nils-herrmann/clean_scirepeval huggingface dataset:
Generalized multilevel current source inverter topology with self-balancing current

A generalized single-phase multilevel current source inverter (MCSI) topology with self-balancing current is proposed, which uses the duality transformation from the generalized multilevel voltage source inverter (MVSI) topology. The existing single-phase 8- and 6-switch 5-level current source inverters (CSIs) can be derived from this generalized MCSI topology. In the proposed topology, each intermediate DC-link current level can be balanced automatically without adding any external circuits; thus, a true multilevel structure is provided. Moreover, owing to the dual relationship, many research results relating to the operation, modulation, and control strategies of MVSIs can be applied directly to the MCSIs. Some simulation results are presented to verify the proposed MCSI topology.
Distal semitendinosus tendon rupture: is there any benefit of surgical intervention?

Isolated rupture of the distal semitendinosus is rare, and as a result, there is paucity of evidence over the best method of managing the injury, that is, surgical or nonsurgical, particularly in light of the fact that the tendon is routinely harvested for anterior cruciate ligament (ACL) reconstruction. We present the cases of 2 elite sprinters with isolated ruptures of the distal semitendinosus who were managed nonoperatively, and we take the opportunity to look at the literature surrounding the management of this injury.
Angle of Arrival Localization in {LTE} Using {MIMO} Pre-Coder Index Feedback

The paper presents a new angle of arrival (AoA) positioning technique for long term evolution (LTE) cellular systems. The method quantifies the directional correlation between TX antenna bore-sight and different multiple-input-multiple-output (MIMO) pre-coder indices. This correlation is used to develop an AoA positioning method that only builds on functionality and hardware available in standard LTE base stations. Measurements from a Swedish test network indicate that the method performs as expected.
Foreign Body Inclusion Cyst of the Nasal Radix after Augmentation Rhinoplasty

Development of a cystic mass on the nasal dorsum is a very rare complication of aesthetic rhinoplasty. Most reported cases are of mucous cyst and entrapment of the nasal mucosa in the subcutaneous space due to traumatic surgical technique has been suggested as a presumptive pathogenesis. Here, we report a case of dorsal nasal cyst that had a different pathogenesis for cyst formation. A 58-yr-old woman developed a large cystic mass on the nasal radix 30 yr after augmentation rhinoplasty with silicone material. The mass was removed via a direct open approach and the pathology findings revealed a foreign body inclusion cyst associated with silicone. Successful nasal reconstruction was performed with autologous cartilages. Discussion and a brief review of the literature will be focused on the pathophysiology of and treatment options for a postrhinoplasty dorsal cyst.
PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms

Analog/mixed-signal machine learning (ML) accelerators exploit the unique computing capability of analog/mixed-signal circuits and inherent error tolerance of ML algorithms to obtain higher energy efficiencies than digital ML accelerators. Unfortunately, these analog/mixed-signal ML accelerators lack programmability, and even instruction set interfaces, to support diverse ML algorithms or to enable essential software control over the energy-vs-accuracy tradeoffs. We propose PROMISE, the first end-to-end design of a PROgrammable MIxed-Signal accElerator from Instruction Set Architecture (ISA) to high-level language compiler for acceleration of diverse ML algorithms. We first identify prevalent operations in widely-used ML algorithms and key constraints in supporting these operations for a programmable mixed-signal accelerator. Second, based on that analysis, we propose an ISA with a PROMISE architecture built with silicon-validated components for mixed-signal operations. Third, we develop a compiler that can take a ML algorithm described in a high-level programming language (Julia) and generate PROMISE code, with an IR design that is both language-neutral and abstracts away unnecessary hardware details. Fourth, we show how the compiler can map an application-level error tolerance specification for neural network applications down to low-level hardware parameters (swing voltages for each application Task) to minimize energy consumption. Our experiments show that PROMISE can accelerate diverse ML algorithms with energy efficiency competitive even with fixed-function digital ASICs for specific ML algorithms, and the compiler optimization achieves significant additional energy savings even for only 1% extra errors.
Total Dose Evaluation of Deep Submicron CMOS Imaging Technology Through Elementary Device and Pixel Array Behavior Analysis

Ionizing radiation effects on CMOS image sensors (CIS) manufactured using a 0.18 mum imaging technology are presented through the behavior analysis of elementary structures, such as field oxide FET, gated diodes, photodiodes and MOSFETs. Oxide characterizations appear necessary to understand ionizing dose effects on devices and then on image sensors. The main degradations observed are photodiode dark current increases (caused by a generation current enhancement), minimum size NMOSFET off-state current rises and minimum size PMOSFET radiation induced narrow channel effects. All these effects are attributed to the shallow trench isolation degradation which appears much more sensitive to ionizing radiation than inter layer dielectrics. Unusual post annealing effects are reported in these thick oxides. Finally, the consequences on sensor design are discussed thanks to an irradiated pixel array and a comparison with previous work is discussed.
A Social-Aware Group Formation Framework for Information Diffusion in Narrowband Internet of Things

Due to the heterogeneous and resource-constrained characters of Internet of Things (IoT), how to guarantee ubiquitous network connectivity is challenging. Although LTE cellular technology is the most promising solution to provide network connectivity in IoTs, information diffusion by cellular network not only occupies its saturating bandwidth, but also costs additional fees. Recently, NarrowBand-IoT (NB-IoT), introduced by 3GPP, is designed for low-power massive devices, which intends to refarm wireless spectrum and increase network coverage. For the sake of providing high link connectivity and capacity, we stimulate effective cooperations among user equipments (UEs), and propose a social-aware group formation framework to allocate resource blocks (RBs) effectively following an in-band NB-IoT solution. Specifically, we first introduce a social-aware multihop device-to-device (D2D) communication scheme to upload information toward the eNodeB within an LTE, so that a logical cooperative D2D topology can be established. Then, we formulate the D2D group formation as a scheduling optimization problem for RB allocation, which selects the feasible partition for the UEs by jointly considering relay method selection and spectrum reuse for NB-IoTs. Since the formulated optimization problem has a high computational complexity, we design a novel heuristic with a comprehensive consideration of power control and relay selection. Performance evaluations based on synthetic and real trace simulations manifest that the presented method can significantly increase link connectivity, link capacity, network throughput, and energy efficiency comparing with the existing solutions.
De-constructing depersonalization: Further evidence for symptom clusters

Depersonalization disorder is defined in the DSM-IV-TR using a single symptom criterion, which does not do justice to the phenomenological complexity of the disorder. In 394 affected adults, the Cambridge Depersonalization Scale yielded five factors (numbing, unreality of self, perceptual alterations, unreality of surroundings, and temporal disintegration), put forth as symptom criteria for a better diagnosis of depersonalization disorder.
Risky families: family social environments and the mental and physical health of offspring.

Risky families are characterized by conflict and aggression and by relationships that are cold, unsupportive, and neglectful. These family characteristics create vulnerabilities and/or interact with genetically based vulnerabilities in offspring that produce disruptions in psychosocial functioning (specifically emotion processing and social competence), disruptions in stress-responsive biological regulatory systems, including sympathetic-adrenomedullary and hypothalamic-pituitary-adrenocortical functioning, and poor health behaviors, especially substance abuse. This integrated biobehavioral profile leads to consequent accumulating risk for mental health disorders, major chronic diseases, and early mortality. We conclude that childhood family environments represent vital links for understanding mental and physical health across the life span.
Heterosexual and Nonheterosexual Young University Students' Involvement in Traditional and Cyber Forms of Bullying

Research has consistently found that school students who do not identify as self-declared completely heterosexual are at increased risk of victimization by bullying from peers. This study examined heterosexual and nonheterosexual university students' involvement in both traditional and cyber forms of bullying, as either bullies or victims. Five hundred twenty-eight first-year university students (M=19.52 years old) were surveyed about their sexual orientation and their bullying experiences over the previous 12 months. The results showed that nonheterosexual young people reported higher levels of involvement in traditional bullying, both as victims and perpetrators, in comparison to heterosexual students. In contrast, cyberbullying trends were generally found to be similar for heterosexual and nonheterosexual young people. Gender differences were also found. The implications of these results are discussed in terms of intervention and prevention of the victimization of nonheterosexual university students.
A high efficiency bridgeless flyback PFC converter for adapter application

This paper proposes a high efficiency low cost AC/DC converter for adapter application. In order to achieve high efficiency and low cost for adapter with universal AC input, a single stage bridgeless flyback PFC converter with peak current clamping technique was proposed. Compared with conventional flyback PFC converter, the conduction loss is reduced due to bridgeless structure. And the size of transformer can also be significantly reduced due to lower peak current, which results in lower cost and higher power density. Detailed operation principles and design considerations are illustrated. Experimental results from a 90W prototype with universal input and 20V/4.5A output are presented to verify the operation and performance of the proposed converter. The minimum efficiency at full load is above 91% over the entire input range.
Taxpayer compliance classification using C4.5, SVM, KNN, Naive Bayes and MLP

Tax revenue has a very important role to fund the State's finances. In order for the optimal tax revenue, the tax authorities must perform tax supervision to the taxpayers optimally. By using the self-assessment taxation system that is taxpayers calculation, pay and report their own tax obligations added with the data of other parties will create a very large data. Therefore, the tax authorities are required to immediately know the taxpayer non-compliance for further audit. This research uses the classification algorithm C4.5, SVM (Support Vector Machine), KNN (K-Nearest Neighbor), Naive Bayes and MLP (Multilayer Perceptron) to classify the level of taxpayer compliance with four goals that are corporate taxpayers comply formally and materially required, corporate taxpayers comply formally required, corporate taxpayers comply materially required and corporate taxpayers not comply formally and materially required. The classification results of each algorithm are compared and the best algorithm chosen based on criteria F-Score, Accuracy and Time taken to build the model by using fuzzy TOPSIS method. The final result shows that C4.5 algorithm is the best algorithm to classify taxpayer compliance level compared to other algorithms.
The effects of wall slide and sling slide exercises on scapular alignment and
pain in subjects with scapular downward rotation

The present study was performed to evaluate the changes in the scapular alignment, pressure pain threshold and pain in subjects with scapular downward rotation after 4 weeks of wall slide exercise or sling slide exercise.  Twenty-two subjects with scapular downward rotation participated in this study. The alignment of the scapula was measured using radiographic analysis (X-ray). Pain and pressure pain threshold were assessed using visual analogue scale and digital algometer. Patients were assessed before and after a 4 weeks of exercise.  In the within-group comparison, the wall slide exercise group showed significant differences in the resting scapular alignment, pressure pain threshold, and pain after four weeks. The between-group comparison showed that there were significant differences between the wall slide group and the sling slide group after four weeks.  The results of this study found that the wall slide exercise may be effective at reducing pain and improving scapular alignment in subjects with scapular downward rotation.
The Perception-Distortion Tradeoff

Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this paper, we prove mathematically that distortion and perceptual quality are at odds with each other. Specifically, we study the optimal probability for correctly discriminating the outputs of an image restoration algorithm from real images. We show that as the mean distortion decreases, this probability must increase (indicating worse perceptual quality). As opposed to the common belief, this result holds true for any distortion measure, and is not only a problem of the PSNR or SSIM criteria. However, as we show experimentally, for some measures it is less severe (e.g. distance between VGG features). We also show that generative-adversarial-nets (GANs) provide a principled way to approach the perception-distortion bound. This constitutes theoretical support to their observed success in low-level vision tasks. Based on our analysis, we propose a new methodology for evaluating image restoration methods, and use it to perform an extensive comparison between recent super-resolution algorithms.
Black-out test vs. UV camera for corona inspection of HV motor stator endwindings

As part of a factory acceptance test program for high voltage motors, end users sometimes specify a black-out test. This is a traditional offline inspection where the stator is placed in complete darkness, each phase is energized to 115% of rated line-to-neutral voltage, and both ends of the stator are observed to determine the presence, location and severity of endwinding surface partial discharges (PD). The setup for the test may be complex and risky due to the need for observers to be standing in darkness close to the energized parts of the stator. The test results are qualitative and strongly depend on the observer's eyesight and individual perception. A safer and more accurate alternative is to use an ultraviolet (UV) camera or viewer. The observed PD activity may be observed in ambient lighting, recorded, and quantified through simultaneous offline PD measurements. This paper describes the two inspection techniques, and presents experimental validation of the UV corona camera inspection method as a suitable replacement for a black-out test. Sample 13.8-kV coils were wound in a fixture simulating their relationship in a stator winding, and subjected to high-potential tests while observed under black-out conditions and with a UV corona camera in ambient lighting. The stator windings from two high voltage compressor motors were inspected using the same camera. Recorded images of the observed discharges and measured PD activity in the sample coils and stator winding were used to compare the evaluation by each test method.
Easy-first Coreference Resolution Vesel in Sto yanov

We describe an approach to coreference resolution that relies on the intuition that easy decisions should be made early, while harder decisions should be left for later when more information is available. We are inspired by the recent success of the rule-based system of Raghunathan et al. (2010), which relies on the same intuition. Our system, however, automatically learns from training data what constitutes an easy decision. Thus, we can utilize more features, learn more precise weights, and adapt to any dataset for which training data is available. Experiments show that our system outperforms recent state-of-the-art coreference systems including Raghunathan et al.’s system as well as a competitive baseline that uses a pairwise classifier.
Brain Region Segmentation using Convolutional Neural Network

Brain region segmentation or skull stripping is an essential step in neuroimaging application such as surgical, surface reconstruction, image registration etc. The accuracy of all existing methods depends on the registration and image geometry. When this fails, the probability of success is very less. In order to avoid this, Convolutional Neural Network (CNN) is used. For brain extraction which is free from geometry and registration. CNN learned the connectedness and shape of the brain. OASIS database is used which is publicly available benchmark dataset. In this method, training phase uses 30 images and 10 images are used for testing phase. The performance of CNN results is closer to the ground truth results given by experts.
PI3K in cancer: divergent roles of isoforms, modes of activation and therapeutic targeting

Phosphatidylinositol 3-kinases (PI3Ks) are crucial coordinators of intracellular signalling in response to extracellular stimuli. Hyperactivation of PI3K signalling cascades is one of the most common events in human cancers. In this Review, we discuss recent advances in our knowledge of the roles of specific PI3K isoforms in normal and oncogenic signalling, the different ways in which PI3K can be upregulated, and the current state and future potential of targeting this pathway in the clinic.
Human-inspired robotic exoskeleton (HuREx) for lower limb rehabilitation

A robot exoskeleton which is inspired by the human musculoskeletal system has been developed for lower limb rehabilitation. The device was manufactured using a novel technique employing 3D printing and fiber reinforcement to make one-of-a-kind form fitting human-robot connections. Actuation of the exoskeleton is achieved using PMAs (pneumatic air muscles) and cable actuation to give the system inherent compliance while maintaining a very low mass. The entire system was modeled including a new hybrid model for PMAs. Simulation and experimental results for a force and impedance based trajectory tracking controller demonstrate the feasibility for using the HuREx system for gait and rehabilitation training.
Optimizing Majority-Inverter Graphs with functional hashing

A Majority-Inverter Graph (MIG) is a recently introduced logic representation form whose algebraic and Boolean properties allow for efficient logic optimization. In particular, when considering logic depth reduction, MIG algorithms obtained significantly superior synthesis results as compared to the state-of-the-art approaches based on AND-inverter graphs and commercial tools. In this paper, we present a new MIG optimization algorithm targeting size minimization based on functional hashing. The proposed algorithm makes use of minimum MIG representations which are precomputed for functions up to 4 variables using an approach based on Satisfiability Modulo Theories (SMT). Experimental results show that heavily-optimized MIGs can be further minimized also in size, thanks to our proposed methodology. When using the optimized MIGs as starting point for technology mapping, we were able to improve both depth and area for the arithmetic instances of the EPFL benchmarks beyond the current results achievable by state-of-the-art logic synthesis algorithms.
Conductance and congestion in power law graphs

It has been observed that the degrees of the topologies of several communication networks follow heavy tailed statistics. What is the impact of such heavy tailed statistics on the performance of basic communication tasks that a network is presumed to support? How does performance scale with the size of the network? We study routing in families of sparse random graphs whose degrees follow heavy tailed distributions. Instantiations of such random graphs have been proposed as models for the topology of the Internet at the level of Autonomous Systems as well as at the level of routers. Let n be the number of nodes. Suppose that for each pair of nodes with degrees du and dv we have O(du dv) units of demand. Thus the total demand is O(n2). We argue analytically and experimentally that in the considered random graph model such demand patterns can be routed so that the flow through each link is at most O(n log2 n). This is to be compared with a bound O(n2) that holds for arbitrary graphs. Similar results were previously known for sparse random regular graphs, a.k.a. "expander graphs." The significance is that Internet-like topologies, which grow in a dynamic, decentralized fashion and appear highly inhomogeneous, can support routing with performance characteristics comparable to those of their regular counterparts, at least under the assumption of uniform demand and capacities. Our proof uses approximation algorithms for multicommodity flow and establishes strong bounds of a generalization of "expansion," namely "conductance." Besides routing, our bounds on conductance have further implications, most notably on the gap between first and second eigenvalues of the stochastic normalization of the adjacency matrix of the graph.
An underwater laser vision system for relative 3-D posture estimation to mesh-like targets

In this paper we described the development of both the hardware and the algorithms for a novel laser vision system suitable for measuring distances from both solid and mesh-like targets in underwater environments. The system was developed as a part of the AQUABOT project that developed an underwater robotic system for autonomous inspection of offshore aquaculture installation. The system takes into account the hemispherical optics typical in underwater vehicle designs and implements an array of line-lasers to ensure that mesh-like targets provide reflections in a consistent manner. The developed algorithms for the laser vision system are capable of providing either raw pointcloud data sets from each laser or with additional processing high level information like distance and relative orientation of the target with respect to the ROV can be recovered. An automatic calibration procedure along with the accompanying hardware that was developed, is described in this paper, to reduce the calibration overhead required by regular maintenance operations as is typical for underwater vehicles operating in sea-water. A set of experimental results in controlled laboratory environment as well as at offshore aquaculture installations demonstrate the performance of the system.