nils-herrmann/clean_scirepeval huggingface dataset:
Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control

An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate frameworks. However, the new brain-decoding methods have also posed new challenges for analysis and statistical inference on the group level. We discuss why the usual procedure of performing t-tests on accuracy maps across subjects in order to produce a group statistic is inappropriate. We propose a solution to this problem for local MVPA approaches, which achieves higher sensitivity than other procedures. Our method uses random permutation tests on the single-subject level, and then combines the results on the group level with a bootstrap method. To preserve the spatial dependency induced by local MVPA methods, we generate a random permutation set and keep it fixed across all locations. This enables us to later apply a cluster size control for the multiple testing problem. More specifically, we explicitly compute the distribution of cluster sizes and use this to determine the p-values for each cluster. Using a volumetric searchlight decoding procedure, we demonstrate the validity and sensitivity of our approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, our results showed a higher sensitivity. We discuss the theoretical applicability and the practical advantages of our approach, and outline its generalization to other local MVPA methods, such as surface decoding techniques.
The Pyro Toolkit for AI and Robotics

Python robotics toolkit for exploring topics in AI and robotics. We present key abstractions that allow Pyro controllers to run unchanged on a variety of real and simulated robots. We demonstrate Pyro’s use in a set of curricular modules. We then describe how Pyro can provide a smooth transition for the student from symbolic agents to real-world robots, which significantly reduces the cost of learning to use robots. Finally we show how Pyro has been successfully integrated into existing AI and robotics courses.
Fenofibrate Induces Ketone Body Production in Melanoma and Glioblastoma Cells

Ketone bodies [beta-hydroxybutyrate (bHB) and acetoacetate] are mainly produced in the liver during prolonged fasting or starvation. bHB is a very efficient energy substrate for sustaining ATP production in peripheral tissues; importantly, its consumption is preferred over glucose. However, the majority of malignant cells, particularly cancer cells of neuroectodermal origin such as glioblastoma, are not able to use ketone bodies as a source of energy. Here, we report a novel observation that fenofibrate, a synthetic peroxisome proliferator-activated receptor alpha (PPARa) agonist, induces bHB production in melanoma and glioblastoma cells, as well as in neurospheres composed of non-transformed cells. Unexpectedly, this effect is not dependent on PPARa activity or its expression level. The fenofibrate-induced ketogenesis is accompanied by growth arrest and downregulation of transketolase, but the NADP/NADPH and GSH/GSSG ratios remain unaffected. Our results reveal a new, intriguing aspect of cancer cell biology and highlight the benefits of fenofibrate as a supplement to both canonical and dietary (ketogenic) therapeutic approaches against glioblastoma.
A Hybrid Approach to Query Answering under Expressive Datalog+/-

Datalog is a family of ontology languages that combine good computational properties with high expressive power. Datalog languages are provably able to capture many relevant Semantic Web languages. In this paper we consider the class of weakly-sticky (WS) Datalog programs, which allow for certain useful forms of joins in rule bodies as well as extending the well-known class of weakly-acyclic TGDs. So far, only nondeterministic algorithms were known for answering queries on WS Datalog programs. We present novel deterministic query answering algorithms under WS Datalog. In particular, we propose: (1) a bottom-up grounding algorithm based on a query-driven chase, and (2) a hybrid approach based on transforming a WS program into a so-called sticky one, for which query rewriting techniques are known. We discuss how our algorithms can be optimized and effectively applied for query answering in real-world scenarios.
Different mechanisms of decapitation: three classic and one unique case history.

Three classic cases and one exceptional case are reported. The unique case of decapitation took place in a traffic accident, while the others were seen after homicide, vehicle-assisted suicide, and after long-jump hanging. Thorough scene examinations were performed, and photographs from the scene were available in all cases. Through the autopsy of each case, the mechanism for the decapitation in each case was revealed. The severance lines were through the neck and the cervical vertebral column, except for in the motor vehicle accident case, where the base of skull was fractured. This case was also unusual as the mechanism was blunt force. In the homicide case, the mechanism was the use of a knife combined with a saw, while in the two last cases, a ligature made the cut through the neck. The different mechanisms in these decapitations are suggested.
Earned scope management: a case of study of scope performance using use cases as scope in a real project

Three of the major constraints on projects are scope (size), cost (money), and time (schedule). While the Earned Value Management (EVM) and Earned Schedule (ES) techniques manage cost and time constraints, they do not explicitly tackle the scope constraint. While scope management is recognized as a key success factor in software projects, there is a lack of formal techniques in the literature to manage scope. This paper proposes an Earned Scope Management (ESM) technique based on non-conventional scope units such as Use Cases, for a software project scope.
Facile coating of manganese oxide on tin oxide nanowires with high-performance capacitive behavior.

In this paper, a very simple solution-based method is employed to coat amorphous MnO2 onto crystalline SnO2 nanowires grown on stainless steel substrate, which utilizes the better electronic conductivity of SnO2 nanowires as the supporting backbone to deposit MnO2 for supercapacitor electrodes. Cyclic voltammetry (CV) and galvanostatic charge/discharge methods have been carried out to study the capacitive properties of the SnO2/MnO2 composites. A specific capacitance (based on MnO2) as high as 637 F g(-1) is obtained at a scan rate of 2 mV s(-1) (800 F g(-1) at a current density of 1 A g(-1)) in 1 M Na2SO4 aqueous solution. The energy density and power density measured at 50 A g(-1) are 35.4 W h kg(-1) and 25 kW kg(-1), respectively, demonstrating the good rate capability. In addition, the SnO2/MnO2 composite electrode shows excellent long-term cyclic stability (less than 1.2% decrease of the specific capacitance is observed after 2000 CV cycles). The temperature-dependent capacitive behavior is also discussed. Such high-performance capacitive behavior indicates that the SnO2/MnO2 composite is a very promising electrode material for fabricating supercapacitors.
Biological and nanotechnological applications using interactions between ionic liquids and nucleic acids

Nucleic acids have emerged as powerful biological and nanotechnological tools. In biological and nanotechnological experiments, methods of extracting and purifying nucleic acids from various types of cells and their storage are critical for obtaining reproducible experimental results. In nanotechnological experiments, methods for regulating the conformational polymorphism of nucleic acids and increasing sequence selectivity for base pairing of nucleic acids are important for developing nucleic acid-based nanomaterials. However, dearth of media that foster favourable behaviour of nucleic acids has been a bottleneck for promoting the biology and nanotechnology using the nucleic acids. Ionic liquids (ILs) are solvents that may be potentially used for controlling the properties of the nucleic acids. Here, we review researches regarding the behaviour of nucleic acids in ILs. The efficiency of extraction and purification of nucleic acids from biological samples is increased by IL addition. Moreover, nucleic acids in ILs show long-term stability, which maintains their structures and enhances nuclease resistance. Nucleic acids in ILs can be used directly in polymerase chain reaction and gene expression analysis with high efficiency. Moreover, the stabilities of the nucleic acids for duplex, triplex, and quadruplex (G-quadruplex and i-motif) structures change drastically with IL cation-nucleic acid interactions. Highly sensitive DNA sensors have been developed based on the unique changes in the stability of nucleic acids in ILs. The behaviours of nucleic acids in ILs detailed here should be useful in the design of nucleic acids to use as biological and nanotechnological tools.
Regularizing Relation Representations by First-order Implications

Methods for automated knowledge base construction often rely on trained fixed-length vector representations of relations and entities to predict facts. Recent work showed that such representations can be regularized to inject first-order logic formulae. This enables to incorporate domain-knowledge for improved prediction of facts, especially for uncommon relations. However, current approaches rely on propositionalization of formulae and thus do not scale to large sets of formulae or knowledge bases with many facts. Here we propose a method that imposes first-order constraints directly on relation representations, avoiding costly grounding of formulae. We show that our approach works well for implications between pairs of relations on artificial datasets.
Always-On Speech Recognition Using TrueNorth, a Reconfigurable, Neurosynaptic Processor

Deep neural networks (DNN) have been shown to be very effective at solving challenging problems in several areas of computing, including vision, speech, and natural language processing. However, traditional platforms for implementing these DNNs are often very power hungry, which has lead to significant efforts in the development of configurable platforms capable of implementing these DNNs efficiently. One of these platforms, the IBM TrueNorth processor, has demonstrated very low operating power in performing visual computing and neural network classification tasks in real-time. The neuron computation, synaptic memory, and communication fabrics are all configurable, so that a wide range of network types and topologies can be mapped to TrueNorth. This reconfigurability translates into the capability to support a wide range of low-power functions in addition to feed-forward DNN classifiers, including for example, the audio processing functions presented here.In this work, we propose an end-to-end audio processing pipeline that is implemented entirely on a TrueNorth processor and designed to specifically leverage the highly-parallel, low-precision computing primitives TrueNorth offers. As part of this pipeline, we develop an audio feature extractor (LATTE) designed for implementation on TrueNorth, and explore the tradeoffs among several design variants in terms of accuracy, power, and performance. We customize the energy-efficient deep neuromorphic networks structures that our design utilizes as the classifier and show how classifier parameters can trade between power and accuracy. In addition to enabling a wide range of diverse functions, the reconfigurability of TrueNorth enables re-training and re-programming the system to satisfy varying energy, speed, area, and accuracy requirements. The resulting system's end-to-end power consumption can be as low as <inline-formula><tex-math notation="LaTeX"> $14.43\text{mW}$</tex-math><alternatives><inline-graphic xlink:href="tsai-ieq1-2630683.gif"/></alternatives> </inline-formula>, which would give up to 100 hours of continuous usage with button cell batteries (CR3023 <inline-formula><tex-math notation="LaTeX">$1.5\; \text{Whr}$</tex-math><alternatives> <inline-graphic xlink:href="tsai-ieq2-2630683.gif"/></alternatives></inline-formula>) or 450 hours with cellphone batteries (iPhone 6s <inline-formula><tex-math notation="LaTeX">$6.55\; \text{Whr}$</tex-math><alternatives> <inline-graphic xlink:href="tsai-ieq3-2630683.gif"/></alternatives></inline-formula>).
Power electronic transformer (PET) converter: Design of a 1.2MW demonstrator for traction applications

A power electronic transformer converter made of cascaded multiple cells with a medium frequency link can replace the bulky traditional transformer in various applications with several advantages, including reduced size and weight, and energy savings. This paper describes the design of a 1.2MW power electronic transformer (PET) demonstrator for traction applications. Several design challenges, i.e. the selection of the IGBT modules, the design of the multiple-functional medium frequency transformer, and the mechanical arrangement to achieve the high voltage insulation between the grid voltage and ground potential, have been discussed. The experimental results, obtained from the developed PET demonstrator, are presented.
Automated spatiotemporal and semantic information extraction for hazards

Automated spatiotemporal and semantic information extraction for hazards." PhD (Doctor of Philosophy) thesis, ii To everyone who has supported and helped me over the years. iii ACKNOWLEDGMENTS It has been a long journey since I started my Ph.D. study at the University of Iowa. During the six years, I received many supports from my adviser, professors, my family and friends. First of all, I would like to give my sincerest thanks to my adviser Dr. Kathleen Stewart for her enormous contributions of advices and time. She brought me to this research field to start the journey. She gave me a lot of inspirations to explore the new path, and kept me on the right track. I also want to thank my committee members Dr. time and supports. Thanks to all professors and colleagues in the Department of Geographical and Sustainability Science at University of Iowa for their help during my study period. I would like to thank my family and friends for all their supports and love during the 6 years for my Ph.D. study. Last but not least, I want to give my best thanks to my husband. He gives me a lot of supports and encouragement throughout my study. Without his supports, I could not achieve to the destination of the journey. Thank you to all of you.  This dissertation explores three research topics related to automated spatiotemporal and semantic information extraction about hazard events from Web news reports and other social media. The dissertation makes a unique contribution of bridging geographic information science, geographic information retrieval, and natural language processing. Geographic information retrieval and natural language processing techniques are applied to extract spatiotemporal and semantic information automatically from Web documents, to retrieve information about patterns of hazard events that are not explicitly described in the texts. Chapters 2, 3 and 4 can be regarded as three standalone journal papers. The research topics covered by the three chapters are related to each other, and are presented in a sequential way. Chapter 2 begins with an investigation of methods for automatically extracting spatial and temporal information about hazards from Web news reports. A set of rules is developed to combine the spatial and temporal information contained in the reports based on how this information is presented in text in order to capture the dynamics of hazard events (e.g., changes in event locations, new events occurring) as they …
Surgery Simulation – A Challenge for Graphics and Vision

Medicine is an extremely challenging field of research, which has been more than any other discipline of fundamental importance for human existence. The variety and inherent complexity of unsolved problems has made it a major driving force for many natural and engineering sciences. Hence, from the early days of Computer Graphics and Computer Vision the medical field has been one of most important application areas with an enduring provision of fascinating research challenges. Conversely, individual Graphics and Computer Vision tools and methods have become increasingly irreplaceable in modern medicine. In this article I will present my personal view of the interdisciplinary field of surgery simulation which encompasses many different disciplines including Medicine, Computer Graphics, Computer Vision, Mechanics,  I will discuss the individual tasks, challenges and problems arising during the design and implementation of advanced surgery simulation environments, where my emphasis is directed towards the roles of graphics and vision.
Sonar-Based Detection and Tracking of a Diver for Underwater Human-Robot Interaction Scenarios

An underwater robotic assistant could help a human diver by illuminating work areas, fetching tools from the surface, or monitoring the diver's activity for abnormal behavior. However, in order for basic Underwater Human-Robot Interaction (UHRI) to be successful, the robotic assistant has to first be able to detect and track the diver. This paper discusses the detection and tracking of a diver with a high-frequency forward-looking sonar. The first step in the diver detection involves utilizing classical 2D image processing techniques to segment moving objects in the sonar image. The moving objects are then passed through a blob detection algorithm, and then the blob clusters are processed by the cluster classification process. Cluster classification is accomplished by matching observed cluster trajectories with trained Hidden Markov Models (HMM), which results in a cluster being classified as either a diver or clutter. Real-world results show that a moving diver can be autonomously distinguished from stationary objects in a noisy sonar image and tracked.
Operant Procedures for Assessing Behavioral Flexibility in Rats

Executive functions consist of multiple high-level cognitive processes that drive rule generation and behavioral selection. An emergent property of these processes is the ability to adjust behavior in response to changes in one's environment (i.e., behavioral flexibility). These processes are essential to normal human behavior, and may be disrupted in diverse neuropsychiatric conditions, including schizophrenia, alcoholism, depression, stroke, and Alzheimer's disease. Understanding of the neurobiology of executive functions has been greatly advanced by the availability of animal tasks for assessing discrete components of behavioral flexibility, particularly strategy shifting and reversal learning. While several types of tasks have been developed, most are non-automated, labor intensive, and allow testing of only one animal at a time. The recent development of automated, operant-based tasks for assessing behavioral flexibility streamlines testing, standardizes stimulus presentation and data recording, and dramatically improves throughput. Here, we describe automated strategy shifting and reversal tasks, using operant chambers controlled by custom written software programs. Using these tasks, we have shown that the medial prefrontal cortex governs strategy shifting but not reversal learning in the rat, similar to the dissociation observed in humans. Moreover, animals with a neonatal hippocampal lesion, a neurodevelopmental model of schizophrenia, are selectively impaired on the strategy shifting task but not the reversal task. The strategy shifting task also allows the identification of separate types of performance errors, each of which is attributable to distinct neural substrates. The availability of these automated tasks, and the evidence supporting the dissociable contributions of separate prefrontal areas, makes them particularly well-suited assays for the investigation of basic neurobiological processes as well as drug discovery and screening in disease models.
Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images

Breast cancer is one of the leading causes of cancer death among women worldwide. The proposed approach comprises three steps as follows. Firstly, the image is preprocessed to remove speckle noise while preserving important features of the image. Three methods are investigated, i.e., Frost Filter, Detail Preserving Anisotropic Diffusion, and Probabilistic Patch-Based Filter. Secondly, Normalized Cut or Quick Shift is used to provide an initial segmentation map for breast lesions. Thirdly, a postprocessing step is proposed to select the correct region from a set of candidate regions. This approach is implemented on a dataset containing 20 B-mode ultrasound images, acquired from UDIAT Diagnostic Center of Sabadell, Spain. The overall system performance is determined against the ground truth images. The best system performance is achieved through the following combinations: Frost Filter with Quick Shift, Detail Preserving Anisotropic Diffusion with Normalized Cut and Probabilistic Patch-Based with Normalized Cut.
Review of developments in BLDC motor controllers along with study of four-quadrant operation and active Power Factor correction

This paper presents a review on recent developments in BLDC motor controllers and studies on four quadrant operation of BLDC drive along with active PFC. The main areas reviewed include Sensor-less control, Direct Torque Control (DTC), Fuzzy logic control, controller for four quadrant operation and active Power Factor Corrected (PFC) converter fed BLDC motor drive. A comprehensive study has been done on four quadrant operation and active PFC converter fed BLDC motor drive with simulation in MATLAB/SIMULINK. The proposed control algorithm for four quadrant operation detects the speed reversal requirement and changes the quadrant of operation accordingly. In PFC converter fed BLDC motor drive, a Boost converter working in continuous current mode is designed to improve the supply power factor.
On Batcher's Merge Sorts as Parallel Sorting Algorithms

In this paper we examine the average running times of Batcher's bitonic merge and Batcher's odd-even merge when they are used as parallel merging algorithms. It has been shown previously that the running time of odd-even merge can be upper bounded by a function of the maximal rank diierence for elements in the two input sequences. Here we give an almost matching lower bound for odd-even merge as well as a similar upper bound for (a special version of) bitonic merge. From this follows that the average running time of odd-even merge (bitonic merge) is ((n=p)(1+log(1+p 2 =n))) (O((n=p)(1+log(1+p 2 =n))), resp.) where n is the size of the input and p is the number of processors used. Using these results we then show that the average running times of odd-even merge sort and bitonic merge sort are O((n=p)(logn + (log(1 + p 2 =n)) 2)), that is, the two algorithms are optimal on the average if n p 2 =2 p log p. The derived bounds do not allow to compare the two sorting algorithms directly, thus we also present experimental results, obtained by a simulation program, for various sizes of input and numbers of processors.