nils-herrmann/clean_scirepeval huggingface dataset:
SiCloPe: Silhouette-Based Clothed People

We introduce a new silhouette-based representation for modeling clothed human bodies using deep generative models. Our method can reconstruct a complete and textured 3D model of a person wearing clothes from a single input picture. Inspired by the visual hull algorithm, our implicit representation uses 2D silhouettes and 3D joints of a body pose to describe the immense shape complexity and variations of clothed people. Given a segmented 2D silhouette of a person and its inferred 3D joints from the input picture, we first synthesize consistent silhouettes from novel view points around the subject. The synthesized silhouettes, which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction. We then infer the texture of the subject’s back view using the frontal image and segmentation mask as input to a conditional generative adversarial network. Our experiments demonstrate that our silhouette-based model is an effective representation and the appearance of the back view can be predicted reliably using an image-to-image translation network. While classic methods based on parametric models often fail for single-view images of subjects with challenging clothing, our approach can still produce successful results, which are comparable to those obtained from multi-view input.
Composing Simple Image Descriptions using Web-scale N-grams

Studying natural language, and especially how people describe the world around them can help us better understand the visual world. In turn, it can also help us in the quest to generate natural language that describes this world in a human manner. We present a simple yet effective approach to automatically compose image descriptions given computer vision based inputs and using web-scale n-grams. Unlike most previous work that summarizes or retrieves pre-existing text relevant to an image, our method composes sentences entirely from scratch. Experimental results indicate that it is viable to generate simple textual descriptions that are pertinent to the specific content of an image, while permitting creativity in the description – making for more human-like annotations than previous approaches.
How to reach top accuracy for a visual pedestrian warning system from a car?

Due to the wide applicability of pedestrian detection in surveillance and safety, this research topic has received much attention in computer vision literature. However, the focus of this research mainly lies in detecting and locating pedestrians individually as accurate as possible. In recent years, a number of datasets are captured using a forward looking camera from a car, which imposes the application of warning the driver when pedestrians are in front of the car. For such applications, it is not required to detect each pedestrian independently, but to generate an alarm when necessary. In this paper we explore techniques to boost the accuracy of recent channel-based algorithms in this application: algorithmic refinements as well as the inclusion of an LWIR image channel. We use the KAIST dataset which is constructed from image-pairs of both the visual and the LWIR spectrum, in day and night conditions. We study the influence of techniques that have shown success in literature.
The use of artificial neural networks in decision support in cancer: A systematic review

Artificial neural networks have featured in a wide range of medical journals, often with promising results. This paper reports on a systematic review that was conducted to assess the benefit of artificial neural networks (ANNs) as decision making tools in the field of cancer. The number of clinical trials (CTs) and randomised controlled trials (RCTs) involving the use of ANNs in diagnosis and prognosis increased from 1 to 38 in the last decade. However, out of 396 studies involving the use of ANNs in cancer, only 27 were either CTs or RCTs. Out of these trials, 21 showed an increase in benefit to healthcare provision and 6 did not. None of these studies however showed a decrease in benefit. This paper reviews the clinical fields where neural network methods figure most prominently, the main algorithms featured, methodologies for model selection and the need for rigorous evaluation of results.
Design of Boost-Flyback Single-Stage PFC converter for LED power supply without electrolytic capacitor for energy-storage

Light emitting diodes (LEDs) are likely to be used for general lighting applications due to their high efficiency and longer life. This paper presents the concept of applying large voltage ripple for energy storage into the Boost-Flyback Single -Stage PFC converter for the elimination of the electrical capacitor. Following proposed design procedure, the single stage PFC circuit with small energy storage capacitance can still achieve good output voltage regulation while preserving desired input power factor. The detailed theoretical analysis and design procedure of the Single-Stage PFC converter is presented. The experimental results obtained on a 60W prototype converter along with waveforms are presented.
Peripherally administered antibodies against amyloid β-peptide enter the central nervous system and reduce pathology in a mouse model of Alzheimer disease

One hallmark of Alzheimer disease is the accumulation of amyloid β-peptide in the brain and its deposition as plaques. Mice transgenic for an amyloid β precursor protein (APP) mini-gene driven by a platelet-derived (PD) growth factor promoter (PDAPP mice), which overexpress one of the disease-linked mutant forms of the human amyloid precursor protein, show many of the pathological features of Alzheimer disease, including extensive deposition of extracellular amyloid plaques, astrocytosis and neuritic dystrophy. Active immunization of PDAPP mice with human amyloid β-peptide reduces plaque burden and its associated pathologies. Several hypotheses have been proposed regarding the mechanism of this response. Here we report that peripheral administration of antibodies against amyloid β-peptide, was sufficient to reduce amyloid burden. Despite their relatively modest serum levels, the passively administered antibodies were able to enter the central nervous system, decorate plaques and induce clearance of preexisting amyloid. When examined in an ex vivo assay with sections of PDAPP or Alzheimer disease brain tissue, antibodies against amyloid β-peptide triggered microglial cells to clear plaques through Fc receptor-mediated phagocytosis and subsequent peptide degradation. These results indicate that antibodies can cross the blood–brain barrier to act directly in the central nervous system and should be considered as a therapeutic approach for the treatment of Alzheimer disease and other neurological disorders.
Small and fast moving object detection and tracking in sports video sequences

We propose an algorithm for detection and tracking of small and fast moving objects, like a ping pong ball or a cricket ball, in sports video sequences. For detection, the proposed method uses only motion as a cue; moreover it does not use any texture information. Our method is able to detect the object with very low contrast and negligible texture content. Along with detection, we also propose a tracking algorithm using the multiple filter bank approach. Thus we provide a complete solution. The tracking algorithm is able to track maneuvering as well as non-maneuvering movements of the object without using any a priori information about the target dynamics
Universal construction based on 3D printing electric motors: Steps towards self-replicating robots to transform space exploration

Through a recent confluence of technological capacities, self-replicating robots have become a potentially nearterm rather than speculative technology. In a practical sense, self-replicating robots can never become obsolete — the first self- replicating robots will spawn all future generations of robots, subject to deliberate upgrading and/or evolutionary change. Furthermore, this technology promises to revolutionise space exploration by bypassing the apparently-insurmountable problem of high launch costs. We present recent efforts in 3D printing the key robotic components required for any such self- replicating machine.
Students' perception of Kahoot!'s influence on teaching and learning

Technology is being increasingly integrated into teaching environments in view of enhancing students’ engagement and motivation. In particular, game-based student response systems have been found to foster students’ engagement, enhance classroom dynamics and improve overall students’ learning experience. This article presents outcomes of research that examined students’ experience using a game-based student response system, Kahoot!, in an Information Systems Strategy and Governance course at a research-intensive teaching university in New Zealand. We conducted semi-structured interviews with students to learn about the extent to which Kahoot! influence classroom dynamics, motivation and students’ learning process. Key findings revealed that Kahoot! enriched the quality of student learning in the classroom, with the highest influence reported on classroom dynamics, engagement, motivation and improved learning experience. Our findings also suggest that the use of educational games in the classroom is likely to minimise distractions, thereby improving the quality of teaching and learning beyond what is provided in conventional classrooms. Other factors that contributed to students’ enhanced learning included the creation and integration of appropriate content in Kahoot!, providing students with timely feedback, and game-play (gamification) strategies.
An intelligent market making strategy in algorithmic trading

Market making (MM) strategies have played an important role in the electronic stock market. However, the MM strategies without any forecasting power are not safe while trading. In this paper, we design and implement a twotier framework, which includes a trading signal generator based on a supervised learning approach and an event-driven MM strategy. The proposed generator incorporates the information within order book microstructure and market news to provide directional predictions. The MM strategy in the second tier trades on the signals and prevents itself from profit loss led by market trending. Using half a year price tick data from Tokyo Stock Exchange (TSE) and Shanghai Stock Exchange (SSE), and corresponding Thomson Reuters news of the same time period, we conduct the back-testing and simulation on an industrial near-to-reality simulator. From the empirical results, we find that 1) strategies with signals perform better than strategies without any signal in terms of average daily profit and loss (PnL) and sharpe ratio (SR), and 2) correct predictions do help MM strategies readjust their quoting along with market trending, which avoids the strategies triggering stop loss procedure that further realizes the paper loss.
A Siamese inception architecture network for person re-identification

Person re-identification is an extremely challenging problem as person’s appearance often undergoes dramatic changes due to the large variations of viewpoints, illuminations, poses, image resolutions, and cluttered backgrounds. How to extract discriminative features is one of the most critical ways to address these challenges. In this paper, we mainly focus on learning high-level features and combine the low-level, mid-level, and high-level features together to re-identify a person across different cameras. Firstly, we design a Siamese inception architecture network to automatically learn effective semantic features for person re-identification in different camera views. Furthermore, we combine multi-level features in null space with the null Foley–Sammon transform metric learning approach. In this null space, images of the same person are projected to a single point, which minimizes the intra-class scatter to the extreme and maximizes the relative inter-class separation simultaneously. Finally, comprehensive evaluations demonstrate that our approach achieves better performance on four person re-identification benchmark datasets, including Market-1501, CUHK03, PRID2011, and VIPeR.
Repeatable texture sampling with interchangeable patches

Rendering textures in real-time environments is a key task in computer graphics. This paper presents a new parallel patch-based method which allows repeatable sampling without cache, and does not create visual repetitions. Interchangeable patches of arbitrary shape are prepared in a preprocessing step, such that patches may lie over the boundary of other patches in a repeating tile. This compresses the example texture into an infinite texture map with small memory requirements, suitable for GPU and ray-tracing applications. The quality of textures rendered with this method can be tuned in the offline preprocessing step, and they can then be rendered in times comparable to Wang tiles. Experimental results demonstrate combined benefits in speed, memory requirements, and quality of randomisation when compared to previous methods.
k-Stacks: High-density valet parking for automated vehicles

Automated valet parking not only improves driving comfort, but can have a considerable impact on the urban landscape by reducing the required parking space. We present the first study of parking space optimization for automated valet parking with an in-depth theoretical analysis of the parking lot properties under various aspects, inluding the worst-case extraction time, total shunting distance, and the number of shunting operations (each per car). Most importantly, the proposed model bounds all these values. We verify the theoretical properties of our model in four simulated scenarios, one of which is based on real-world data from a downtown parking garage. We show that very good pick-up times of about 1 min are possible with very little overhead in terms of shunting distance and time, while providing a significantly improved parking density as compared to conventional parking lots.
GEOSTATISTICS : PAST , PRESENT , AND FUTURE

Geostatistical analyses were first developed in the 1950's as a result of interest in areal or block averages for ore reserves in the mining industry. Today, variogram estimation and spatial prediction (kriging) span all sciences where data exhibit spatial correlation. Theoretical properties of the spatial process are addressed under the distribution-free and likelihood-based perspectives. Strengths and weaknesses of these two dominant methodologies for modeling spatial correlation and predicting responses at unsampled sites and areal units are explored. Examples of hot spot detection and areal prediction show the flexibility of the Bayesian paradigm. Current bottlenecks and future directions in the field of geostatistics are discussed.
Simulation and analysis of DQ frame and P+Resonant controls for voltage source inverter to distributed generation

An inverter control design in DQ reference frame takes 3-phase sinusoidal signals and converts them to the DQ-frame to make a 2-phase based DC variable control. The error is calculated by subtracting the feedback from reference and then feedding it to PI controllers. The outputs of PI controllers are of the voltage form and transferred back to the ABC frame and fed to SPWM modules. P+Resonant controllers suggest establishing the control strategy in stationary (alpha-beta) frame. In this frame the axis are fix and three phase variables are mapped in a rotating vector with the same angular speed of the original signals. The P+Resonant controller has zero steady state error at the resonant frequency. This paper explains how to apply DQ-frame and P+Resonant for inverter controller design for grid connected single-phase Voltage Source Inverters (VSI). To evaluate the effectiveness of both control techniques, it is also presented simulated results with application of these controllers.
Scholarly communication and bibliometrics

Why devote an ARIST chapter to scholarly communication and bibliometrics, and why now? Bibliometrics already is a frequently covered ARIST topic, with chapters such as that by White and McCain (1989) on bibliometrics generally, White and McCain (1997) on visualization of literatures, Wilson and Hood (2001) on informetric laws, and Tabah (2001) on literature dynamics. Similarly, scholarly communication has been addressed in other ARIST chapters such as Bishop and Star (1996) on social informatics and digital libraries, Schamber (1994) on relevance and information behavior, and many earlier chapters on information needs and uses. More than a decade ago, the first author addressed the intersection of scholarly communication and bibliometrics with a journal special issue and an edited book (Borgman, 1990; Borgman & Paisley, 1989), and she recently examined interim developments (Borgman, 2000a, 2000c). This review covers the decade (1990–2000) since the comprehensive 1990 volume, citing earlier works only when necessary to explain the foundation for recent developments. Given the amount of attention these topics have received, what is new and exciting enough to warrant a full chapter in 2001? What is new is that electronic scholarly communication is reaching critical mass, and CHAPTER 1
Space Vector PWM for PMSM simulation using Matlab Simulink

Space Vector PWM (SVPWM) model is often built based on high-level functions and verified based on the output of the inverter or the model of the electrical motor with best possible accuracy. However, SVPWM implementation on digital hardware such as Field Programmable Gate Array (FPGA) and Application-specific Integrated Circuit (ASIC) is constrained by the limited resources and computation accuracy in these digital hardware compared to the mathematical model. The paper proposed a method that utilizes Matlab Simulink and Fixed-Point Toolbox to construct hardware-amenable SVPWM model. Using the proposed model, it is possible to estimate the digital hardware resources used and analyze the accuracy of the system before the actual designing process takes place. The model has been simulated and verified with signal switching patterns and output signals from the model of the electrical motor. Based on functional comparisons, it was found that the outputs of the SVPWM model are almost identical to the digital hardware implementation.
Developmental deficits in social perception in autism: the role of the amygdala and fusiform face area

Autism is a severe developmental disorder marked by a triad of deficits, including impairments in reciprocal social interaction, delays in early language and communication, and the presence of restrictive, repetitive and stereotyped behaviors. In this review, it is argued that the search for the neurobiological bases of the autism spectrum disorders should focus on the social deficits, as they alone are specific to autism and they are likely to be most informative with respect to modeling the pathophysiology of the disorder. Many recent studies have documented the difficulties persons with an autism spectrum disorder have accurately perceiving facial identity and facial expressions. This behavioral literature on face perception abnormalities in autism is reviewed and integrated with the functional magnetic resonance imaging (fMRI) literature in this area, and a heuristic model of the pathophysiology of autism is presented. This model posits an early developmental failure in autism involving the amygdala, with a cascading influence on the development of cortical areas that mediate social perception in the visual domain, specifically the fusiform "face area" of the ventral temporal lobe. Moreover, there are now some provocative data to suggest that visual perceptual areas of the ventral temporal pathway are also involved in important ways in representations of the semantic attributes of people, social knowledge and social cognition. Social perception and social cognition are postulated as normally linked during development such that growth in social perceptual skills during childhood provides important scaffolding for social skill development. It is argued that the development of face perception and social cognitive skills are supported by the amygdala-fusiform system, and that deficits in this network are instrumental in causing autism.
Gradual Typing for Functional Languages

Static and dynamic type systems have well-known strengths and weaknesses, and each is better suited for different programming tasks. There have been many efforts to integrate static and dynamic typing and thereby combine the benefits of both typing disciplines in the same language. The flexibility of static typing can be improved by adding a type Dynamic and a typecase form. The safety and performance of dynamic typing can be improved by adding optional type annotations or by performing type inference (as in soft typing). However, there has been little formal work on type systems that allow a programmer-controlled migration between dynamic and static typing. Thatte proposed Quasi-Static Typing, but it does not statically catch all type errors in completely annotated programs. Anderson and Drossopoulou defined a nominal type system for an object-oriented language with optional type annotations. However, developing a sound, gradual type system for functional languages with structural types is an open problem. In this paper we present a solution based on the intuition that the structure of a type may be partially known/unknown at compiletime and the job of the type system is to catch incompatibilities between the known parts of types. We define the static and dynamic semantics of a λ-calculus with optional type annotations and we prove that its type system is sound with respect to the simply-typed λ-calculus for fully-annotated terms. We prove that this calculus is type safe and that the cost of dynamism is “pay-as-you-go”.