nils-herrmann/clean_scirepeval huggingface dataset:
Scalable and Consistent Radio Map Management Scheme for Participatory Sensing-based Wi-Fi Fingerprinting

Although Wi-Fi fingerprinting is a promising solution for indoor localization, its widespread use is limited due to the necessity of time-consuming site survey. Recently, active research has been conducted to reduce site-survey cost with participatory sensing. However, most of the previous schemes focused on construction of radio map, but not on radio map management regarding scalability and consistency issues. Radio map construction should handle a large amount of data collected from many people over a long period in limited space. It should also guarantee consistent accuracy regardless of time and user context, such as device type and direction. In this paper, we describe a radio map management scheme, based on fingerprint clustering technique, which considers both the scalability and consistency issues. The proposed scheme clusters the fingerprints collected at different times with different types of devices and picks a cluster head as a representative fingerprint. We validate the feasibility of the proposed scheme with real experiments in an office environment.
Effects of the intake of Undaria pinnatifida (Wakame) and its sporophylls (Mekabu) on postprandial glucose and insulin metabolism.

Long-term suppression of postprandial glucose concentration is an important dietary strategy for the prevention and treatment of type 2 diabetes. Because previous reports have suggested that seaweed may exert anti-diabetic effects in animals, the effects of Wakame or Mekabu intake with 200 g white rice, 50 g boiled soybeans, 60 g potatoes, and 40 g broccoli on postprandial glucose, insulin and free fatty acid levels were investigated in healthy subjects. Plasma glucose levels at 30 min and glucose area under the curve (AUC) at 0-30 min after the Mekabu meal were significantly lower than that after the control meal. Plasma glucose and glucose AUC were not different between the Wakame and control meals. Postprandial serum insulin and its AUC and free fatty acid concentration were not different among the three meals. In addition, fullness, satisfaction, and wellness scores were not different among the three meals. Thus, consumption of 70 g Mekabu with a white rice-based breakfast reduces postprandial glucose concentration.
Virtual model control of a bipedal walking robot

The transformation from high level task speci cation to low level motion control is a fundamental issue in sensorimotor control in animals and robots. This thesis develops a control scheme called virtual model control which addresses this issue. Virtual model control is a motion control language which uses simulations of imagined mechanical components to create forces, which are applied through joint torques, thereby creating the illusion that the components are connected to the robot. Due to the intuitive nature of this technique, designing a virtual model controller requires the same skills as designing the mechanism itself. A high level control system can be cascaded with the low level virtual model controller to modulate the parameters of the virtual mechanisms. Discrete commands from the high level controller would then result in uid motion. An extension of Gardner's Partitioned Actuator Set Control method is developed. This method allows for the speci cation of constraints on the generalized forces which each serial path of a parallel mechanism can apply. Virtual model control has been applied to a bipedal walking robot. A simple algorithm utilizing a simple set of virtual components has successfully compelled the robot to walk eight consecutive steps. Thesis Supervisor: Gill A. Pratt Title: Assistant Professor of Electrical Engineering and Computer Science
StimDust: A 6.5mm3, wireless ultrasonic peripheral nerve stimulator with 82% peak chip efficiency

We present a 6.5mm3, 10mg, wireless peripheral nerve stimulator. The stimulator is powered and controlled through ultrasound from an external transducer and utilizes a single 750×750×750μm3 piezocrystal for downlink communication, powering, and readout, reducing implant volume and mass. An IC with 0.06mm2 active circuit area, designed in TSMC 65nm LPCMOS process, converts harvested ultrasound to stimulation charge with a peak efficiency of 82%. A custom wireless protocol that does not require a clock or memory circuits reduces on-chip power to 4μW when not stimulating. The encapsulated stimulator was cuffed to the sciatic nerve of an anesthetized rodent and demonstrated full-scale nerve activation in vivo. We achieve a highly efficient and temporally precise wireless peripheral nerve stimulator that is the smallest and lightest to our knowledge.
Adaptive Spectral Graph Convolutional Networks for Skeleton-Based Action Recognition

Traditional deep methods for skeleton-based action recognition usually structure the skeleton as a coordinates sequence or a pseudo-image to feed to RNNs or CNNs, which cannot explicitly exploit the natural connectivity among the joints. Recently, graph convolutional networks (GCNs), which generalize CNNs to more generic non-Euclidean structures, obtains remarkable performance for skeletonbased action recognition. In this work, we propose a novel adaptive spectral graph convolutional network which address the three major issues exposed in former GCN-based methods applied to action recognition: 1) The sampling function of graph convolutional operation applied in former methods is usually heuristically designed. We avoid the tricked definition of sampling function by transforming the GCN to frequency domain based on spectral graph theory. 2) The topology of the graph is set by hand and fixed over all layers, which may be not optimal for the action recognition task and the hierarchical CNN structures. In our model, the topology of the graph in each layer can be either uniformly or individually learned by BP algorithm, which can bring more flexibility and generality. 3) The first-order information (the coordinate of joints) is mainly used in former GCNs, while the second-order information (the length and direction of bones) is less exploited. A two-stream framework is proposed in this work to model both of the joints and bones information simultaneously. Extensive experiments on two large-scale datasets, NTU-RGB+D and Kinetics, demonstrate the performance of our model exceeds the state-of-the-art by a significant margin.
Doubly Convolutional Neural Networks

Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs). In this paper, we propose doubly convolutional neural networks (DCNNs), which significantly improve the performance of CNNs by further exploring this idea. In stead of allocating a set of convolutional filters that are independently learned, a DCNN maintains groups of filters where filters within each group are translated versions of each other. Practically, a DCNN can be easily implemented by a two-step convolution procedure, which is supported by most modern deep learning libraries. We perform extensive experiments on three image classification benchmarks: CIFAR-10, CIFAR-100 and ImageNet, and show that DCNNs consistently outperform other competing architectures. We have also verified that replacing a convolutional layer with a doubly convolutional layer at any depth of a CNN can improve its performance. Moreover, various design choices of DCNNs are demonstrated, which shows that DCNN can serve the dual purpose of building more accurate models and/or reducing the memory footprint without sacrificing the accuracy.
Performance Evaluation of GNSS for Train Localization

Global Navigation Satellite Systems (GNSS) are applicable to deliver train locations in real time. This train localization function should comply with railway functional safety standards; thus, the GNSS performance needs to be evaluated in consistent with railway EN 50126 standard [Reliability, Availability, Maintainability, and Safety (RAMS)]. This paper demonstrates the performance of the GNSS receiver for train localization. First, the GNSS performance and railway RAMS properties are compared by definitions. Second, the GNSS receiver measurements are categorized into three states (i.e., up, degraded, and faulty states). The relations between the states are illustrated in a stochastic Petri net model. Finally, the performance properties are evaluated using real data collected on the railway track in High Tatra Mountains in Slovakia. The property evaluation is based on the definitions represented by the modeled states.
Realistic User Behavior Modeling for Energy Saving in Residential Buildings

Due to the high costs of live research, performance simulation has become a widely accepted method of assessment for the quality of proposed solutions in this field. Additionally, being able to simulate the behavior of the future occupants of a residential building can be very useful since it can support both design-time and run-time decisions leading to reduced energy consumption through, e.g., the design of model predictive controllers that incorporate user behavior predictions. In this work, we provide a framework for simulating user behavior in residential buildings. In fact, we are interested in how to deal with all user behavior aspects so that these computer simulations can provide a realistic framework for testing alternative policies for energy saving.
Bayesian Reputation Modeling in E-Marketplaces Sensitive to Subjectivity, Deception and Change

We present a model for buying agents in e-marketplaces to interpret evaluations of sellers provided by other buying agents, known as advisors. The interpretation of seller evaluations is complicated by the inherent subjectivity of each advisor, the possibility that advisors may deliberately provide misleading evaluations to deceive competitors and the dynamic nature of seller and advisor behaviours that may naturally change seller evaluations over time. Using a Bayesian approach, we demonstrate how to cope with subjectivity, deception and change in a principled way. More specifically, by modeling seller properties and advisor evaluation functions as dynamic random variables, buyers can progressively learn a probabilistic model that naturally and “correctly” calibrates the interpretation of seller evaluations without having to resort to heuristics to explicitely detect and filter/discount unreliable seller evaluations. Our model, called BLADE, is shown empirically to achieve lower mean error in the estimation of seller properties when compared to other models for reasoning about advisor ratings of sellers in electronic maketplaces.
Building-in reliability in BCD (Bipolar-CMOS-DMOS) technologies

This paper discusses the “Building-In Reliability” (BIR) approach to process development, particularly for technologies integrating Bipolar, CMOS, and DMOS devices (so-called BCD technologies). Examples of BIR reliability assessments include gate oxide integrity (GOI) through Time-Dependent Dielectric Breakdown (TDDB) studies and degradation of laterally diffused MOS (LDMOS) devices by Hot-Carrier Injection (HCI) stress. TDDB allows calculation of gate oxide failure rates based on operating voltage waveforms and temperature. HCI causes increases in LDMOS resistance (Rdson), which decreases efficiency in power applications.
Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks

In this paper, we consider cooperative spectrum sensing based on energy detection in cognitive radio networks. Soft combination of the observed energy values from different cognitive radio users is investigated. Maximal ratio combination (MRC) is theoretically proved to be nearly optimal in low signal- to-noise ratio (SNR) region, an usual scenario in the context of cognitive radio. Both MRC and equal gain combination (EGC) exhibit significant performance improvement over conventional hard combination. Encouraged by the performance gain of soft combination, we propose a new softened hard combination scheme with two-bit overhead for each user and achieve a good tradeoff between detection performance and complexity. While traditionally energy detection suffers from an SNR wall caused by noise power uncertainty, it is shown in this paper that an SNR wall reduction can be achieved by employing cooperation among independent cognitive radio users.
Optical coherence tomography.

A technique called optical coherence tomography (OCT) has been developed for noninvasive cross-sectional imaging in biological systems. OCT uses low-coherence interferometry to produce a two-dimensional image of optical scattering from internal tissue microstructures in a way that is analogous to ultrasonic pulse-echo imaging. OCT has longitudinal and lateral spatial resolutions of a few micrometers and can detect reflected signals as small as approximately 10(-10) of the incident optical power. Tomographic imaging is demonstrated in vitro in the peripapillary area of the retina and in the coronary artery, two clinically relevant examples that are representative of transparent and turbid media, respectively.
Supporting presence in collaborative environments by haptic force feedback

An experimental study of interaction in a collaborative desktop virtual environment is described. The aim of the experiment was to investigate if added haptic force feedback in such an environment affects perceived virtual presence, perceived social presence, perceived task performance, and task performance. A between-group design was employed, where seven pairs of subjects used an interface with graphic representation of the environment, audio connection, and haptic force feedback. Seven other pairs of subjects used an interface without haptic force feedback, but with identical features otherwise. The PHANToM, a one-point haptic device, was used for the haptic force feedback, and a program especially developed for the purpose provided the virtual environment. The program enables for two individuals placed in different locations to simultaneously feel and manipulate dynamic objects in a shared desktop virtual environment. Results show that haptic force feedback significantly improves task performance, perceived task performance, and pereceived virtual presence in the collaborative distributed environment. The results suggest that haptic force feedback increases perceived social presence, but the difference is not significant.
Deep Compositional Question Answering with Neural Module Networks

Visual question answering is fundamentally compositional in nature—a question like where is the dog? shares substructure with questions like what color is the dog? and where is the cat? This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural “modules” into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.
Fast facial expression recognition based on local binary patterns

Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features called Local Binary Patterns (LBP) for facial expression recognition. Simulation results illustrate that LBP features are effective and efficient for facial expression recognition. A real-time implementation of the proposed approach is also demonstrated which can recognize expressions accurately at the rate of 4.8 frames per second.
Tales from the grave: Opposing autopsy reports from a body exhumed.

We report an autopsy case of a 42-year-old woman who, when discovered, had been dead in her apartment for approximately 1 week under circumstances involving treachery, assault and possible drug overdose. This case is unique as it involved two autopsies of the deceased by two different medical examiners who reached opposing conclusions. The first autopsy was performed about 10 days after death. The second autopsy was performed after an exhumation approximately 2 years after burial. Evidence collected at the crime scene included blood samples from which DNA was extracted and analysed, fingerprints and clothing containing dried body fluids. The conclusion of the first autopsy was accidental death due to cocaine toxicity; the conclusion of the second autopsy was death due to homicide given the totality of evidence. Suspects 1 and 2 were linked to the death of the victim by physical evidence and suspect 3 was linked by testimony. Suspect 1 received life in prison, and suspects 2 and 3 received 45 and 20 years in prison, respectively. This case indicates that cocaine toxicity is difficult to determine in putrefied tissue and that exhumations can be important in collecting forensic information. It further reveals that the combined findings of medical examiners, even though contradictory, are useful in determining the circumstances leading to death in criminal justice. Thus, this report demonstrates that such criminal circumstances require comparative forensic review and, in such cases, scientific conclusions can be difficult.
Calibration of Kinect for Xbox One and Comparison between the Two Generations of Microsoft Sensors

In recent years, the videogame industry has been characterized by a great boost in gesture recognition and motion tracking, following the increasing request of creating immersive game experiences. The Microsoft Kinect sensor allows acquiring RGB, IR and depth images with a high frame rate. Because of the complementary nature of the information provided, it has proved an attractive resource for researchers with very different backgrounds. In summer 2014, Microsoft launched a new generation of Kinect on the market, based on time-of-flight technology. This paper proposes a calibration of Kinect for Xbox One imaging sensors, focusing on the depth camera. The mathematical model that describes the error committed by the sensor as a function of the distance between the sensor itself and the object has been estimated. All the analyses presented here have been conducted for both generations of Kinect, in order to quantify the improvements that characterize every single imaging sensor. Experimental results show that the quality of the delivered model improved applying the proposed calibration procedure, which is applicable to both point clouds and the mesh model created with the Microsoft Fusion Libraries.
Percutaneous exposure to the nerve agent VX: Efficacy of combined atropine, obidoxime and diazepam treatment.

The nerve agent VX is most likely to enter the body via liquid contamination of the skin. After percutaneous exposure, the slow uptake into the blood, and its slow elimination result in toxic levels in plasma for a period of several hours. Consequently, this has implications for the development of toxic signs and for treatment onset. In the present study, clinical signs, toxicokinetics and effects on respiration, electroencephalogram and heart rate were investigated in hairless guinea pigs after percutaneous exposure to 500 microg/kg VX. We found that full inhibition of AChE and partial inhibition of BuChE in blood were accompanied by the onset of clinical signs, reflected by a decline in respiratory minute volume, bronchoconstriction and a decrease in heart rate. Furthermore, we investigated the therapeutic efficacy of a single dose of atropine, obidoxime and diazepam, administered at appearance of first clinical signs, versus that of repetitive dosing of these drugs on the reappearance of signs. A single shot treatment extended the period to detrimental physiological decline and death for several hours, whereas repetitive administration remained effective as long as treatment was continued. In conclusion, percutaneous VX poisoning showed to be effectively treatable when diagnosed on time and when continued over the entire period of time during which VX, in case of ineffective decontamination, penetrates the skin.
Fractal Basins of Attraction Associated with a Damped Newton's Method

An intriguing and unexpected result for students learning numerical analysis is that Newton’s method, applied to the simple polynomial z − 1 = 0 in the complex plane, leads to intricately interwoven basins of attraction of the roots. As an example of an interesting open question that may help to stimulate student interest in numerical analysis, we investigate the question of whether a damping method, which is designed to increase the likelihood of convergence for Newton’s method, modifies the fractal structure of the basin boundaries. The overlap of the frontiers of numerical analysis and nonlinear dynamics provides many other problems that can help to make numerical analysis courses interesting.
Receding Horizon Control of Nonlinear Systems : A Control Lyapunov Function Approach

With the advent of faster and cheaper computers, optimization based control methodologies have become a viable candidate for control of nonlinear systems. Over the past twenty years, a group of such control schemes have been successfully used in the process control industry where the processes are either intrinsically stable or have very large time constants. The purpose of this thesis is to provide a theoretical framework for synthesis of a class of optimization based control schemes, known as receding horizon control t echniques for nonlinear systems such as unmanned aerial vehicles. It is well known that unconstrained infinite horizon optimal control may be used to construct a stabilizing controller for a nonlinear system. In t his thesis, we show that similar stabilization results may be achieved using unconstrained finite horizon optimal control. The key idea is to approximate the tail of the infinite horizon costto-go using, as terminal cost, an appropriate control Lyapunov function (eLF) . A eLF can be thought of as generalization of the concept of a Lyapunov function to systems with inputs. Roughly speaking, the terminal eLF should provide an (incremental) upper bound on the cost . In this fashion , important stability characteristics may be retained without the use of t erminal constraints such as those employed by a number of other researchers. The absence of constraints allows a significant speedup in computation. Furthermore, it is shown that in order to guarantee stability, it suffices to satisfy an improvement property, thereby relaxing the requirement that truly optimal trajectories be found. We provide a complete analysis of the stability and region of attraction/operation properties of receding horizon control strategies that utilize finite horizon approximations in the proposed class. It is shown that the guaranteed region of operation contains that of the e LF controller and may be made as large as desired by increasing