nils-herrmann/clean_scirepeval huggingface dataset:
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flowbased models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models. Our implementation is available at https://github.com/aravind0706/flowpp.
Sensitivity and specificity of WAIS-III/WMS-III demographically corrected factor scores in neuropsychological assessment.

This study explored the neurodiagnostic utility of 6 factor scores identified by recent exploratory and confirmatory factor analyses of the WAIS-III and WMS-III: Verbal Comprehension, Perceptual Organization, Processing Speed, Working Memory, Auditory Memory and Visual Memory. Factor scores were corrected for age. education, sex and ethnicity to minimize their influences on diagnostic accuracy. Cut-offs at 1, 1.5 and 2 standard deviations (SDs) below the standardization sample mean were applied to data from the overlapping test normative samples (N = 1073) and 6 clinical samples described in the WAIS-III/WMS-III Technical Manual (N = 126). The analyses suggest that a I SD cut-off yields the most balanced levels of sensitivity and specificity; more strict (1.5 or 2 SD) cut-offs generally result in trading modest gains in specificity for larger losses in sensitivity. Finally, using combinations of WAIS-III/WMS-III factors together as test batteries, we explored the sensitivity and specificity implications of varying diagnostic decision rules (e.g.,1 vs. 2 impaired factors = "impairment"). For most of the disorders considered here, even a small (e.g., 3 factor) WAIS-III/WMS-III battery provides quite good overall diagnostic accuracy.
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

The success of convolutional neural networks (CNNs) in computer vision applications has been accompanied by a significant increase of computation and memory costs, which prohibits its usage on resource-limited environments such as mobile or embedded devices. To this end, the research of CNN compression has recently become emerging. In this paper, we propose a novel filter pruning scheme, termed structured sparsity regularization (SSR), to simultaneously speedup the computation and reduce the memory overhead of CNNs, which can be well supported by various off-the-shelf deep learning libraries. Concretely, the proposed scheme incorporates two different regularizers of structured sparsity into the original objective function of filter pruning, which fully coordinates the global outputs and local pruning operations to adaptively prune filters. We further propose an Alternative Updating with Lagrange Multipliers (AULM) scheme to efficiently solve its optimization. AULM follows the principle of ADMM and alternates between promoting the structured sparsity of CNNs and optimizing the recognition loss, which leads to a very efficient solver (2.5× to the most recent work that directly solves the group sparsity based regularization). Moreover, by imposing the structured sparsity, the online inference is extremely memory-light, since the number of filters and the output feature maps are simultaneously reduced. The proposed scheme has been deployed to a variety of state-of-the-art CNN structures including LeNet, AlexNet, VGGNet, ResNet and GoogLeNet over different datasets. Quantitative results demonstrate that the proposed scheme achieves superior performance over the state-of-the-art methods. We further demonstrate the proposed compression scheme for the task of transfer learning, including domain adaptation and object detection, which also show exciting performance gains over the state-of-the-arts. Our source code is available at https://github.com/ShaohuiLin/SSR.
Spatiotemporal user and place modelling on the geo-social web

Users of Location-Based Social Networks (LBSN) are giving away information about their whereabouts, and their interactions in the geographic space. In comparison to other types of personal data, location data are sensitive and can reveal user’s daily routines, activities, experiences and interests in the physical world. As a result, the user is facing an information overload that overburdens him to make a satisfied decision on where to go or what to do in a place. Thus, finding the matching places, users and content is one of the key challenges in LSBNs. This thesis investigates the different dimensions of data collected on LBSNs and proposes a user and place modelling framework. In particular, this thesis proposes a novel approach for the construction of different views of personal user profiles that reflect their interest in geographic places, and how they interact with geographic places. Three novel modelling frameworks are proposed, the static user model, the dynamic user model and the semantic place model. The static user model is a basic model that is used to represent the overall user interactions towards places. On the other hand, the dynamic user model captures the change of the user’s preferences over time. The semantic place model identifies user activities in places and models the relationships between places, users, implicit place types, and implicit activities. The proposed models demonstrate how geographic place characteristics as well as implicit user interactions in the physical space can further enrich the user profiles. The enrichment method proposed is a novel method that combines the semantic and the spatial influences into user profiles. Evaluation of the proposed methods is carried out using realistic data sets
OKBC: A Programmatic Foundation for Knowledge Base Interoperability

The technology for building large knowledge bases (KBs) is yet to witness a breakthrough so that a KB can be constructed by the assembly of prefabricated knowledge components. Knowledge components include both pieces of domain knowledge (for example, theories of economics or fault diagnosis) and KB tools (for example, editors and theorem provers). Most of the current KB development tools can only manipulate knowledge residing in the knowledge representation system (KRS) for which the tools were originally developed. Open Knowledge Base Connectivity (OKBC) is an application programming interface for accessing KRSs, and was developed to enable the construction of reusable KB tools. OKBC improves upon its predecessor, the Generic Frame Protocol (GFP), in several significant ways. OKBC can be used with a much larger range of systems because its knowledge model supports an assertional view of a KRS. OKBC provides an explicit treatment of entities that are not frames, and it has a much better way of controlling inference and specifying default values. OKBC can be used on practically any platform because it supports network transparency and has implementations for multiple programming languages. In this paper, we discuss technical design issues faced in the development of OKBC, highlight how OKBC improves upon GFP, and report on practical experiences in using it.
Considerations for classroom seating arrangements and the role of teacher characteristics and beliefs

As a part of classroom management, teachers face the question of how and where to seat their students. However, it is far from clear what considerations teachers have when making seating arrangements. Therefore, in this study seating arrangement considerations from 50 teachers in grades 4–6 of elementary school were assessed. In Phase 1, teachers were interviewed about their goals and considerations for classroom seating arrangements. Teachers mentioned between 2 and 19 reasons for placing students at specific places in the classroom, with mostly academic considerations. They mainly preferred arrangements in small groups to promote student cooperation. In Phase 2, teachers completed a questionnaire about seating arrangements. This allowed us to examine individual differences between teachers related to gender, years of experience, and beliefs, and the concurrence between the interview and questionnaire data. Teachers reported multiple and various considerations for seating arrangements. Correlations with their general student-oriented or subject-oriented beliefs and personal characteristics were low. The concurrence between measurement methods also was low. The discussion focused on teacher awareness of classroom seating arrangements as an important part of classroom management and a tool for prevention and intervention. & Mariola C. Gremmen m.c.gremmen@rug.nl Yvonne H. M. van den Berg y.vandenberg@psych.ru.nl Eliane Segers e.segers@pwo.ru.nl Antonius H. N. Cillessen a.cillessen@psych.ru.nl 1 Behavioural Science Institute, Radboud University, Montessorilaan 3, 6500 HE Nijmegen, The Netherlands 2 Present Address: Grote Rozenstraat 31, 9712 TG Groningen, The Netherlands 123
On The Differential Privacy of Thompson Sampling With Gaussian Prior

We show that Thompson Sampling with Gaussian Prior as detailed by Algorithm 2 in (Agrawal & Goyal, 2013) is already differentially private. Theorem 1 show that it enjoys a very competitive privacy loss of only O(ln T ) after T rounds. Finally, Theorem 2 show that one can control the privacy loss to any desirable ǫ level by appropriately increasing the variance of the samples from the Gaussian posterior. And this increases the regret only by a term of O( ln T ǫ ). This compares favorably to the previous result for Thompson Sampling in the literature ((Mishra & Thakurta, 2015)) which adds a term of O( ln T ǫ ) to the regret in order to achieve the same privacy level. Furthermore, our result use the basic Thompson Sampling with few modifications whereas the result of (Mishra & Thakurta, 2015) required sophisticated constructions.
Power and thermal characterization of a lithium-ion battery pack for hybrid-electric vehicles

A 1D electrochemical, lumped thermal model is used to explore pulse power limitations and thermal behavior of a 6 Ah, 72 cell, 276 V nominal i-ion hybrid-electric vehicle (HEV) battery pack. Depleted/saturated active material Li surface concentrations in the negative/positive electrodes onsistently cause end of high-rate (∼25 C) pulse discharge at the 2.7 V cell−1 minimum limit, indicating solid-state diffusion is the limiting echanism. The 3.9 V cell−1 maximum limit, meant to protect the negative electrode from lithium deposition side reaction during charge, is overly onservative for high-rate (∼15 C) pulse charges initiated from states-of-charge (SOCs) less than 100%. Two-second maximum pulse charge rate rom the 50% SOC initial condition can be increased by as much as 50% without risk of lithium deposition. Controlled to minimum/maximum oltage limits, the pack meets partnership for next generation vehicles (PNGV) power assist mode pulse power goals (at operating temperatures 16 ◦C), but falls short of the available energy goal. In a vehicle simulation, the pack generates heat at a 320 W rate on a US06 driving cycle at 25 ◦C, with more heat generated at lower temperatures. ess aggressive FUDS and HWFET cycles generate 6–12 times less heat. Contact resistance ohmic heating dominates all other mechanisms, ollowed by electrolyte phase ohmic heating. Reaction and electronic phase ohmic heats are negligible. A convective heat transfer coefficient of = 10.1 W m−2 K−1 maintains cell temperature at or below the 52 ◦C PNGV operating limit under aggressive US06 driving.
From single cells to deep phenotypes in cancer

In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.
Generate Identity-Preserving Faces by Generative Adversarial Networks

Generating identity-preserving faces aims to generate various face images keeping the same identity given a target face image. Although considerable generative models have been developed in recent years, it is still challenging to simultaneously acquire high quality of facial images and preserve the identity. Here we propose a compelling method using generative adversarial networks (GAN). Concretely, we leverage the generator of trained GAN to generate plausible faces and FaceNet as an identity-similarity discriminator to ensure the identity. Our method is the first one to generate identitypreserving faces using GAN. Experimental results show that our method is qualified to generate both plausible and identity-preserving faces with high quality. In addition, our method provides a universal framework which can be realized in various ways by combining different face generators and identity-similarity discriminator.
Automatic Modulation Classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Risk Minimization Principle

As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. Automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.
IDEA: Planning at the Core of Autonomous Reactive Agents

Several successful autonomous systems are separated into technologically diverse functional layers operating at different levels of abstraction. This diversity makes them difficult to implement and validate. In this paper, we present IDEA (Intelligent Distributed Execution Architecture) a unified planning and execution framework. In IDEA a layered system can be implemented as separate agents, one per layer, each representing its interactions with the world in a model. At all level, the model representation primitives and their semantics is the same. Moreover, each agent relies on a single model, plan database, plan runner and on a variety of planners, both reactive and deliberative. The framework allows the specification of agents that operate within a guaranteed reaction time and supports flexible specification of reactive vs. deliberative agent behavior. Within the IDEA framework we are working to fully duplicate the functionalities of the DS1 Remote Agent and extend it to domains of higher complexity than autonomous spacecraft
Real-time categorization of driver's gaze zone using the deep learning techniques

This paper presents a study in which driver's gaze zone is categorized using new deep learning techniques. Since the sequence of gaze zones of a driver reflects precisely what and how he behaves, it allows us infer his drowsiness, focusing or distraction by analyzing the images coming from a camera. A Haar feature based face detector is combined with a correlation filter based MOSS tracker for the face detection task to handle a tough visual environment in the car. Driving database is a big-data which was constructed using a recording setup within a compact sedan by driving around the urban area. The gaze zones consist of 9 categories depending on where a driver is looking at during driving. A convolutional neural network is trained to categorize driver's gaze zone from a given face detected image using a multi-GPU platform, and then its network parameters are transferred to a GPU within a PC running on Windows to operate in the real-time basis. Result suggests that the correct rate of gaze zone categorization reaches to 95% in average, indicating that our system outperforms the state-of-art gaze zone categorization methods based on conventional computer vision techniques.
Modeling and Control of a Flux-Modulated Compound-Structure Permanent-Magnet Synchronous Machine for Hybrid Electric Vehicles

The compound-structure permanent-magnet synchronous machine (CS-PMSM), comprising a double rotor machine (DRM) and a permanent-magnet (PM) motor, is a promising electronic-continuously variable transmission (e-CVT) concept for hybrid electric vehicles (HEVs). By CS-PMSM, independent speed and torque control of the vehicle engine is realized without a planetary gear unit. However, the slip rings and brushes of the conventional CS-PMSM are considered a major drawback for vehicle application. In this paper, a brushless flux-modulated CS-PMSM is investigated. The operating principle and basic working modes of the CS-PMSM are discussed. Mathematical models of the CS-PMSM system are given, and joint control of the two integrated machines is proposed. As one rotor of the DRM is mechanically connected with the rotor of the PM motor, special rotor position detection and torque allocation methods are required. Simulation is carried out by Matlab/Simulink, and the feasibility of the control system is proven. Considering the complexity of the controller, a single digital signal processor (DSP) is used to perform the interconnected control of dual machines instead of two separate ones, and a typical hardware implementation is proposed.
A Sensing Chair Using Pressure Distribution Sensors

One challenge in multimodal interface research is the lack of robust subsystems that support multimodal interactions. By focusing on a chair—an object that is involved in virtually all human–computer interactions, the sensing chair project enables an ordinary office chair to become aware of its occupant’s actions and needs. Surface-mounted pressure distribution sensors are placed over the seatpan and backrest of the chair for real time capturing of contact information between the chair and its occupant. Given the similarity between a pressure distribution map and a grayscale image, pattern recognition techniques commonly used in computer and robot vision, such as principal components analysis, have been successfully applied to solving the problem of sitting posture classification. The current static posture classification system operates in real time with an overall classification accuracy of 96% and 79% for familiar (people it had felt before) and unfamiliar users, respectively. Future work is aimed at a dynamic posture tracking system that continuously tracks not only steady-state (static) but transitional (dynamic) sitting postures. Results reported here form important stepping stones toward an intelligent chair that can find applications in many areas including multimodal interfaces, intelligent environment, and safety of automobile operations.
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data

We investigate the problem of generating fast approximate a nswers to queries posed to large sparse binary data sets. We focus in particular on probabilistic mode l-based approaches to this problem and develop a number of techniques that are significantly more accurate t han a baseline independence model. In particular, we introduce two techniques for building probabil ist c models from frequent itemsets: the itemset maximum entropy method, and the itemset inclusion-exclusi on model. In the maximum entropy method we treat itemsets as constraints on the distribution of the q uery variables and use the maximum entropy principle to build a joint probability model for the query at tributes online. In the inclusion-exclusion model itemsets and their frequencies are stored in a data structur e alled an ADtree that supports an efficient implementation of the inclusion-exclusion principle in orde r to answer the query. We empirically compare these two itemset-based models to direct querying of the ori ginal data, querying of samples of the original data, as well as other probabilistic models such as the indep endence model, the Chow-Liu tree model, and the Bernoulli mixture model. These models are able to handle high-dimensionality (hundreds or thousands of attributes), whereas most other work on this topic has foc used on relatively low-dimensional OLAP problems. Experimental results on both simulated and realwor d transaction data sets illustrate various fundamental tradeoffs between approximation error, model complexity, and the online time required to compute a query answer.
Near real-time reliable stereo matching using programmable graphics hardware

A near-real-time stereo matching technique is presented in this paper, which is based on the reliability-based dynamic programming algorithm we proposed earlier. The new algorithm can generate semi-dense disparity maps using only two dynamic programming passes, while our previous approach requires 20-30 passes. We also implement the algorithm on programmable graphics hardware, which further improves the processing speed. The experiments on the four Middlebury stereo datasets show that the new algorithm can produce dense (>85% of the pixels) and reliable (error rate <0.3%) matches in near real-time (0.05-0.1 sec). If needed, it can also be used to generate dense disparity maps. Based on the evaluation conducted by the Middlebury Stereo Vision Research Website, the new algorithm is ranked between the variable window and the graph cuts approaches and currently is the most accurate dynamic programming based technique. When more than one reference images are available, the accuracy can be further improved with little extra computation time.