nils-herrmann/clean_scirepeval huggingface dataset:
Bistatic backscatter radio for power-limited sensor networks

For applications that require large numbers of wireless sensors spread in a field, backscatter radio can be utilized to minimize the monetary and energy cost of each sensor. Commercial backscatter systems such as those in radio frequency identification (RFID), utilize modulation designed for the bandwidth limited regime, and require medium access control (MAC) protocols for multiple access. High tag/sensor bitrate and monostatic reader architectures result in communication range reduction. In sharp contrast, sensing applications typically require the opposite: extended communication ranges that could be achieved with bitrate reduction and bistatic reader architectures. This work presents non-coherent frequency shift keying (FSK) for bistatic backscatter radio; FSK is appropriate for the power limited regime and also allows many RF tags/sensors to convey information to a central reader simultaneously with simple frequency division multiplexing (FDM). However, classic non-coherent FSK receivers are not directly applicable in bistatic backscatter radio. This work a) carefully derives the complete signal model for bistatic backscatter radio, b) describes the details of backscatter modulation with emphasis on FSK and its corresponding receiver, c) proposes techniques to overcome the difficulties introduced by the utilization of bistatic architectures, such as the carrier frequency offset (CFO), and d) presents bit error rate (BER) performance for the proposed receiver and carrier recovery techniques.
Clinical applications of venous flaps in the reconstruction of hands and fingers

In recent years, the venous flap has been highly regarded in microsurgical and reconstructive surgeries, especially in the reconstruction of hand and digit injuries. It is easily designed and harvested with good quality. It is thin and pliable, without the need of sacrificing a major artery at the donor site, and has no limitation on the donor site. It can be transferred not only as a pure skin flap, but also as a composite flap including tendons and nerves as well as vein grafts. All these advantages make it an optimal candidate for hand and digit reconstruction when conventional flaps are limited or unavailable. In this article, we review its classifications and the selection of donor sites, update its clinical applications, and summarize its indications for all types of venous flaps in hand and digit reconstruction.
Turntable-based 3D object reconstruction

In this paper, we present a system that can acquire graphical models from real objects. Given an image sequence of a complex shape object placed on a turntable, the presented algorithm generates automatically the 3D model. In contrast to previous approaches, the technique described here is only based on conies properties and uses the spatiotemporal aspect of the sequence of images. From the projective properties of the conies and using the camera calibration parameters the Euclidean 3D coordinates of a point are obtained from the geometric locus of the image points trajectories. An algorithm has been implemented to compute the 3D reconstruction automatically. Examples on both synthetic and real image sequences are presented
Three-Dimensional 128 Gb MLC Vertical nand Flash Memory With 24-WL Stacked Layers and 50 MB/s High-Speed Programming

In this work, we present a true 3D 128 Gb 2 bit/cell vertical-NAND (V-NAND) Flash product for the first time. The use of barrier-engineered materials and gate all-around structure in the 3D V-NAND cell exhibits advantages over 1 × nm planar NAND, such as small Vth shift due to small cell coupling and narrow natural Vth distribution. Also, a negative counter-pulse scheme realizes a tightly programmed cell distribution. In order to reduce the effect of a large WL coupling, a glitch-canceling discharge scheme and a pre-offset control scheme is implemented. Furthermore, an external high-voltage supply scheme along with the proper protection scheme for a high-voltage failure is used to achieve low power consumption. The chip accomplishes 50 MB/s write throughput with 3 K endurance for typical embedded applications. Also, extended endurance of 35 K is achieved with 36 MB/s of write throughput for data center and enterprise SSD applications.
Vehicle Re-identification Using Quadruple Directional Deep Learning Features

In order to resist the adverse effect of viewpoint variations for improving vehicle re-identification performance, we design quadruple directional deep learning networks to extract quadruple directional deep learning features (QD-DLF) of vehicle images. The quadruple directional deep learning networks are with similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers. Specifically, the same basic deep learning architecture is a shortly and densely connected convolutional neural network to extract basic feature maps of an input square vehicle image in the first stage. Then, the quadruple directional deep learning networks utilize different directional pooling layers, i.e., horizontal average pooling (HAP) layer, vertical average pooling (VAP) layer, diagonal average pooling (DAP) layer and anti-diagonal average pooling (AAP) layer, to compress the basic feature maps into horizontal, vertical, diagonal and anti-diagonal directional feature maps, respectively. Finally, these directional feature maps are spatially normalized and concatenated together as a quadruple directional deep learning feature for vehicle re-identification. Extensive experiments on both VeRi and VehicleID databases show that the proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods.
Importance of sediments in understanding nutrient cyclings in lakes

Inorganic and organic nutrients are continuously transported to lake bottoms by sedimentation. By various biological, physical, chemical and mechanical processes quantities of certain nutrients can be brought back to the free water again. This cycling between the sediments and water may occur according to various schemes dependent on lake type and bottom conditions. Lake morphology, temperature regimes, trophic level and sediment type can all strongly influence the size of nutrient pools and rates of turnover. The various activities of bacteria, benthic algae, macrophytes, benthic invertebrates and fish, in conjunction with influences of temperature, pH-values, Eh-values, water content, organic matter and elemental sediment composition, lead to the extremely complex nature of nutrient cycling. Three essential components of aquatic ecosystems are discussed, namely carbon, nitrogen and phosphorus. The objective of this paper is to illustrate in condensed form the heterogeneous nature of nutrient cycling processes. In addition, the importance of sediments in understanding nutrient cycling is discussed from a water management perspective.
Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso)

The problem of consistently estimating the sparsity pattern of a vector beta* isin Rp based on observations contaminated by noise arises in various contexts, including signal denoising, sparse approximation, compressed sensing, and model selection. We analyze the behavior of l1-constrained quadratic programming (QP), also referred to as the Lasso, for recovering the sparsity pattern. Our main result is to establish precise conditions on the problem dimension p, the number k of nonzero elements in beta*, and the number of observations n that are necessary and sufficient for sparsity pattern recovery using the Lasso. We first analyze the case of observations made using deterministic design matrices and sub-Gaussian additive noise, and provide sufficient conditions for support recovery and linfin-error bounds, as well as results showing the necessity of incoherence and bounds on the minimum value. We then turn to the case of random designs, in which each row of the design is drawn from a N (0, Sigma) ensemble. For a broad class of Gaussian ensembles satisfying mutual incoherence conditions, we compute explicit values of thresholds 0 < thetasl(Sigma) les thetasu(Sigma) < +infin with the following properties: for any delta > 0, if n > 2 (thetasu + delta) klog (p- k), then the Lasso succeeds in recovering the sparsity pattern with probability converging to one for large problems, whereas for n < 2 (thetasl - delta)klog (p - k), then the probability of successful recovery converges to zero. For the special case of the uniform Gaussian ensemble (Sigma = Iptimesp), we show that thetasl = thetas<u = 1, so that the precise threshold n = 2 klog(p- k) is exactly determined.
Image in Painting Techniques : A survey

Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e. image Inpainting fills the missing or damaged region in an image utilizing spatial information of its neighbouring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. It is also applied to red-eye correction, super resolution, compression etc. The main goal of the Inpainting algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. There have been several approaches proposed for the image inpainting. This proposed work presents a brief survey of different image inpainting techniques and comparative study of these techniques. In this paper we provide a review of different techniques used for image Inpainting. We discuss different inpainting techniques like image PDE based image inpainting, Exemplar based image inpainting, hybrid inpainting, and texture synthesis based image inpainting and semi-automatic and fast digital Inpainting.
Clinical practice guidelines for the sustained use of sedatives and analgesics in the critically ill adult.

Judith Jacobi, PharmD, FCCM, BCPS; Gilles L. Fraser, PharmD, FCCM; Douglas B. Coursin, MD; Richard R. Riker, MD; Dorrie Fontaine, RN, DNSc, FAAN; Eric T. Wittbrodt,  Michael F. Masica, MD, MPH; H. Scott Bjerke, MD; William M. Coplin, MD; David W. Crippen, MD, FCCM; Barry D. Fuchs, MD; Ruth M. Kelleher, RN; Paul E. Marik, MDBCh, FCCM; Stanley A. Nasraway, Jr, MD, FCCM; Michael J. Murray, MD, PhD, FCCM; William T. Peruzzi, MD, FCCM; Philip D. Lumb, MB, BS, FCCM. Developed through the Task Force of the American College of Critical Care Medicine (ACCM) of the Society of Critical Care Medicine (SCCM), in collaboration with the American Society of Health-System Pharmacists (ASHP), and in alliance with the American College of Chest Physicians; and approved by the Board of Regents of ACCM and the Council of SCCM and the ASHP Board of Directors
CyberMath: A Shared Virtual Environment for Mathematics Exploration

We present CyberMath, an avatar-based shared virtual environment aimed at improving the current problematic situation in mathematics education. CyberMath allows mathematics to be presented in a new and exciting way and is suitable for exploring and teaching mathematics in situations both where teacher and students are co-present and physically separated. CyberMath also supports a variety of teaching styles, ranging from traditional one-tomany lectures to teacher-supported interaction and individual off-line exploration. We also summarize the results of two initial usability studies of our system.
Institutional Repository Predicting Corporate Bankruptcy : whither do we stand ?

Following triggered corporate bankruptcies, an increasing number of prediction models have emerged since 1960s. This study provides a critical analysis of methodologies and empirical findings of applications of these models across 10 different countries. The study’s empirical exercise finds that predictive accuracies of different corporate bankruptcy prediction models are, generally, comparable. Artificially Intelligent Expert System (AIES) models perform marginally better than statistical and theoretical models. Overall, use of Multiple Discriminant Analysis (MDA) dominates the research followed by logit models. Study deduces useful observations and recommendations for future research in this field.
Cold-Start Item and User Recommendation with Decoupled Completion and Transduction

A major challenge in collaborative filtering based recommender systems is how to provide recommendations when rating data is sparse or entirely missing for a subset of users or items, commonly known as the cold-start problem. In recent years, there has been considerable interest in developing new solutions that address the cold-start problem. These solutions are mainly based on the idea of exploiting other sources of information to compensate for the lack of rating data. In this paper, we propose a novel algorithmic framework based on matrix factorization that simultaneously exploits the similarity information among users and items to alleviate the cold-start problem. In contrast to existing methods, the proposed algorithm decouples the following two aspects of the cold-start problem: (a) the completion of a rating sub-matrix, which is generated by excluding cold-start users and items from the original rating matrix; and (b) the transduction of knowledge from existing ratings to cold-start items/users using side information. This crucial difference significantly boosts the performance when appropriate side information is incorporated. We provide theoretical guarantees on the estimation error of the proposed two-stage algorithm based on the richness of similarity information in capturing the rating data. To the best of our knowledge, this is the first algorithm that addresses the cold-start problem with provable guarantees. We also conduct thorough experiments on synthetic and real datasets that demonstrate the effectiveness of the proposed algorithm and highlights the usefulness of auxiliary information in dealing with both cold-start users and items.
Lipoedema: from clinical presentation to therapy. A review of the literature.

Lipoedema is an infrequently recognized disorder in women. Lipoedema is characterized by bilateral enlargement of the legs due to abnormal depositions of subcutaneous fat associated with often mild oedema. There is substantial variability in disease severity. The diagnosis should be made as early as possible to prevent complications of the disorder, which is associated with increasing functional and cosmetic morbidity. This review describes clinical manifestations, pathogenesis, technical investigations, management and therapies of lipoedema, with the aim of optimizing management and care of patients with lipoedema.
The Hippo pathway effector Yki downregulates Wg signaling to promote retinal differentiation in the Drosophila eye.

The evolutionarily conserved Hippo signaling pathway is known to regulate cell proliferation and maintain tissue homeostasis during development. We found that activation of Yorkie (Yki), the effector of the Hippo signaling pathway, causes separable effects on growth and differentiation of the Drosophila eye. We present evidence supporting a role for Yki in suppressing eye fate by downregulation of the core retinal determination genes. Other upstream regulators of the Hippo pathway mediate this effect of Yki on retinal differentiation. Here, we show that, in the developing eye, Yki can prevent retinal differentiation by blocking morphogenetic furrow (MF) progression and R8 specification. The inhibition of MF progression is due to ectopic induction of Wingless (Wg) signaling and Homothorax (Hth), the negative regulators of eye development. Modulating Wg signaling can modify Yki-mediated suppression of eye fate. Furthermore, ectopic Hth induction due to Yki activation in the eye is dependent on Wg. Last, using Cut (Ct), a marker for the antennal fate, we show that suppression of eye fate by hyperactivation of yki does not change the cell fate (from eye to antenna-specific fate). In summary, we provide the genetic mechanism by which yki plays a role in cell fate specification and differentiation - a novel aspect of Yki function that is emerging from multiple model organisms.
Smart energy metering and power theft control using arduino & GSM

Energy theft is a very common problem in countries like India where consumers of energy are increasing consistently as the population increases. Utilities in electricity system are destroying the amounts of revenue each year due to energy theft. The newly designed AMR used for energy measurements reveal the concept and working of new automated power metering system but this increased the Electricity theft forms administrative losses because of not regular interval checkout at the consumer's residence. It is quite impossible to check and solve out theft by going every customer's door to door. In this paper, a new procedure is followed based on MICROCONTROLLER Atmega328P to detect and control the energy meter from power theft and solve it by remotely disconnect and reconnecting the service (line) of a particular consumer. An SMS will be sent automatically to the utility central server through GSM module whenever unauthorized activities detected and a separate message will send back to the microcontroller in order to disconnect the unauthorized supply. A unique method is implemented by interspersed the GSM feature into smart meters with Solid state relay to deal with the non-technical losses, billing difficulties, and voltage fluctuation complication.
The antitumor action of cannabinoids on glioma tumorigenesis.

Cannabinoids are a class of chemical compounds with a wide spectrum of pharmacological effects, mediated by two specific plasma membrane receptors (CB1 and CB2). Recently, CB1 and CB2 expression levels have been detected in human tumors, including those of brain. Cannabinoids-endocannabinoids exert anti-inflammatory, anti-proliferative, anti-invasive, anti-metastatic and pro-apoptotic effects in different cancer types, both in vitro and in vivo in animal models, after local or systemic administration. We present the available experimental and clinical data, to date, regarding the antitumor action of cannabinoids on the tumorigenesis of gliomas.
Algorithmic approach to lower lid blepharoplasty.

The debate continues around transconjunctival versus transcutaneous approaches. Despite the perceived safety of the former, many experienced surgeons continue to advocate the latter. This review aims to present a balanced view of each approach. It will first address the anatomic basis of lower lid aging and then organize recent literature and associated discussion into the transconjunctival and transcutaneous approaches. The integrated algorithm employed by the senior author will be presented. Finally this review will describe less mainstream suture techniques for lower lid rejuvenation and lower lid blepharoplasty complications with a focus upon lower lid malposition.
A new type of microstrip band-stop filter using spurline

In this paper, A wide bandwidth micro-strip band-stop filter (BSF) is presented. This filter combines two traditional BSFs: stepped impedance resonator and spuline. A micro-strip line is used as the main line, then spurline is used to realize filtering some frequency. For efficiently reduce the size of the filter, the spurline was designed as SIR structure. 1/4 wavelength SIR resonator can be equivalent as the capacitor and inductance on the basic principle of band-stop filter. Due to the inherently compact characteristics of the spurline, the proposed filter shows a better rejection performance than open-stub. From 3.6 GHz to 4.4 GHz, the proposed filter has a better rejection with S21 is less than 20dB and the maximum rejection level of 80dB. The stop bandwidth is 20% and its spurious stop band is high than 13 GHz, more than 3 times as high as the center frequency, the SWR is less than 1.5.
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically generate such strokes, we propose to model a brush as a reinforcement learning agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also elaborate on the design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.
Robust visual tracking using autoregressive hidden Markov Model

Recent studies on visual tracking have shown significant improvement in accuracy by handling the appearance variations of the target object. Whereas most studies present schemes to extract the time-invariant characteristics of the target and adaptively update the appearance model, the present paper concentrates on modeling the probabilistic dependency between sequential target appearances . To actualize this interest, a new Bayesian tracking framework is formulated under the autoregressive Hidden Markov Model (AR-HMM), where the probabilistic dependency between sequential target appearances is implied. During the learning phase at each time step, the proposed tracker separates formerly seen target samples into several clusters based on their visual similarity, and learns cluster-specific classifiers as multiple appearance models, each of which represents a certain type of the target appearance. Then the dependency between these appearance models is learned. During the searching phase, the target state is estimated by inferring the most probable appearance model under the consideration of its dependency on formerly utilized appearance models. The proposed method is tested on 12 challenging video sequences containing targets with abrupt appearance variations, and demonstrates that it outperforms current state-of-the-art methods in accuracy.