nils-herrmann/clean_scirepeval huggingface dataset:
Pneumatic robotic systems for upper limb rehabilitation

The aim of rehabilitation robotic area is to research on the application of robotic devices to therapeutic procedures. The goal is to achieve the best possible motor, cognitive and functional recovery for people with impairments following various diseases. Pneumatic actuators are attractive for robotic rehabilitation applications because they are lightweight, powerful, and compliant, but their control has historically been difficult, limiting their use. This article first reviews the current state-of-art in rehabilitation robotic devices with pneumatic actuation systems reporting main features and control issues of each therapeutic device. Then, a new pneumatic rehabilitation robot for proprioceptive neuromuscular facilitation therapies and for relearning daily living skills: like taking a glass, drinking, and placing object on shelves is described as a case study and compared with the current pneumatic rehabilitation devices.
AND/OR Cutset Conditioning

Cutset conditioning is one of the methods of solving reasoning tasks for graphical models, especially when space restrictions make inference (e.g., jointree-clustering) algorithms infeasible. The wcutsetis a natural extension of the method to a hybrid algorithm that performs search on the conditioning variables and inference on the remaining problems of induced width bounded by w. This paper takes a fresh look at these methods through the spectrum of AND/OR search spaces for graphical models. The resultingAND/OR cutset method is a strict improvement over the traditional one, often by exponential amounts.
Theory and Algorithms for Forecasting Time Series

We present data-dependent learning bounds for the general scenario of non-stationary nonmixing stochastic processes. Our learning guarantees are expressed in terms of a datadependent measure of sequential complexity and a discrepancy measure that can be estimated from data under some mild assumptions. We also also provide novel analysis of stable time series forecasting algorithm using this new notion of discrepancy that we introduce. We use our learning bounds to devise new algorithms for non-stationary time series forecasting for which we report some preliminary experimental results.
Caring for caregivers: designing for integrality

Health and wellness have drawn significant attention in the HCI and CSCW communities. Many prior studies have focused on designing technologies that are patient-centric, allowing caregivers to take better care of patients. Less has been done in understanding and minimizing the burden of caregiving in caregivers' own lives. We conducted a qualitative interview study to understand their experiences in caregiving. The findings reveal a great magnitude of challenges in the caregivers' day-to-day lives, ranging from the physical and social, to the personal and emotional. Caregivers have to constantly balance their personal lives with work, family, and their caregiver roles, which can be overwhelmingly stressful. We discuss how caregivers attempt maintaining this balance through two concepts: first, giving-impact, and second, visibility-invisibility. Our study's findings call for system design that focuses not only on patients but also caregivers, addressing the burdens that often impair their health and wellness.
Towards affective camera control in games

Information about interactive virtual environments, such as games, is perceived by users through a virtual camera. While most interactive applications let users control the camera, in complex navigation tasks within 3D environments users often get frustrated with the interaction. In this paper, we propose inclusion of camera control as a vital component of affective adaptive interaction in games. We investigate the impact of camera viewpoints on psychophysiology of players through preference surveys collected from a test game. Data is collected from players of a 3D prey/predator game in which player experience is directly linked to camera settings. Computational models of discrete affective states of fun, challenge, boredom, frustration, excitement, anxiety and relaxation are built on biosignal (heart rate, blood volume pulse and skin conductance) features to predict the pairwise self-reported emotional preferences of the players. For this purpose, automatic feature selection and neuro-evolutionary preference learning are combined providing highly accurate affective models. The performance of the artificial neural network models on unseen data reveals accuracies of above 80% for the majority of discrete affective states examined. The generality of the obtained models is tested in different test-bed game environments and the use of the generated models for creating adaptive affect-driven camera control in games is discussed.
Surgical management for a huge presacral teratoma and a meningocele in an adult with Currarino triad: a case report

The Currarino triad is a rare hereditary syndrome comprising anorectal malformation, sacral bony defect, and presacral mass. Most of the patients are diagnosed during infancy.



A 44-year-old man was diagnosed with Currarino triad, with a huge presacral teratoma and meningocele. One-stage surgery via posterior approach was successful.



Treatment of the presacral mass in the Currarino triad, diagnosed in adulthood, is challenging. Multidisciplinary management and detailed planning before surgery are important for a satisfactory outcome.
5G Millimeter-Wave Antenna Array: Design and Challenges

As there has been an explosive increase in wireless data traffic, mmw communication has become one of the most attractive techniques in the 5G mobile communications systems. Although mmw communication systems have been successfully applied to indoor scenarios, various external factors in an outdoor environment limit the applications of mobile communication systems working at the mmw bands. In this article, we discuss the issues involved in the design of antenna array architecture for future 5G mmw systems, in which the antenna elements can be deployed in the shapes of a cross, circle, or hexagon, in addition to the conventional rectangle. The simulation results indicate that while there always exists a non-trivial gain fluctuation in other regular antenna arrays, the circular antenna array has a flat gain in the main lobe of the radiation pattern with varying angles. This makes the circular antenna array more robust to angle variations that frequently occur due to antenna vibration in an outdoor environment. In addition, in order to guarantee effective coverage of mmw communication systems, possible solutions such as distributed antenna systems and cooperative multi-hop relaying are discussed, together with the design of mmw antenna arrays. Furthermore, other challenges for the implementation of mmw cellular networks, for example, blockage, communication security, hardware development, and so on, are discussed, as are potential solutions.
Feature Selection using One Class SVM : A New Perspective

Feature selection is an important task for mining useful information from datasets in high dimensions, a typical characteristic of biology domains such as microarray datasets. In this paper, we present an altogether new perspective on feature selection. We pose feature selection as a one class SVM problem of modeling the space in which features can be represented. We show that finding the support vectors in our one class formulation is tantamount to performing feature selection. Further, we show that our formulation reduces to the standard QPFS formulation in the dual problem space. Not only our formulation gives new insights into the task of feature selection, solving it directly in the primal space can give significant computational gains when the number of the samples is much smaller than the number of features. We validate our thesis by experimenting on three different microarray datasets.
The Consciousness Prior

A new prior is proposed for representation learning, which can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by the phenomenon of consciousness seen as the formation of a low-dimensional combination of a few concepts constituting a conscious thought, i.e., consciousness as awareness at a particular time instant. This provides a powerful constraint on the representation in that such low-dimensional thought vectors can correspond to statements about reality which are true, highly probable, or very useful for taking decisions. The fact that a few elements of the current state can be combined into such a predictive or useful statement is a strong constraint and deviates considerably from the maximum likelihood approaches to modelling data and how states unfold in the future based on an agent's actions. Instead of making predictions in the sensory (e.g. pixel) space, the consciousness prior allows the agent to make predictions in the abstract space, with only a few dimensions of that space being involved in each of these predictions. The consciousness prior also makes it natural to map conscious states to natural language utterances or to express classical AI knowledge in the form of facts and rules, although the conscious states may be richer than what can be expressed easily in the form of a sentence, a fact or a rule.
Evaluation of the possible proapoptotic effect on Tetrodotoxin on Ehrlich Ascites Carcinoma Cells ( EAC ) using P 53 & Caspase 3 immunohistochemistry in mice ' s liver

It is becoming obvious that all cancers have a defe ctiv p53 pathway, either through TP53 mutation or deregulation of the tumor suppressor function of the wild type TP53 . In this study we examined the expression of P53 and Caspase 3 in transperitoneally injected Ehrlich As cite carcinoma cells (EAC) treated with Tetrodotoxin in the liver of adult mice in order to evaluate the po ssible pro apoptotic effect of Tetrodotoxin .  Early in the treatment, num erous EAC detected in the large blood vessels & cen tral veins and expressed both of P53 & Caspase 3 in contrast to the late absence of P53 expressing EAC at the 12 th day of Tetrodotoxin treatment. In the same context , predominantly the perivascular hepatocytes expresse d Caspase 3 in contrast to the more diffuse express ion pattern late with Tetrodotoxin treatment. Non of the hepatocytes ever expressed P5 3 neither with early nor late Tetrodotoxin treatmen t.  Tetrodotoxin therapy has a proapoptotic effect on Ehrlich Ascites carcin oma Cells (EAC). This may be through enhancing the tumor suppressor function of the wild type TP53 with subsequent Caspase 3 activation .
A high-power, low-loss W-band SPDT switch using SiGe PIN diodes

This paper presents a W-band SPDT switch implemented using PIN diodes in a new 90 nm SiGe BiCMOS technology. The SPDT switch achieves a minimum insertion loss of 1.4 dB and an isolation of 22 dB at 95 GHz, with less than 2 dB insertion loss from 77-134 GHz, and greater than 20 dB isolation from 79-129 GHz. The input and output return losses are greater than 10 dB from 73-133 GHz. By reverse biasing the off-state PIN diodes, the P1dB is larger than +24 dBm. To the authors' best knowledge, these results demonstrate the lowest loss and highest power handling capability achieved by a W-band SPDT switch in any silicon-based technology reported to date.
Enabling self-powered autonomous wireless sensors with new-generation I2C-RFID chips

A self-powered autonomous RFID device with sensing and computing capabilities is presented in this paper. Powered by an RF energy-harvesting circuit enhanced by a DC-DC voltage booster in silicon-on-insulator (SOI) technology, the device relies on a microcontroller and a new generation I2C-RFID chip to wirelessly deliver sensor data to standard RFID EPC Class-1 Generation-2 (Gen2) readers. When the RF power received from the interrogating reader is -14 dBm or higher, the device, fabricated on an FR4 substrate using low-cost discrete components, is able to produce 2.4-V DC voltage to power its circuitry. The experimental results demonstrate the effectiveness of the device to perform reliable sensor data transmissions up to 5 meters in fully-passive mode. To the best of our knowledge, this represents the longest read range ever reported for passive UHF RFID sensors compliant with the EPC Gen2 standard.
Clustering financial time series: New insights from an extended hidden Markov model

In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian returns. The proposed model handles simultaneously the heterogeneity across stock markets and over time, i.e., time-constant and timevarying discrete latent variables capture unobserved heterogeneity between and within stock markets, respectively. The results show a clear distinction between two groups of stock markets, each one characterized by different regime switching dynamics that correspond to different expected return-risk patterns. We identify three regimes: the so-called bull and bear regimes, as well as a stable regime with returns close to 0, which turns out to be the most frequently occurring regime. This is consistent with stylized facts in financial econometrics.
Supply Networks as Complex Systems: A Network-Science-Based Characterization

Outsourcing, internationalization, and complexity characterize today's aerospace supply chains, making aircraft manufacturers structurally dependent on each other. Despite several complexity-related supply chain issues reported in the literature, aerospace supply chain structure has not been studied due to a lack of empirical data and suitable analytical toolsets for studying system structure. In this paper, we assemble a large-scale empirical data set on the supply network of Airbus and apply the new science of networks to analyze how the industry is structured. Our results show that the system under study is a network, formed by communities connected by hub firms. Hub firms also tend to connect to each other, providing cohesiveness, yet making the network vulnerable to disruptions in them. We also show how network science can be used to identify firms that are operationally critical and that are key to disseminating information.
A comparison of CNN-based face and head detectors for real-time video surveillance applications

Detecting faces and heads appearing in video feeds are challenging tasks in real-world video surveillance applications due to variations in appearance, occlusions and complex backgrounds. Recently, several CNN architectures have been proposed to increase the accuracy of detectors, although their computational complexity can be an issue, especially for realtime applications, where faces and heads must be detected live using high-resolution cameras. This paper compares the accuracy and complexity of state-of-the-art CNN architectures that are suitable for face and head detection. Single pass and region-based architectures are reviewed and compared empirically to baseline techniques according to accuracy and to time and memory complexity on images from several challenging datasets. The viability of these architectures is analyzed with real-time video surveillance applications in mind. Results suggest that, although CNN architectures can achieve a very high level of accuracy compared to traditional detectors, their computational cost can represent a limitation for many practical real-time applications.
Atmospheric Turbulence Effects on Wind-Turbine Wakes : An LES Study

A numerical study of atmospheric turbulence effects on wind-turbine wakes is presented. Large-eddy simulations of neutrally-stratified atmospheric boundary layer flows through stand-alone wind turbines were performed over homogeneous flat surfaces with four different aerodynamic roughness lengths. Emphasis is placed on the structure and characteristics of turbine wakes in the cases where the incident flows to the turbine have the same mean velocity at the hub height but different mean wind shears and turbulence intensity levels. The simulation results show that the different turbulence intensity levels of the incoming flow lead to considerable influence on the spatial distribution of the mean velocity deficit, turbulence intensity, and turbulent shear stress in the wake region. In particular, when the turbulence intensity level of the incoming flow is higher, the turbine-induced wake (velocity deficit) recovers faster, and the locations of the maximum turbulence intensity and turbulent stress are closer to the turbine. A detailed analysis of the turbulence kinetic energy budget in the wakes reveals also an important effect of the incoming flow turbulence level on the magnitude and spatial distribution of the shear production and transport terms.
An Adaptive PAM-4 5 Gb / s Backplane Transceiver in 0 . 25 um CMOS

This paper describes a novel backplane transceiver, which uses PAM-4 (pulse amplitude modulated four level) signalling and continuously adaptive transmit based equalization to move 5 Gcbh (channel bits per second) across typical FR-4 backplanes for total distances of up to 50 inches through two sets of backplane connectors. The paper focuses on the implementation of the equalizer and the adaptation algorithms, and includes measured results. The 17 mm2 device is implemented in a 0.25um CMOS process, operates on 2.5 V and 3.3 V supplies and consumes 1.2 W. 11.  A. Transceiver Block Diagram A block diagram of the transceiver is illustrated in figure 1.
Singapore Chinese Health Study: development, validation, and calibration of the quantitative food frequency questionnaire.

This report describes the development and validation/calibration of a structured food frequency questionnaire for use in a large-scale cohort study of diet and health in Chinese men and women aged 45-74 years in Singapore, the development of a food composition database for analysis of the dietary data, and the results of the dietary validation/calibration study. The present calibration study comparing estimated intakes from 24-hour recalls with those from the food frequency questionnaires revealed correlations of 0.24-0.79 for energy and nutrients among the Singapore Chinese, which are comparable to the correlation coefficients reported in calibration studies of other populations. We also report on the nutritional profiles of Singapore Chinese on the basis of results of 1,880 24-hour dietary recalls conducted on 1,022 (425 men and 597 women) cohort subjects. Comparisons with age-adjusted corresponding values for US whites and blacks show distinct differences in dietary intakes between the Singapore and US populations. The Singapore cohort will be followed prospectively to identify dietary associations with cancer risk and other health outcomes.
Past, Present and Future of Ambient Intelligence and Smart Environments

We are gradually making a transition to a new era where computers become truly intertwined with our daily lives. Up to not so long ago, we were able to know clearly where computers were and in which way they affected our lives. This has been gradually blurred and now computing devices of various types are all around us, embedded in different objects we interact with and in that way they influence our lives. There are indications that this trend is irreversible and that computing and society will now interact with each other in far richer ways than before, to the point that computing will become transparent to humans and still intrinsically involved in our daily living. This paper provides a brief overview of the evolution of these fields, describes some of the current developments, and points at some of the immediate challenges that researchers in these area face.
Lexical Normalization of Spanish Tweets

Twitter data have brought new opportunities to know what happens in the world in real-time, and conduct studies on the human subjectivity on a diversity of issues and topics at large scale, which would not be feasible using traditional methods. However, as well as these data represent a valuable source, a vast amount of noise can be found in them. Because of the brevity of texts and the widespread use of mobile devices, non-standard word forms abound in tweets, which degrade the performance of Natural Language Processing tools. In this paper, a lexical normalization system of tweets written in Spanish is presented. The system suggests normalization candidates for out-of-vocabulary (OOV) words based on similarity of graphemes or phonemes. Using contextual information, the best correction candidate for a word is selected. Experimental results show that the system correctly detects OOV words and the most of cases suggests the proper corrections. Together with this, results indicate a room for improvement in the correction candidate selection. Compared with other methods, the overall performance of the system is above-average and competitive to different approaches in the literature.
Single Image Action Recognition Using Semantic Body Part Actions

In this paper, we propose a novel single image action recognition algorithm based on the idea of semantic part actions. Unlike existing part-based methods, we argue that there exists a mid-level semantic, the semantic part action; and human action is a combination of semantic part actions and context cues. In detail, we divide human body into seven parts: head, torso, arms, hands and lower body. For each of them, we define a few semantic part actions (e.g. head: laughing). Finally, we exploit these part actions to infer the entire body action (e.g. applauding). To make the proposed idea practical, we propose a deep network-based framework which consists of two subnetworks, one for part localization and the other for action prediction. The action prediction network jointly learns part-level and body-level action semantics and combines them for the final decision. Extensive experiments demonstrate our proposal on semantic part actions as elements for entire body action. Our method reaches mAP of 93.9% and 91.2% on PASCAL VOC 2012 and Stanford-40, which outperforms the state-of-the-art by 2.3% and 8.6%.
Progressive Sparse Local Attention for Video object detection

Transferring image-based object detectors to domain of videos remains a challenging problem. Previous efforts mostly exploit optical flow to propagate features across frames, aiming to achieve a good trade-off between performance and computational complexity. However, introducing an extra model to estimate optical flow would significantly increase the overall model size. The gap between optical flow and high-level features can hinder it from establishing the spatial correspondence accurately. Instead of relying on optical flow, this paper proposes a novel module called Progressive Sparse Local Attention (PSLA), which establishes the spatial correspondence between features across frames in a local region with progressive sparse strides and uses the correspondence to propagate features. Based on PSLA, Recursive Feature Updating (RFU) and Dense feature Transforming (DFT) are introduced to model temporal appearance and enrich feature representation respectively. Finally, a novel framework for video object detection is proposed. Experiments on ImageNet VID are conducted. Our framework achieves a state-of-the-art speedaccuracy trade-off with significantly reduced model capacity.