nils-herrmann/clean_scirepeval huggingface dataset:
What Do Stroke Patients Look for in Game-Based Rehabilitation

Stroke is one of the most common causes of physical disability, and early, intensive, and repetitive rehabilitation exercises are crucial to the recovery of stroke survivors. Unfortunately, research shows that only one third of stroke patients actually perform recommended exercises at home, because of the repetitive and mundane nature of conventional rehabilitation exercises. Thus, to motivate stroke survivors to engage in monotonous rehabilitation is a significant issue in the therapy process. Game-based rehabilitation systems have the potential to encourage patients continuing rehabilitation exercises at home. However, these systems are still rarely adopted at patients' places. Discovering and eliminating the obstacles in promoting game-based rehabilitation at home is therefore essential. For this purpose, we conducted a study to collect and analyze the opinions and expectations of stroke patients and clinical therapists. The study is composed of 2 parts: Rehab-preference survey - interviews to both patients and therapists to understand the current practices, challenges, and expectations on game-based rehabilitation systems; and Rehab-compatibility survey - a gaming experiment with therapists to elaborate what commercial games are compatible with rehabilitation. The study is conducted with 30 outpatients with stroke and 19 occupational therapists from 2 rehabilitation centers in Taiwan. Our surveys show that game-based rehabilitation systems can turn the rehabilitation exercises more appealing and provide personalized motivation for various stroke patients. Patients prefer to perform rehabilitation exercises with more diverse and fun games, and need cost-effective rehabilitation systems, which are often built on commodity hardware. Our study also sheds light on incorporating the existing design-for-fun games into rehabilitation system. We envision the results are helpful in developing a platform which enables rehab-compatible (i.e., existing, appropriately selected) games to be operated on commodity hardware and brings cost-effective rehabilitation systems to more and more patients' home for long-term recovery.
End-to-End Sound Source Separation Conditioned On Instrument Labels

Can we perform an end-to-end sound source separation (SSS) with a variable number of sources using a deep learning model? This paper presents an extension of the WaveU-Net  model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicative conditioning with instrument labels at the bottleneck of the Wave-U-Net and show its effect on the separation results. This approach can be further extended to other types of conditioning such as audio-visual SSS and score-informed SSS.
Hole-filling Based on Disparity Map and Inpainting for Depth- Image-Based Rendering

Due to the changes in viewpoint, holes may appear in the novel view synthesized by depth-image-based rendering (DIBR). A hole-filling method combining disparity-mapbased hole-filling and inpainting is proposed. The method first eliminates matching errors in destination image according to the disparity map. Then, holes are classified into different types according to their sizes. Finally, a disparity-map-based approach and an improved exemplar-based inpainting algorithm are used to fill different types of holes according to the type of hole. Experimental results show that the artifacts at the edge of foreground objects can be reduced in synthesized view since the matching errors are eliminated before hole-filling. In addition, the proposed method can achieve more natural and satisfactory results in filled areas in comparison with the disparity-mapbased approach and Gautier’s inpainting algorithm, though it may result in higher time complexity.  Depth-image-based Rendering, Hole-filling, Holes, Disparity Map, Inpainting
Common-Specific Multimodal Learning for Deep Belief Network

Multimodal Deep Belief Network has been widely used to extract representations for multimodal data by fusing the high-level features of each data modality into common representations. Such straightforward fusion strategy can benefit the classification and information retrieval tasks. However, it may introduce noise in case the high-level features are not naturally common hence non-fusable for different modalities. Intuitively, each modality may have its own specific features and corresponding representation capabilities thus should not be simply fused. Therefore, it is more reasonable to fuse only the common features and represent the multimodal data by both the fused features and the modality-specific features. To distinguish common features from modal-specific features is a challenging task for traditional DBN models where all features are crudely mixed. This paper proposes the Common-Specific Multimodal Deep Belief Network (CSDBN) to solve the problem. CS-DBN automatically separates common features from modal-specific features and fuses only the common ones for data representation. Experimental results demonstrate the superiority of CS-DBN for classification tasks compared with the baseline approaches.
A Low-Noise, Non-Contact EEG/ECG Sensor

Typical electroencephalogram (EEG) and electrocardiogram (ECG) sensors require conductive gel to ensure low-impedance electrical contact between the sensor and skin, making set-up time-consuming and long-term recording problematic. We present a gel-free, non-contact EEG/ECG sensor with on-board electrode that capacitively couples to the skin. Active shielding of the high-impedance input significantly reduces noise pickup, and reduces variations in gain as a function of gap distance. The integrated sensor combines amplification, bandpass filtering, and analog-to-digital conversion within a 1 inch diameter enclosure. The measured input-referred noise, over 1-100 Hz frequency range, is 2 muVrms at 0.2 mm sensor distance, and 17 muVrms at 3.2 mm distance. Experiments coupling the sensor to human scalp through hair and to chest through clothing produce clear EEG and ECG recorded signals.
Leptotrichia species in human infections II

Leptotrichia species are non-motile facultative anaerobic/anaerobic bacteria that are found mostly in the oral cavity and some other parts of the human body, in animals, and even in ocean sediments. Valid species include L. buccalis, L. goodfellowii, L. hofstadii, L. honkongensis, L. shahii, L. trevisanii, and L. wadei. Some species require serum or blood for growth. All species ferment carbohydrates and produce lactic acid that may be involved with tooth decay. Acting as opportunistic pathogens, they are involved in a variety of diseases, and have been isolated from immunocompromised but also immunocompetent individuals. Mucositis, oral lesions, wounds, and abscesses may predispose to Leptotrichia septicemia. Because identification of Leptotrichia species by phenotypic features occasionally lead to misidentification, genetic techniques such as 16S rRNA gene sequencing is recommended. Early diagnosis and treatment of leptotrichia infections is important for positive outcomes. Over the last years, Leptotrichia species have been associated with several changes in taxonomy and new associations with clinical diseases. Such changes are reported in this updated review.
A compact broadband hybrid ridged SIW and GCPW coupler

In this paper, a novel hybrid ridged substrate integrated waveguide (RSIW) and ground coplanar waveguide (GCPW) structure coupler is presented. In this coupler, the RSIW and GCPW are arranged back to back to realize high-integration. Such hybrid coupler combines the broadband advantage of RSIW and the flexibility of GCPW. According to the simulated results, it can operate at 28 GHz and achieve a relative bandwidth of 17%.
A direct scattering model for tracking vehicles with high-resolution radars

In advanced driver assistance systems and autonomous driving, reliable environment perception and object tracking based on radar is fundamental. High-resolution radar sensors often provide multiple measurements per object. Since in this case traditional point tracking algorithms are not applicable any more, novel approaches for extended object tracking emerged in the last few years. However, they are primarily designed for lidar applications or omit the additional Doppler information of radars. Classical radar based tracking methods using the Doppler information are mostly designed for point tracking of parallel traffic. The measurement model presented in this paper is developed to track vehicles of approximately rectangular shape in arbitrary traffic scenarios including parallel and cross traffic. In addition to the kinematic state, it allows to determine and track the geometric state of the object. Using the Doppler information is an important component in the model. Furthermore, it neither requires measurement preprocessing, data clustering, nor explicit data association. For object tracking, a Rao-Blackwellized particle filter (RBPF) adapted to the measurement model is presented.
A Variable-Flux Hybrid-PM Switched-Flux Memory Machine for EV/HEV Applications

In this paper, a novel topology of hybrid-permanent-magnet switched-flux memory machine (HPM-SFMM) is proposed, which is characterized by a conventional SFPM machine with embedded “V”-shaped Al-Ni-Co magnet poles. The proposed machine combines the distinct synergies of torque enhancement in neodymium-iron-boron (NdFeB) PM and flux variability in aluminum-nickel-cobalt (AlNiCo) PM. By changing the magnetization directions of Al-Ni-Co magnets, the wide-speed-range high-efficiency operation can be readily achieved, which is highly favorable for automotive applications. The configuration and operating principle of the machine are first described, and the combination of stator/rotor pole numbers is optimized. In addition, the torque density/flux adjusting capability as the functions of dual-magnet dimensions are analytically derived. The magnet hybridizing proportion is optimized in order to achieve a favorite tradeoff between flux adjusting range and torque density improvement. Then, the electromagnetic performance of the proposed HPM-SFMM is investigated. Finally, a 12/14-stator/rotor pole prototype is fabricated to experimentally verify the analysis.
B2C web site quality and emotions during online shopping episodes: An empirical study

This paper explores the impact of the quality of a web site on the cognitive process leading to consumers’ emotions—considered as direct antecedents to shopping behaviors and operationalized as mental states of readiness arising from the appraisal of events. A parsimonious theoretical model was defined and tested with data collected from 215 web-shopping episodes during which consumers were shopping for low-touch products. Analysis of the results showed that web site quality had a positive impact on the cognitive appraisal of situational state, which in turn influenced five of the six emotions of the proposed model: liking, joy, pride, dislike, and frustration. Results also showed that a substantial number of shoppers experienced intensively the emotions of liking and joy. Moreover, this paper highlights several implications that could help managers and webmasters improve the quality of their web sites.
The neural mechanisms of top-down attentional control

Selective visual attention involves dynamic interplay between attentional control systems and sensory brain structures. We used event-related functional magnetic resonance imaging (fMRI) during a cued spatial-attention task to dissociate brain activity related to attentional control from that related to selective processing of target stimuli. Distinct networks were engaged by attention-directing cues versus subsequent targets. Superior frontal, inferior parietal and superior temporal cortex were selectively activated by cues, indicating that these structures are part of a network for voluntary attentional control. This control biased activity in multiple visual cortical areas, resulting in selective sensory processing of relevant visual targets.
Worldwide use and impact of the NASA Astrophysics Data System digital library

The NASA Astrophysics Data System (ADS), along with astronomy's journals and data centers (a collaboration dubbed URANIA), has developed a distributed on-line digital library which has become the dominant means by which astronomers search, access and read their technical literature. Digital libraries permit the easy accumulation of a new type of bibliometric measure, the number of electronic accesses (\reads") of individual articles. By combining data from the text, citation, and reference databases with data from the ADS readership logs we have been able to create Second Order Bibliometric Operators, a customizable class of collaborative lters which permits substantially improved accuracy in literature queries. Using the ADS usage logs along with membership statistics from the International Astronomical Union and data on the population and gross domestic product (GDP) we develop an accurate model for world-wide basic research where the number of scientists in a country is proportional to the GDP of that country, and the amount of basic research done by a country is proportional to the number of scientists in that country times that country's per capita GDP. We introduce the concept of utility time to measure the impact of the ADS/URANIA and the electronic astronomical library on astronomical research. We nd that in 2002 it amounted to the equivalent of 736 FTE researchers, or $250 Million, or the astronomical research done in France. Subject headings: digital libraries; bibliometrics; sociology of science; information retrieval
The Optimum Tapered Transmission Line Matching Section

The tapered transmission line matching section is analyzed by treating it as a high pass filter. The ideal reflection coefficient characteristic for the taper that gives the smallest pass band tolerance for a given cutoff frequency and vice versa is derived from the expression for the reflection coefficient of the optimum designed n section quarter-wave transformer by taking the limit as n tends to infinity. By neglecting the square of the reflection coefficient in the differential equation for the reflection coefficient on the taper, a synthesis procedure is derived for obtaining an optimum taper. The procedure is similar to that used in designing an optimum line source distribution in antenna theory. Conditions are derived for the maximum allowable pass band reflection and change in impedance level for which the theory remains accurate. For a maimum reflection coefficient of 0.1 in the pass band the theory remains accurate for all frequencies above the cutoff value provided the change in impedance level does not exceed 7.5. This optimum taper is compared with the well-known exponential and Gaussian tapers and is found to be 13.9 per cent and 27 per cent shorter respectively for the same cutoff frequency and pass band tolerance.
Inkjet-printed RF energy harvesting and wireless power trasmission devices on paper substrate

This paper presents an overview of the state-of-the-art inkjet-printed energy harvesting and wireless power transfer technology on paper substrate. RF energy harvesting devices utilizing a charge pump circuit or an active antenna based wireless power transmission device are presented. Inkjet printing technology on paper substrate and fabrication techniques, along with a variety of applications, are presented with particular focus on low-cost and eco-friendly wireless systems for wireless sensor networks.
Three-column fixation for complex tibial plateau fractures.

1) To introduce a computed tomography-based "three-column fixation" concept; and 2) to evaluate clinical outcomes (by using a column-specific fixation technique) for complex tibial plateau fractures (Schatzker classification Types V and VI).



Prospective cohort study.



Level 1 trauma center.



Twenty-nine cases of complex tibial plateau fractures were included. Based on routine x-ray and computed tomography images, all the fractures were classified as a "three-column fracture," which means at least one separate fragment was found in lateral, medial, and posterior columns in the proximal tibia (Schatzker classification Types V and VI).



The patients were operated on in a "floating position" with a combined approach, an inverted L-shaped posterior approach combined with an anterior-lateral approach. All three columns of fractures were fixed.



Operative time, blood loss, quality of reduction and alignment, fracture healing, complications, and functional outcomes based on Hospital for Special Surgery score and lower-extremity measure were recorded.



All the cases were followed for average 27.3 months (range, 24-36 months). All the cases had satisfactory reduction except one case, which had a 4-mm stepoff at the anterior ridge of the tibial plateau postoperatively. No case of secondary articular depression was found. One case had secondary varus deformity, one case had secondary valgus deformity, and two cases of screw loosening occurred postoperatively. No revision surgery was performed. Two cases had culture-negative wound drainage. No infection was noted. The average radiographic bony union time and full weightbearing time were 13.1 weeks (range, 11-16 weeks) and 16.7 weeks (range, 12-24 weeks), respectively. The mean Short Form 36, Hospital for Special Surgery score, and lower-extremity measure at 24 months postoperatively were 89 (range, 80-98), 90 (range, 84-98), and 87 (range, 80-95), respectively. The average range of motion of the affected knee was 2.7° to 123.4° at 2 years after the operation.



Three-column fixation is a new fixation concept in treating complex tibial plateau fractures, which is especially useful for multiplanar fractures involving the posterior column. The combination of posterior and anterior-lateral approaches is a safe and effective way to have direct reduction and satisfactory fixation for such difficult tibial plateau fractures.
Formal Verification With Frama-C: A Case Study in the Space Software Domain

With the increasing importance of software in the aerospace field, as evidenced by its growing size and complexity, a rigorous and reliable software verification and validation process must be applied to ensure conformance with the strict requirements of this software. Although important, traditional validation activities such as testing and simulation can only provide a partial verification of behavior in critical real-time software systems, and thus, formal verification is an alternative to complement these activities. Two useful formal software verification approaches are deductive verification and abstract interpretation, which analyze programs statically to identify defects.  The results indicate that both approaches can be employed in a software verification process to make software more reliable.
A Cognitive System for Understanding Human Manipulation Actions

This paper describes the architecture of a cognitive system that interprets human manipulation actions from perceptual information (image and depth data) and that includes interacting modules for perception and reasoning. Our work contributes to two core problems at the heart of action understanding: (a) the grounding of relevant information about actions in perception (the perception-action integration problem), and (b) the organization of perceptual and high-level symbolic information for interpreting the actions (the sequencing problem). At the high level, actions are represented with the Manipulation Action Grammar, a context-free grammar that organizes actions as a sequence of sub events. Each sub event is described by the hand, movements, objects and tools involved, and the relevant information about these factors is obtained from biologicallyinspired perception modules. These modules track the hands and objects, and they recognize the hand grasp, objects and actions using attention, segmentation, and feature description. Experiments on a new data set of manipulation actions show that our system extracts the relevant visual information and semantic representation. This representation could further be used by the cognitive agent for reasoning, prediction, and planning.
3D conditional generative adversarial networks for high-quality PET image estimation at low dose

Positron emission tomography (PET) is a widely used imaging modality, providing insight into both the biochemical and physiological processes of human body. Usually, a full dose radioactive tracer is required to obtain high-quality PET images for clinical needs. This inevitably raises concerns about potential health hazards. On the other hand, dose reduction may cause the increased noise in the reconstructed PET images, which impacts the image quality to a certain extent. In this paper, in order to reduce the radiation exposure while maintaining the high quality of PET images, we propose a novel method based on 3D conditional generative adversarial networks (3D c-GANs) to estimate the high-quality full-dose PET images from low-dose ones. Generative adversarial networks (GANs) include a generator network and a discriminator network which are trained simultaneously with the goal of one beating the other. Similar to GANs, in the proposed 3D c-GANs, we condition the model on an input low-dose PET image and generate a corresponding output full-dose PET image. Specifically, to render the same underlying information between the low-dose and full-dose PET images, a 3D U-net-like deep architecture which can combine hierarchical features by using skip connection is designed as the generator network to synthesize the full-dose image. In order to guarantee the synthesized PET image to be close to the real one, we take into account of the estimation error loss in addition to the discriminator feedback to train the generator network. Furthermore, a concatenated 3D c-GANs based progressive refinement scheme is also proposed to further improve the quality of estimated images. Validation was done on a real human brain dataset including both the normal subjects and the subjects diagnosed as mild cognitive impairment (MCI). Experimental results show that our proposed 3D c-GANs method outperforms the benchmark methods and achieves much better performance than the state-of-the-art methods in both qualitative and quantitative measures.
On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples

The Domain Adaptation problem in machine learning occurs when the test and training data generating distributions differ. We consider the covariate shift setting, where the labeling function is the same in both domains. Many works have proposed algorithms for Domain Adaptation in this setting. However, there are only very few generalization guarantees for these algorithms. We show that, without strong prior knowledge about the training task, such guarantees are actually unachievable (unless the training samples are prohibitively large). The contributions of this paper are two-fold: On the one hand we show that Domain Adaptation in this setup is hard. Even under very strong assumptions about the relationship between source and target distribution and, on top of that, a realizability assumption for the target task with respect to a small class, the required total sample sizes grow unboundedly with the domain size. On the other hand, we present settings where we achieve almost matching upper bounds on the sum of the sizes of the two samples. Moreover, the (necessarily large) samples can be mostly unlabeled (target) samples, which are often much cheaper to obtain than labels. The size of the labeled (source) sample shrinks back to standard dependence on the VC-dimension of the concept class. This implies that unlabeled target-generated data is provably beneficial for DA learning.