, unlike BLPs, which use deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for problems like plan/activity recognition that require abductive reasoning. In order to demonstrate the efficacy of BALPs, we apply it to two abductive reasoning tasks – plan recognition and natural language understanding.
Amantadine-Induced Livedo Racemosa.

Although livedo reticularis is a known adverse effect of amantadine, only limited studies have addressed this association. Livedo racemosa in contrast to livedo reticularis is characterized by a striking violaceous netlike pattern of the skin similar to livedo reticularis with a different histopathology and morphology (irregular, broken circular segments). In this case report, we present 2 cases of livedo racemosa and edema of lower extremities following amantadine treatment. The cutaneous biopsies in both cases showed intraluminal thrombi in subcutaneous blood vessels without evidence of vasculitis, which is consistent with livedo racemosa.
Blockchain access control Ecosystem for Big Data security

In recent years, the advancement in modern technologies has not only resulted in an explosion of huge data sets being captured and recorded in different fields, but also given rise to concerns in the security and protection of data during storage, transmission, processing, and access. The blockchain is a distributed ledger that records transactions in a secure, flexible, verifiable and permanent way. Transactions in a blockchain can be an exchange of an asset, the execution of the terms of a smart contract, or an update to a record. In this paper, we have developed a blockchain access control ecosystem that gives asset owners the sovereign right to effectively manage access control of large data sets and protect against data breaches. The Linux Foundation's Hyperledger Fabric blockchain is used to run the business network while the Hyperledger composer modeling tool is used to implement the smart contracts or transaction processing functions that run on the blockchain network. Blockchain, access control, data sharing, privacy, data protection, hyperledger, distributed ledger technology, smart contract
A mini review on pyridoacridines: Prospective lead compounds in medicinal chemistry

Natural products are increasingly being considered "critical and important" in drug discovery paradigms as a number of them such as camptothecin, penicillin, and vincristine serve as "lead molecules" for the discovery of potent compounds of therapeutic interests namely irinotecan, penicillin G, vinblastine respectively. Derived compounds of pharmacological interests displayed a wide variety of activity viz. anticancer, anti-inflammatory, antimicrobial, anti-protozoal, etc.; when modifications or derivatizations are performed on a parent moiety representing the corresponding derivatives. Pyridoacridine is such a moiety which forms the basic structure of numerous medicinally important natural products such as, but not limited to, amphimedine, ascididemin, eilatin, and sampangine. Interestingly, synthetic analogues of natural pyridoacridine exhibit diverse pharmacological activities and in view of these, natural pyridoacridines can be considered as "lead compounds". This review additionally provides a brief but critical account of inherent structure activity relationships among various subclasses of pyridoacridines. Furthermore, the current aspects and future prospects of natural pyridoacridines are detailed for further reference and consideration.
Flavor of roasted peanuts (Arachis hypogaea) - Part II: Correlation of volatile compounds to sensory characteristics.

Flavor and color of roasted peanuts are important research areas due to their significant influence on consumer preference. The aim of the present study was to explore correlations between sensory attributes of peanuts, volatile headspace compounds and color parameters. Different raw peanuts were selected to be representative of common market types, varieties, growing locations and grades used in Europe. Peanuts were roasted by a variety of processing technologies, resulting in 134 unique samples, which were analyzed for color, volatile composition and flavor profile by expert panel. Several headspace volatile compounds which positively or negatively correlated to "roasted peanut", "raw bean", "dark roast" and "sweet" attributes were identified. Results demonstrated that the correlation of CIELAB color parameters with roast related aromas, often taken for granted by the industry, is not strong when samples of different raw materials are subjected to different processing conditions.
High Performance Median Filtering Algorithm Based on NVIDIA GPU Computing

Median filtering technique is often used to remove additive white, salt and pepper noise from a signal or a source image. This filtering method is essential for the processing of digital data representing analog signals in real time. The median filter considers each pixel in the image in turn and looks at its nearby neighbors to determine whether or not it is representative of its surroundings. It replaces the pixel value with the median of neighboring pixel values. The median is calculated by first sorting all the pixel values from the surrounding neighborhood into numerical order and then replacing the pixel being considered with the middle pixel value. We have used graphics processing units (GPUs) to implement the post-processing, performed by NVIDIA Compute Unified Device Architecture (CUDA). Such a system is faster than the CPU version, or other traditional computing, for processing medical applications such as echography or Doppler. This paper shows the effect of the Median Filtering and a comparison of the performance of the CPU and GPU in terms of response time.
Do the effects of working memory training depend on baseline ability level?

There is a debate about the ability to improve cognitive abilities such as fluid intelligence through training on tasks of working memory capacity. The question addressed in the research presented here is who benefits the most from training: people with low cognitive ability or people with high cognitive ability? Subjects with high and low working memory capacity completed a 23-session study that included 3 assessment sessions, and 20 sessions of training on 1 of 3 training regiments: complex span training, running span training, or an active-control task. Consistent with other research, the authors found that training on 1 executive function did not transfer to ability on a different cognitive ability. High working memory subjects showed the largest gains on the training tasks themselves relative to the low working memory subjects-a finding that suggests high spans benefit more than low spans from training with executive function tasks.
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal. Expert demonstrations provided by humans, however, often show significant variability due to latent factors that are typically not explicitly modeled. In this paper, we propose a new algorithm that can infer the latent structure of expert demonstrations in an unsupervised way. Our method, built on top of Generative Adversarial Imitation Learning, can not only imitate complex behaviors, but also learn interpretable and meaningful representations of complex behavioral data, including visual demonstrations. In the driving domain, we show that a model learned from human demonstrations is able to both accurately reproduce a variety of behaviors and accurately anticipate human actions using raw visual inputs. Compared with various baselines, our method can better capture the latent structure underlying expert demonstrations, often recovering semantically meaningful factors of variation in the data.
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNN networks, including those developed specifically for crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes.
People tracking and re-identification by face recognition for RGB-D camera networks

This paper describes a face recognition-based people tracking and re-identification system for RGB-D camera networks. The system tracks people and learns their faces online to keep track of their identities even if they move out from the camera's field of view once. For robust people re-identification, the system exploits the combination of a deep neural network- based face representation and a Bayesian inference-based face classification method. The system also provides a predefined people identification capability: it associates the online learned faces with predefined people face images and names to know the people's whereabouts, thus, allowing a rich human-system interaction. Through experiments, we validate the re-identification and the predefined people identification capabilities of the system and show an example of the integration of the system with a mobile robot. The overall system is built as a Robot Operating System (ROS) module. As a result, it simplifies the integration with the many existing robotic systems and algorithms which use such middleware. The code of this work has been released as open-source in order to provide a baseline for the future publications in this field.
Clickstream Analytics: An Experimental Analysis of the Amazon Users' Simulated Monthly Traffic

Online shopping in recent years demonstrate a constant increase and as a result the study of user behavior through clickstream has attracted again the interest of the research community. This increase though requires novel approaches to clickstream analytics since the volume of the products available online and the corresponding transactions is huge. In this paper, a sequential frequent itemsets detection methodology (SAFID) is adopted to solve a clickstream analytics problem by analyzing a composite dataset which simulates monthly traffic of Amazon U.S. online retail shop. It is shown that the methodology can perform the analysis very efficiently in a simple desktop and detect all the frequently bought together products which can provide valuable knowledge to marketers of online retail stores. The methodology used can further be improved to handle larger datasets by considering a cloud computing environment.
Multi-objective Optimal Sizing and Energy Management of Hybrid Energy Storage System for Electric Vehicles

Hybrid energy storage system (HESS) composed of lithium-ion battery and supercapacitors has been recognized as one of the most promising solutions to face against the high cost, low power density and short cycle life of the battery-only energy storage system, which is the major headache hindering the further penetration of electric vehicles. In this work, the HESS sizing and energy management problem of an electric race car is investigated as a case study to improve the driving mileage and battery cycle life performance. Compared with the existing research, the distinctive features of this work are: (1) A dynamic model and a degradation model of the battery are employed to describe the dynamic behavior and to predict the cycle life of the battery more precisely; (2) Considering the fact that the design and control problems are coupled in most cases, in order to achieve a global optimal design solution and an implementable real-time energy management system, a Bi-level multi-objective sizing and control framework based on non-dominated sorting genetic algorithm-II and fuzzy logic control (FLC) is proposed to size the HESS and to optimize the membership functions of a FLC based EMS at the same time; (3) In order to improve the optimization efficiency, a vectorized fuzzy inference system which allows large scale of fuzzy logic controllers operating in parallel is devised. At last, the Pareto optimal solutions of different HESSs are obtained and compared to show the achieved enhancements of the proposed Bi-level optimal sizing and energy management framework.
Learning movement primitive libraries through probabilistic segmentation

Movement primitives are a well established approach for encoding and executing movements. While the primitives themselves have been extensively researched, the concept of movement primitive libraries has not received similar attention. Libraries of movement primitives represent the skill set of an agent. Primitives can be queried and sequenced in order to solve specific tasks. The goal of this work is to segment unlabeled demonstrations into a representative set of primitives. Our proposed method differs from current approaches by taking advantage of the often neglected, mutual dependencies between the segments contained in the demonstrations and the primitives to be encoded. By exploiting this mutual dependency, we show that we can improve both the segmentation and the movement primitive library. Based on probabilistic inference our novel approach segments the demonstrations while learning a probabilistic representation of movement primitives. We demonstrate our method on two real robot applications. First, the robot segments sequences of different letters into a library, explaining the observed trajectories. Second, the robot segments demonstrations of a chair assembly task into a movement primitive library. The library is subsequently used to assemble the chair in an order not present in the demonstrations.