LLukas22/scidocs huggingface dataset:
Designing an Inverter-based Operational Transconductance Amplifier-capacitor Filter with Low Power Consumption for Biomedical Applications

The operational transconductance amplifier-capacitor (OTA-C) filter is one of the best structures for implementing continuous-time filters. It is particularly important to design a universal OTA-C filter capable of generating the desired filter response via a single structure, thus reducing the filter circuit power consumption as well as noise and the occupied space on the electronic chip. In this study, an inverter-based universal OTA-C filter with very low power consumption and acceptable noise was designed with applications in bioelectric and biomedical equipment for recording biomedical signals. The very low power consumption of the proposed filter was achieved through introducing bias in subthreshold MOSFET transistors. The proposed filter is also capable of simultaneously receiving favorable low-, band-, and high-pass filter responses. The performance of the proposed filter was simulated and analyzed via HSPICE software (level 49) and 180 nm complementary metal-oxide-semiconductor technology. The rate of power consumption and noise obtained from simulations are 7.1 nW and 10.18 nA, respectively, so this filter has reduced noise as well as power consumption. The proposed universal OTA-C filter was designed based on the minimum number of transconductance blocks and an inverter circuit by three transconductance blocks (OTA).
Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process

We observe a surface roughness in end milling machining process which is influenced by machine parameters, namely radial rake angle, speed and feed rate cutting condition. In this machining, we need to minimize and to obtain as low as possible the surface roughness by determining the optimum values of the three parameters. In previous works, some researchers used a response surface methodology (RSM) and a soft-computing approach, which was based on ordinary linear regression and genetic algorithms (GAs), to estimate the minimum surface roughness and its corresponding values of the parameters. However, the construction of the ordinary regression models was conducted without considering the existence of multicollinearity which can lead to inappropriate prediction. Beside that it is known the relation between the surface roughness and the three parameters is nonlinear, which implies that a linear regression model can be inappropriate model to approximate it. In this paper, we present a technique developed using hybridization of kernel principal component analysis (KPCA) based nonlinear regression and GAs to estimate the optimum values of the three parameters such that the estimated surface roughness is as low as possible. We use KPCA based regression to construct a nonlinear regression and to avoid the effect of multicollinearity in its prediction model. We show that the proposed technique gives more accurate prediction model than the ordinary linear regression’s approach. Comparing with the experiment data and RSM, our technique reduces the minimum surface roughness by about 45.3% and 54.2%, respectively. 2012 Elsevier Ltd. All rights reserved.
Optimal Day-Ahead Charging Scheduling of Electric Vehicles Through an Aggregative Game Model

The electric vehicle (EV) market has been growing rapidly around the world. With large scale deployment of EVs in power systems, both the grid and EV owners will benefit if the flexible demand of EV charging is properly managed through the electricity market. When EV charging demand is considerable in a grid, it will impact spot prices in the electricity market and consequently influence the charging scheduling itself. The interaction between the spot prices and the EV demand needs to be considered in the EV charging scheduling, otherwise it will lead to a higher charging cost. A day-ahead EV charging scheduling based on an aggregative game model is proposed in this paper. The impacts of the EV demand on the electricity prices are formulated with the game model in the scheduling considering possible actions of other EVs. The existence and uniqueness of the pure strategy Nash equilibrium are proved for the game. An optimization method is developed to calculate the equilibrium of the game model through quadratic programming. The optimal scheduling of the individual EV controller considering the actions of other EVs in the game is developed with the EV driving pattern distribution. Case studies with the proposed game model were carried out using real world driving data from the Danish National Travel Surveys. The impacts of the EV driving patterns and price forecasts on the EV demand with the proposed game model were also analysed.
Emotion regulation and loss of control eating Loss of control eating in adolescents : Associations with adaptive and maladaptive emotion regulation strategies

Objective: To examine differences in the use of emotion regulation strategies in adolescents with and without loss of control over eating (LOC). Method: a community-based sample of 524 adolescents from 12 to 18 years old (70.6% girls; Mage = 15.08; SD = 1.59) reported on LOC and the use of several maladaptive and adaptive emotion regulation strategies. Results: Adolescents who experience LOC (28%) report more use of maladaptive strategies. With regard to adaptive strategies a significant group X gender interaction effect was found with girls who report LOC using less adaptive strategies. More specifically, based on the FEEL-KJ less problem-oriented action, distraction, humor enhancement, acceptance and cognitive problem solving were observed in girls who report LOC compared to those who do not report LOC. Boys with LOC report more use of adaptive strategies compared to those who do not report LOC. Discussion: LOC in adolescents is associated with increased use of maladaptive emotion regulation strategies as well as a decreased use of adaptive strategies although the latter is only specific for girls. These results may inform prevention and treatment of emotion regulation problems in adolescents with LOC.
Patient predictors of symptom and functional outcome following cognitive behaviour therapy or befriending in first-episode psychosis

BACKGROUND
Cognitive behaviour therapy (CBT) is an effective treatment for many, but not all, individuals with psychosis. An important goal is identifying individuals more likely to benefit from CBT to ensure appropriate delivery. The current study aimed to examine patient-related predictors of symptom and functional outcome following CBT and Befriending in first-episode psychosis (FEP).


METHOD
Our original randomized controlled trial compared 14 weeks of CBT (n=31) and Befriending (n=31) in FEP (Jackson et al. 2008). A series of regressions were conducted separately for each group to examine demographic, cognitive, symptoms/illness and functioning variables in predicting positive symptoms (BPRS Psychotic), negative symptoms (SANS Total) and functioning (SOFAS) at 1-year follow-up.


RESULTS
In the CBT group, higher baseline functioning (SOFAS) predicted lower levels of positive symptoms (R(2)=0.19; p=0.023), higher educational achievement and lower levels of avolition symptoms (SANS Avolition) predicted lower levels of total negative symptoms (R(2)=0.38; p=0.003), and working/studying at baseline predicted higher functional outcome (R(2)=0.35; p=0.004) at 1 year. In the Befriending group, premorbid adjustment (PAS Average) was the only variable that predicted 1-year positive symptom (R(2)=0.26; p=0.010), negative symptom (R(2)=0.35; p=0.016) and functional (R(2)=0.48; p=0.002) outcome.


CONCLUSIONS
FEP individuals with higher baseline functioning may benefit more from CBT than those with poorer functioning. Individuals with functional difficulties may benefit from alternative treatments initially, such as supported education or employment.
Structure validation by Calpha geometry: phi,psi and Cbeta deviation.

Geometrical validation around the Calpha is described, with a new Cbeta measure and updated Ramachandran plot. Deviation of the observed Cbeta atom from ideal position provides a single measure encapsulating the major structure-validation information contained in bond angle distortions. Cbeta deviation is sensitive to incompatibilities between sidechain and backbone caused by misfit conformations or inappropriate refinement restraints. A new phi,psi plot using density-dependent smoothing for 81,234 non-Gly, non-Pro, and non-prePro residues with B < 30 from 500 high-resolution proteins shows sharp boundaries at critical edges and clear delineation between large empty areas and regions that are allowed but disfavored. One such region is the gamma-turn conformation near +75 degrees,-60 degrees, counted as forbidden by common structure-validation programs; however, it occurs in well-ordered parts of good structures, it is overrepresented near functional sites, and strain is partly compensated by the gamma-turn H-bond. Favored and allowed phi,psi regions are also defined for Pro, pre-Pro, and Gly (important because Gly phi,psi angles are more permissive but less accurately determined). Details of these accurate empirical distributions are poorly predicted by previous theoretical calculations, including a region left of alpha-helix, which rates as favorable in energy yet rarely occurs. A proposed factor explaining this discrepancy is that crowding of the two-peptide NHs permits donating only a single H-bond. New calculations by Hu et al. [Proteins 2002 (this issue)] for Ala and Gly dipeptides, using mixed quantum mechanics and molecular mechanics, fit our nonrepetitive data in excellent detail. To run our geometrical evaluations on a user-uploaded file, see MOLPROBITY (http://kinemage.biochem.duke.edu) or RAMPAGE (http://www-cryst.bioc.cam.ac.uk/rampage).
On Invariance and Selectivity in Representation Learning

We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective, in the sense that two points have the same representation only if they are one the transformation of the other. The mathematical results here sharpen some of the key claims of i-theory -- a recent theory of feedforward processing in sensory cortex.
High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients

This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of the detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a Support Vector Machine (SVM) composed by features at different resolution, from coarse for the first level to fine for the last one. Considering that the spatial stride of the sliding window search is affected by the HOG features size, unlike previous methods based on Adaboost cascades, we can adopt a spatial stride inversely proportional to the features resolution. This produces that the speed-up of the cascade is not only due to the low number of features that need to be computed in the first levels, but also to the lower number of detection windows that needs to be evaluated. Experimental results shows that our method permits a detection rate comparable with the state of the art, but at the same time a gain in the speed of the detection search of 10-20 times depending on the cascade configuration.
Research on Vehicle-to-Grid Technology

Low-carbon economy brings huge development opportunity to electric vehicles. Electric vehicle is an important breakthrough. Electric vehicle potentially provides some valued services to the power grid. An electric vehicle retrofitted the power system that allows electric vehicles to feed power from the vehicle¡¯s battery packs back to the grid or to pull power from the grid to recharge the battery packs. As the light vehicle fleet moves to electric drive (hybrid, battery, and fuel cell vehicles), an opportunity opens for ¡°vehicle-to-grid¡± (V2G).V2G describes a system in which battery electricvehicles¡¢plug-in hybrid electric vehicles or fuel cell electric vehicles communicate with the power grid to provide peak power¡¢spinning reserves or regulation services. In addition, it can provide renewable energy storage and backup in the future. Thus, electric vehicles may have swift violent development owing to the tremendous economic and social values. The aim of this article is to strengthen the regulation and operation management for electric vehicles. Thus, this paper describes potential values of V2G base on the developmental scale of 2020¡¯selectric vehicle in Shanghai. The ¡°V2G¡± research contains the concept¡¢the functions¡¢the scheme ofV2G. For example, it can bring advantages to the power grid by providing spinning reserves and decreasing investment of power plants. To vehicle owners, it can bring additional revenue by selling power to the grid. Furthermore, electric vehicles typically have less air and noise pollution than internal combustion engine vehicles.
Performance of sorting algorithms on the SRC 6 reconfigurable computer

The execution speed of the FPGA processing elements are compared to the microprocessor processing elements in the SRC 6 reconfigurable computer using the following algorithms for sorting: quick sort, heap sort, radix sort, bitonic sort, and odd/even merge. The results show that, for sorting, FPGA technology may not be the best processor choice and that factors such as memory bandwidth, clock speed, algorithm computational density and an algorithm's ability to be pipelined all have an impact on FPGA performance.
Identification of a major QTL for resistance to Cercospora leaf spot disease in cowpea (Vigna unguiculata (L.) Walp.) revealed common genomic region with that for the resistance to angular leaf spot in common bean (Phaseolus vulgaris L.)

Yardlong bean (Vigna unguiculata ssp. unguiculata cv-gr. sesquipedalis) is a type of cowpea (Vigna unguiculata). It is an important vegetable legume crop of Asia. One of the problems in yardlong bean production is the devastation of Cercospora leaf spot (CLS) disease caused by the fungi Cercospora canescens and Pseudocercospora cruenta which can cause yield loss from 35 to 40 %. Breeding for resistance cultivar(s) takes a long time and yet evaluating for the resistance is difficult and laborious. The objective of this research is to locate major gene/quantitative trait locus (QTL) controlling CLS resistance in yardlong bean with the ultimate goal to identify SSR markers closely linked to the resistance gene for marker-assisted selection. An F2 population of 190 plants was developed from a cross between yardlong bean breeding line “CSR12906” (susceptible) and cowpea (Vigna unguiculata ssp. unguiculata) breeding line “IT90K-59-120” (resistant). The F2 plants and F2:3 families were evaluated for the CLS resistance caused by C. canescens and/or P. cruenta using a visual scoring method. Bulked segregant analysis (BSA) was conducted using simple sequence repeat (SSR) markers. Location of the QTL controlling the resistance was determined by inclusive composite interval mapping (ICIM). Disease evaluation revealed that the disease scores were continuously distributed, suggested that the resistance in IT90K-59-120 is a quantitative trait. BSA showed that SSR marker CEDG304 on linkage group 9 (LG9) of yardlong bean associated with the resistance. ICIM identified a major QTL, coined as qCLS9.1, for the resistance on LG9. qCLS9.1 accounted for 25.40–89.26 % of the disease score variation depending on environment and population. Linkage and genome comparison revealed that qCLS9.1 is likely the same as QTL ALS10.1 conferring resistance to angular leaf spot disease caused by P. cruenta in common bean (Phaseolus vulgaris).
Comparison of stability of two kinds of sacro-iliac screws in the fixation of bilateral sacral fractures in a finite element model.

OBJECTIVE
To compare the stability of lengthened sacro-iliac screw and sacro-iliac screw for the treatment of bilateral vertical sacral fractures to provide reference for clinical application.


METHODS
A finite element model of Tile C pelvic ring injury (bilateral type Denis II fracture of sacrum) was produced. (Tile and Denis are surgeons, who put forward the classifications of pelvic ring injury and sacral fracture respectively.) The bilateral sacral fractures were fixed with a lengthened sacro-iliac screw and a sacro-iliac screw in seven types of models, respectively. The translation and angular displacement of the superior surface of the sacrum in the case of standing on both feet were measured and compared.


RESULTS
The stability of one lengthened sacro-iliac screw fixation in the S1 or S2 segment is superior to that of two bidirectional sacro-iliac screws in the same sacral segment; the stability of one lengthened sacro-iliac screw fixation in S1 and S2 segments, respectively, is superior to that of two bidirectional sacro-iliac screw fixation in S1 and S2 segments, respectively; the stability of one lengthened sacro-iliac screw fixation in S1 and S2 segments, respectively, is superior to that of one lengthened sacro-iliac screw fixation in the S1 or S2 segment; the stability of two bidirectional sacro-iliac screw fixation in S1 and S2 segments, respectively, is markedly superior to that of two bidirectional sacro-iliac screw fixation in the S1 or S2 segment and is also markedly superior to that of one sacro-iliac screw fixation in the S1 segment and one sacro-iliac screw fixation in the S2 segment; the vertical stability of the lengthened sacro-iliac screw or the sacro-iliac screw fixation in S2 is superior to that of S1. The rotational stability of the lengthened sacro-iliac screw or sacro-iliac screw fixation in S1 is superior to that of S2.


CONCLUSION
S1 and S2 lengthened sacro-iliac screws should be used for the fixation in bilateral sacral fractures of Tile C pelvic ring injury as far as possible and the most stable fixation is the combination of the lengthened sacro-iliac screws of S1 and S2 segments. Even if lengthened sacro-iliac screws cannot be used due to limited conditions, two bidirectional sacro-iliac screw fixation in S1 and S2 segments, respectively, is recommended. No matter which kind of sacro-iliac screw is applied, the fixation combination of S1 and S2 segments is strongly recommended to maximise the stability of the pelvic posterior ring.
Review of proton conductors for hydrogen separation

There is a global push to develop a range of hydrogen technologies for timely adoption of the hydrogen economy. This is critical in view of the depleting oil reserves and looming transport fuel shortage, global warming, and increasing pollution. Molecular hydrogen (H2) can be generated by a number of renewable and fossil-fuel-based resources. However, given the high cost of H2 generation by renewable energy at this stage, fossil or carbon fuels are likely to meet the shortto medium-term demand for hydrogen. In view of this, effective technologies are required for the separation of H2 from a gas feed (by-products of coal or bio-mass gasification plants, or gases from fossil fuel partial oxidation or reforming) consisting mainly of H2 and CO2 with small quantities of other gases such as CH4, CO, H2O, and traces of sulphur compounds. Several technologies are under development for hydrogen separation. One such technology is based on ion transport membranes, which conduct protons or both protons and electrons. Although these materials have been considered for other applications, such as gas sensors, fuel cells and water electrolysis, the interest in their use as gas separation membranes has developed only recently. In this paper, various classes of proton-conducting materials have been reviewed with specific emphasis on their potential use as H2 separation membranes in the industrial processes of coal gasification, natural gas reforming, methanol reforming and the water– gas shift (WGS) reaction. Keymaterial requirements for their use in these applications have been discussed.
Smile detection in the wild with deep convolutional neural networks

Smile or happiness is one of the most universal facial expressions in our daily life. Smile detection in the wild is an important and challenging problem, which has attracted a growing attention from affective computing community. In this paper, we present an efficient approach for smile detection in the wild with deep learning. Different from some previous work which extracted hand-crafted features from face images and trained a classifier to perform smile recognition in a two-step approach, deep learning can effectively combine feature learning and classification into a single model. In this study, we apply the deep convolutional network, a popular deep learning model, to handle this problem. We construct a deep convolutional network called Smile-CNN to perform feature learning and smile detection simultaneously. Experimental results demonstrate that although a deep learning model is generally developed for tackling “big data,” the model can also effectively deal with “small data.” We further investigate into the discriminative power of the learned features, which are taken from the neuron activations of the last hidden layer of our Smile-CNN. By using the learned features to train an SVM or AdaBoost classifier, we show that the learned features have impressive discriminative ability. Experiments conducted on the GENKI4K database demonstrate that our approach can achieve a promising performance in smile detection.
Combating the Cold Start User Problem in Model Based Collaborative Filtering

For tackling thewell known cold-start user problem inmodel-based recommender systems, one approach is to recommend a few items to a cold-start user and use the feedback to learn a prole. e learned prole can then be used to make good recommendations to the cold user. In the absence of a good initial prole, the recommendations are like random probes, but if not chosen judiciously, both bad recommendations and too many recommendations may turn o a user. We formalize the cold-start user problem by asking what are the b best items we should recommend to a cold-start user, in order to learn her prole most accurately, where b , a given budget, is typically a small number. We formalize the problem as an optimization problem and present multiple non-trivial results, including NP-hardness as well as hardness of approximation. We furthermore show that the objective function, i.e., the least square error of the learned prolew.r.t. the true user prole, is neither submodular nor supermodular, suggesting ecient approximations are unlikely to exist. Finally, we discuss several scalable heuristic approaches for identifying the b best items to recommend to the user and experimentally evaluate their performance on 4 real datasets. Our experiments show that our proposed accelerated algorithms signicantly outperform the prior art in runnning time, while achieving similar error in the learned user prole as well as in the rating predictions. ACM Reference format: Sampoorna Biswas, Laks V.S. Lakshmanan, and Senjuti Basu Ray. 2016. Combating the Cold Start User Problem in Model Based Collaborative Filtering. In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 11 pages. DOI: 10.1145/nnnnnnn.nnnnnnn
Finding Connected Components in O(log n log log n) Time on the EREW PRAM

In this paper we present a new parallel algorithm for finding the connected components of an undirected graph. On a graph with n vertices and m edges, the algorithm runs in O(log n loglog n) time using n + m processors on an EREW (exclusive-read and exclusive-write) PRAM. Prior to our work, the best known exclusive-write algorithm for this problem requires O(log’.5 n) time using n+m processors [JM91, KNP92].