nils-herrmann/clean_scirepeval huggingface dataset:
Low-Level Laser Therapy at 635 nm for Treatment of Chronic Plantar Fasciitis: A Placebo-Controlled, Randomized Study.

Plantar fasciitis affects nearly 1 million persons in the United States at any one time. Conservative therapies have been reported to successfully treat 90% of plantar fasciitis cases; however, for the remaining cases, only invasive therapeutic solutions remain. This investigation studied newly emerging technology, low-level laser therapy. From September 2011 to June 2013, 69 subjects were enrolled in a placebo-controlled, randomized, double-blind, multicenter study that evaluated the clinical utility of low-level laser therapy for the treatment of unilateral chronic fasciitis. The volunteer participants were treated twice a week for 3 weeks for a total of 6 treatments and were evaluated at 5 separate time points: before the procedure and at weeks 1, 2, 3, 6, and 8. The pain rating was recorded using a visual analog scale, with 0 representing "no pain" and 100 representing "worst pain." Additionally, Doppler ultrasonography was performed on the plantar fascia to measure the fascial thickness before and after treatment. Study participants also completed the Foot Function Index. At the final follow-up visit, the group participants demonstrated a mean improvement in heel pain with a visual analog scale score of 29.6 ± 24.9 compared with the placebo subjects, who reported a mean improvement of 5.4 ± 16.0, a statistically significant difference (p < .001). Although additional studies are warranted, these data have demonstrated that low-level laser therapy is a promising treatment of plantar fasciitis.
Maximum Expected Likelihood Estimation for Zero-resource Neural Machine Translation

While neural machine translation (NMT) has made remarkable progress in translating a handful of resource-rich language pairs recently, parallel corpora are not always readily available for most language pairs. To deal with this problem, we propose an approach to zero-resource NMT via maximum expected likelihood estimation. The basic idea is to maximize the expectation with respect to a pivot-to-source translation model for the intended source-to-target model on a pivot-target parallel corpus. To approximate the expectation, we propose two methods to connect the pivot-to-source and source-to-target models. Experiments on two zero-resource language pairs show that the proposed approach yields substantial gains over baseline methods. We also observe that when trained jointly with the source-to-target model, the pivotto-source translation model also obtains improvements over independent training.
Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection

Due to a major shortage of nurses in the U.S., future healthcare service robots are expected to be used in tasks involving direct interaction with patients. Consequently, there is a need to design nursing robots with the capability to detect and respond to patient emotional states and to facilitate positive experiences in healthcare. The objective of this study was to develop a new computational algorithm for accurate patient emotional state classification in interaction with nursing robots during medical service. A simulated medicine delivery experiment was conducted at two nursing homes using a robot with different human-like features. Physiological signals, including heart rate (HR) and galvanic skin response (GSR), as well as subjective ratings of valence (happy-unhappy) and arousal (excited-bored) were collected on elderly residents. A three-stage emotional state classification algorithm was applied to these data, including: (1) physiological feature extraction; (2) statistical-based feature selection; and (3) a machine-learning model of emotional states. A pre-processed HR signal was used. GSR signals were nonstationary and noisy and were further processed using wavelet analysis. A set of wavelet coefficients, representing GSR features, was used as a basis for current emotional state classification. Arousal and valence were significantly explained by statistical features of the HR signal and GSR wavelet features. Wavelet-based de-noising of GSR signals led to an increase in the percentage of correct classifications of emotional states and clearer relationships among the physiological response and arousal and valence. The new algorithm may serve as an effective method for future service robot real-time detection of patient emotional states and behavior adaptation to promote positive healthcare experiences.
Experimental study on reducing cogging torque and no-load power loss in axial-flux permanent-magnet machines with slotted winding

The axial-flux permanent-magnet machine (AFPM) topology is suited for direct-drive applications and, due to their enhanced flux-weakening capability, AFPMs having slotted windings are the most promising candidates for use in wheel-motor drives. In consideration of this, this paper deals with an experimental study devoted to investigate a number of technical solutions to be used in AFPMs having slotted windings in order to achieve substantial reduction of both cogging torque and no-load power loss in the machine. To conduct such an experimental study, a laboratory machine was purposely built incorporating facilities that allow easy-to-achieve offline modifications of the overall magnetic arrangement at the machine air gaps, such as magnet skewing, angular shifting between rotor discs, and accommodation of either PVC or Somaloy wedges for closing the slot openings. The paper discusses experimental results and gives guidelines for the design of AFPMs with improved performance.
Clique Partitions, Graph Compression, and Speeding-Up Algorithms

We first consider the problem of partitioning the edges of a graph ~ into bipartite cliques such that the total order of the cliques is minimized, where the order of a clique is the number of vertices in it. It is shown that the problem is NP-complete. We then prove the existence of a partition of small total order in a sufficiently dense graph and devise an efilcient algorithm to compute such a partition. It turns out that our algorithm exhibits a trade-off between the total order of the partition and the running time. Next, we define the notion of a compression of a graph ~ and use the result on graph partitioning to efficiently compute an optimal compression for graphs of a given size. An interesting application of the graph compression result arises from the fact that several graph algorithms can be adapted to work with the compressed rep~esentation of the input graph, thereby improving the bound on their running times particularly on dense graphs. This makes use of the trade-off result we obtain from our partitioning algorithm. The algorithms analyzed include those for matchings, vertex connectivity, edge connectivity and shortest paths. In each case, we improve upon the running times of the best-known algorithms for these problems.
Fe b 20 04 A Diquark-Triquark Model for the KN Pentaquark

We propose a model for the recently discovered Θ+ exotic KN resonance as a novel kind of a pentaquark with an unusual color structure: a 3c ud diquark, coupled to 3c uds̄ triquark in a relative P -wave. The state has J P = 1/2+, I = 0 and is an antidecuplet of SU(3)f . A rough mass estimate of this pentaquark is close to experiment.
A 40-Gb/s 14-mW CMOS Wireline Receiver

A 40-Gb/s receiver includes a continuous-time linear equalizer, a discrete-time linear equalizer, a two-tap decision-feedback equalizer, a clock and data recovery circuit, and a one-to-four deserializer. Hardware minimization and charge steering techniques are extensively used to reduce the power consumption by a factor of ten. Fabricated in 45-nm CMOS technology, the receiver exhibits a bathtub curve opening of 0.28 UI with a recovered clock jitter of 0.5 psrms.
Mobile Robot Navigation and Obstacle Avoidance Techniques : A Review

Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments.
0.7 V supply self-biased nanoWatt MOS-only threshold voltage monitor

This work presents a self-biased MOSFET threshold voltage VT0 monitor. The threshold condition is defined based on a current-voltage relationship derived from a continuous physical model. The model is valid for any operating condition, from weak to strong inversion, and under triode or saturation regimes. The circuit consists in balancing two self-cascode cells operating at different inversion levels, where one of the transistors that compose these cells is biased at the threshold condition. The circuit is MOSFET-only (can be implemented in any standard digital process), and it operates with a power supply of less than 1 V, consuming tenths of nW. We propose a process independent design methodology, evaluating different trade-offs of accuracy, area and power consumption. Schematic simulation results, including Monte Carlo variability analysis, support the VT0 monitoring behavior of the circuit with good accuracy on a 180 nm process.
Vectorization of Line Drawings via Polyvector Fields

Image tracing is a foundational component of the workflow in graphic design, engineering, and computer animation, linking hand-drawn concept images to collections of smooth curves needed for geometry processing and editing. Even for clean line drawings, modern algorithms often fail to faithfully vectorize junctions, or points at which curves meet; this produces vector drawings with incorrect connectivity. This subtle issue undermines the practical application of vectorization tools and accounts for hesitance among artists and engineers to use automatic vectorization software. To address this issue, we propose a novel image vectorization method based on state-of-the-art mathematical algorithms for frame field processing. Our algorithm is tailored specifically to disambiguate junctions without sacrificing quality.
Game User Experience Evaluation

In a nutshell: This course comprehensively covers important user experience (UX) evaluation methods as well as opportunities and challenges of UX evaluation in the area of entertainment and games. The course is an ideal forum for attendees to gain insight into state-of-the art user experience evaluation methods going way-beyond standard usability and user experience evaluation approaches in the area of human-computer interaction. It surveys and assesses the efforts of user experience evaluation of the gaming and human computer interaction communities during the last 15 years.
Adolescent Initiation of Cannabis Use and Early-Onset Psychosis.

It is important to evaluate the impact of cannabis use on onset and course of psychotic illness, as the increasing number of novice cannabis users may translate into a greater public health burden. This study aims to examine the relationship between adolescent onset of regular marijuana use and age of onset of prodromal symptoms, or first episode psychosis, and the manifestation of psychotic symptoms in those adolescents who use cannabis regularly.



A review was conducted of the current literature for youth who initiated cannabis use prior to the age of 18 and experienced psychotic symptoms at, or prior to, the age of 25. Seventeen studies met eligibility criteria and were included in this review.



The current weight of evidence supports the hypothesis that early initiation of cannabis use increases the risk of early onset psychotic disorder, especially for those with a preexisting vulnerability and who have greater severity of use. There is also a dose-response association between cannabis use and symptoms, such that those who use more tend to experience greater number and severity of prodromal and diagnostic psychotic symptoms. Those with early-onset psychotic disorder and comorbid cannabis use show a poorer course of illness in regards to psychotic symptoms, treatment, and functional outcomes. However, those with early initiation of cannabis use appear to show a higher level of social functioning than non-cannabis users.



Adolescent initiation of cannabis use is associated, in a dose-dependent fashion, with emergence and severity of psychotic symptoms and functional impairment such that those who initiate use earlier and use at higher frequencies demonstrate poorer illness and treatment outcomes. These associations appear more robust for adolescents at high risk for developing a psychotic disorder.
MAESTRO: An Open-source Infrastructure for Modeling Dataflows within Deep Learning Accelerators

We present MAESTRO, a framework to describe and analyze CNN dataflows, and predict performance and energy-efficiency when running neural network layers across various hardware configurations. This includes two components: (i) a concise language to describe arbitrary dataflows and (ii) and analysis framework that accepts the dataflow description, hardware resource description, and DNN layer description as inputs and generates buffer requirements, buffer access counts, network-on-chip (NoC) bandwidth requirements, and roofline performance information. We demonstrate both components across several dataflows as case studies.
Relaxed Cone Stepping for Relief Mapping

The presence of geometric details on object surfaces dramatically changes the way light interacts with these surfaces. Although synthesizing realistic pictures requires simulating this interaction as faithfully as possible, explicitly modeling all the small details tends to be impractical. To address these issues, an image-based technique called relief mapping has recently been introduced for adding per-fragment details onto arbitrary polygonal models (Policarpo et al. 2005). The technique has been further extended to render correct silhouettes (Oliveira and Policarpo 2005) and to handle non-height-field surface details (Policarpo and Oliveira 2006). In all its variations, the ray-height-field intersection is performed using a binary search, which refines the result produced by some linear search procedure. While the binary search converges very fast, the linear search (required to avoid missing large structures) is prone to aliasing, by possibly missing some thin structures, as is evident in Figure 18-1a. Several space-leaping techniques have since been proposed to accelerate the ray-height-field intersection and to minimize the occurrence of aliasing (Donnelly 2005, Dummer 2006, Baboud and Décoret 2006). Cone step mapping (CSM) (Dummer 2006) provides a clever solution to accelerate the intersection calculation for the average case and avoids skipping height-field structures by using some precomputed data (a cone map). However, because CSM uses a conservative approach, the rays tend to stop before the actual surface, which introduces different Relaxed Cone Stepping for Relief Mapping
Transmission gate-based approximate adders for inexact computing

Power dissipation has become a significant concern for integrated circuit design in nanometric CMOS technology. To reduce power consumption, approximate implementations of a circuit have been considered as a potential solution for applications in which strict exactness is not required. In approximate computing, power reduction is achieved through the relaxation of the often demanding requirement of accuracy. In this paper, new approximate adders are proposed for low-power imprecise applications by using logic reduction at the gate level as an approach to relaxing numerical accuracy. Transmission gates are utilized in the designs of two approximate full adders with reduced complexity. A further positive feature of the proposed designs is the reduction of the critical path delay. The approximate adders show advantages in terms of power dissipation over accurate and recently proposed approximate adders. An image processing application is presented using the proposed approximate adders to evaluate the efficiency in power and delay at application level.
Bridging Physical and Virtual Worlds: Complex Event Processing for RFID Data Streams

Advances of sensor and RFID technology provide significant new power for humans to sense, understand and manage the world. RFID provides fast data collection with precise identification of objects with unique IDs without line of sight, thus it can be used for identifying, locating, tracking and monitoring physical objects. Despite these benefits, RFID poses many challenges for data processing and management: i) RFID observations contain duplicates, which have to be filtered; ii) RFID observations have implicit meanings, which have to be transformed and aggregated into semantic data represented in their data models; and iii) RFID data are temporal, streaming, and in high volume, and have to be processed on the fly. Thus, a general RFID data processing framework is needed to automate the transformation of physical RFID observations into the virtual counterparts in the virtual world linked to business applications. In this paper, we take an event-oriented approach to process RFID data, by devising RFID application logic into complex events. We then formalize the specification and semantics of RFID events and rules. We demonstrate that traditional ECA event engine cannot be used to support highly temporally constrained RFID events, and develop an RFID event detection engine that can effectively process complex RFID events. The declarative event-based approach greatly simplifies the work of RFID data processing, and significantly reduces the cost of RFID data integration.
Weakly Supervised Semantic Segmentation Using Web-Crawled Videos

We propose a novel algorithm for weakly supervised semantic segmentation based on image-level class labels only. In weakly supervised setting, it is commonly observed that trained model overly focuses on discriminative parts rather than the entire object area. Our goal is to overcome this limitation with no additional human intervention by retrieving videos relevant to target class labels from web repository, and generating segmentation labels from the retrieved videos to simulate strong supervision for semantic segmentation. During this process, we take advantage of image classification with discriminative localization technique to reject false alarms in retrieved videos and identify relevant spatio-temporal volumes within retrieved videos. Although the entire procedure does not require any additional supervision, the segmentation annotations obtained from videos are sufficiently strong to learn a model for semantic segmentation. The proposed algorithm substantially outperforms existing methods based on the same level of supervision and is even as competitive as the approaches relying on extra annotations.
Synthesis of Oil-Laden Poly(ethylene glycol) Diacrylate Hydrogel Nanocapsules from Double Nanoemulsions.

Multiple emulsions have received great interest due to their ability to be used as templates for the production of multicompartment particles for a variety of applications. However, scaling these complex droplets to nanoscale dimensions has been a challenge due to limitations on their fabrication methods. Here, we report the development of oil-in-water-in-oil (O1/W/O2) double nanoemulsions via a two-step high-energy method and their use as templates for complex nanogels comprised of inner oil droplets encapsulated within a hydrogel matrix. Using a combination of characterization methods, we determine how the properties of the nanogels are controlled by the size, stability, internal morphology, and chemical composition of the nanoemulsion templates from which they are formed. This allows for identification of compositional and emulsification parameters that can be used to optimize the size and oil encapsulation efficiency of the nanogels. Our templating method produces oil-laden nanogels with high oil encapsulation efficiencies and average diameters of 200-300 nm. In addition, we demonstrate the versatility of the system by varying the types of inner oil, the hydrogel chemistry, the amount of inner oil, and the hydrogel network cross-link density. These nontoxic oil-laden nanogels have potential applications in food, pharmaceutical, and cosmetic formulations.
Detecting anomalous events in videos by learning deep representations of appearance and motion

Anomalous event detection is of utmost importance in intelligent video surveillance. Currently, most approaches for the automatic analysis of complex video scenes typically rely on hand-crafted appearance and motion features. However, adopting user defined representations is clearly suboptimal, as it is desirable to learn descriptors specific to the scene of interest. To cope with this need, in this paper we propose Appearance and Motion DeepNet (AMDN), a novel approach based on deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Then, based on the learned features, multiple one-class SVM models are used to predict the anomaly scores of each input. Finally, a novel late fusion strategy is proposed to combine the computed scores and detect abnormal events. The proposed ADMN is extensively evaluated on publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.
Linguistic Matrix Theory

Recent research in computational linguistics has developed algorithms which associate matrices with adjectives and verbs, based on the distribution of words in a corpus of text. These matrices are linear operators on a vector space of context words. They are used to construct the meaning of composite expressions from that of the elementary constituents, forming part of a compositional distributional approach to semantics. We propose a Matrix Theory approach to this data, based on permutation symmetry along with Gaussian weights and their perturbations. A simple Gaussian model is tested against word matrices created from a large corpus of text. We characterize the cubic and quartic departures from the model, which we propose, alongside the Gaussian parameters, as signatures for comparison of linguistic corpora. We propose that perturbed Gaussian models with permutation symmetry provide a promising framework for characterizing the nature of universality in the statistical properties of word matrices. The matrix theory framework developed here exploits the view of statistics as zero dimensional perturbative quantum field theory. It perceives language as a physical system realizing a universality class of matrix statistics characterized by permutation symmetry. 1 d.kartsaklis@qmul.ac.uk 2 s.ramgoolam@qmul.ac.uk 3 mehrnoosh.sadrzadeh@qmul.ac.uk ar X iv :1 70 3. 10 25 2v 1 [ cs .C L ] 2 8 M ar 2 01 7