nils-herrmann/clean_scirepeval huggingface dataset:
Approximately optimal continuous-time motion planning and control via Probabilistic Inference

The problem of optimal motion planing and control is fundamental in robotics. However, this problem is intractable for continuous-time stochastic systems in general and the solution is difficult to approximate if non-instantaneous nonlinear performance indices are present. In this work, we provide an efficient algorithm, PIPC (Probabilistic Inference for Planning and Control), that yields approximately optimal policies with arbitrary higher-order nonlinear performance indices. Using probabilistic inference and a Gaussian process representation of trajectories, PIPC exploits the underlying sparsity of the problem such that its complexity scales linearly in the number of nonlinear factors. We demonstrate the capabilities of our algorithm in a receding horizon setting with multiple systems in simulation.
Deriving the DEA frontier for two-stage processes

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently DEA has been extended to examine the efficiency of two-stage processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model provides not only an overall efficiency score for the entire process, but as well yields an efficiency score for each of the individual stages. Due to the existence of intermediate measures, the usual procedure of adjusting the inputs or outputs by the efficiency scores, as in the standard DEA approach, does not necessarily yield a frontier projection. The current paper develops an approach for determining the frontier points for inefficient DMUs within the framework of two-stage DEA.
Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping

Three-dimensional perception is a key technology for many robotics applications, including obstacle detection, mapping, and localization. There exist a number of sensors and techniques for acquiring 3-D data, many of which have particular utility for various robotic tasks. We introduce a new design for a 3-D sensor system, constructed from a 2-D range scanner coupled with a passive linkage mechanism, such as a spring. By mounting the other end of the passive linkage mechanism to a moving body, disturbances resulting from accelerations and vibrations of the body propel the 2-D scanner in an irregular fashion, thereby extending the device's field of view outside of its standard scanning plane. The proposed 3-D sensor system is advantageous due to its mechanical simplicity, mobility, low weight, and relatively low cost. We analyze a particular implementation of the proposed device, which we call Zebedee, consisting of a 2-D time-of-flight laser range scanner rigidly coupled to an inertial measurement unit and mounted on a spring. The unique configuration of the sensor system motivates unconventional and specialized algorithms to be developed for data processing. As an example application, we describe a novel 3-D simultaneous localization and mapping solution in which Zebedee is mounted on a moving platform. Using a motion capture system, we have verified the positional accuracy of the sensor trajectory. The results demonstrate that the six-degree-of-freedom trajectory of a passive spring-mounted range sensor can be accurately estimated from laser range data and industrial-grade inertial measurements in real time and that a quality 3-D point cloud map can be generated concurrently using the same data.
Open-source, affordable, modular, light-weight, underactuated robot hands

In this paper we present a series of design directions for the development of affordable, modular, light-weight, intrinsically-compliant, underactuated robot hands, that can be easily reproduced using off-the-shelf materials. The proposed robot hands, efficiently grasp a series of everyday life objects and are considered to be general purpose, as they can be used for various applications. The efficiency of the proposed robot hands has been experimentally validated through a series of experimental paradigms, involving: grasping of multiple everyday life objects with different geometries, myoelectric (EMG) control of the robot hands in grasping tasks, preliminary results on a grasping capable quadrotor and autonomous grasp planning under object position and shape uncertainties.
Distant Domain Transfer Learning

In this paper, we study a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL). Different from existing transfer learning problems which assume that there is a close relation between the source domain and the target domain, in the DDTL problem, the target domain can be totally different from the source domain. For example, the source domain classifies face images but the target domain distinguishes plane images. Inspired by the cognitive process of human where two seemingly unrelated concepts can be connected by learning intermediate concepts gradually, we propose a Selective Learning Algorithm (SLA) to solve the DDTL problem with supervised autoencoder or supervised convolutional autoencoder as a base model for handling different types of inputs. Intuitively, the SLA algorithm selects usefully unlabeled data gradually from intermediate domains as a bridge to break the large distribution gap for transferring knowledge between two distant domains. Empirical studies on image classification problems demonstrate the effectiveness of the proposed algorithm, and on some tasks the improvement in terms of the classification accuracy is up to 17% over “non-transfer” methods.
REWIND: Recovery Write-Ahead System for In-Memory Non-Volatile Data-Structures

Recent non-volatile memory (NVM) technologies, such as PCM, STT-MRAM and ReRAM, can act as both main memory and storage. This has led to research into NVM programming models, where persistent data structures remain in memory and are accessed directly through CPU loads and stores. Existing mechanisms for transactional updates are not appropriate in such a setting as they are optimized for block-based storage. We present REWIND, a usermode library approach to managing transactional updates directly from user code written in an imperative generalpurpose language. REWIND relies on a custom persistent in-memory data structure for the log that supports recoverable operations on itself. The scheme also employs a combination of non-temporal updates, persistent memory fences, and lightweight logging. Experimental results on synthetic transactional workloads and TPC-C show the overhead of REWIND compared to its non-recoverable equivalent to be within a factor of only 1.5 and 1.39 respectively. Moreover, REWIND outperforms state-of-the-art approaches for data structure recoverability as well as general purpose and NVM-aware DBMS-based recovery schemes by up to two orders of magnitude.
Device-to-device communications in cellular networks

Device-to-device communications enable two proximity users to transmit signal directly without going through the base station. It can increase network spectral efficiency and energy efficiency, reduce transmission delay, offload traffic for the BS, and alleviate congestion in the cellular core networks. However, many technical challenges need to be addressed for D2D communications to harvest the potential benefits, including device discovery and D2D session setup, D2D resource allocation to guarantee QoS, D2D MIMO transmission, as well as D2D-aided BS deployment in heterogeneous networks. In this article, the basic concepts of D2D communications are first introduced, and then existing fundamental works on D2D communications are discussed. In addition, some potential research topics and challenges are also identified.
Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results

DFRWS is dedicated to the sharing of knowledge and ideas about digital forensics research. Ever since it organized the first open workshop devoted to digital forensics in 2001, DFRWS continues to bring academics and practitioners together in an informal environment. As a non-profit, volunteer organization, DFRWS sponsors technical working groups, annual conferences and challenges to help drive the direction of research and development.
1 The Corporate Social Performance-- Financial Performance Link

Strategic managers are consistently faced with the decision of how to allocate scarce corporate resources in an environment that is placing more and more pressures on them. Recent scholarship in strategic management suggests that many of these pressures come directly from sources associated with social issues in management, rather than traditional arenas of strategic management. Using a greatly-improved source of data on corporate social performance, this paper reports the results of a rigorous study of the empirical linkages between financial and social performance. CSP is found to be positively associated with prior financial performance, supporting the theory that slack resource availability and CSP are positively related. CSP is also found to be positively associated with future financial performance, supporting the theory that good management and CSP are positively related. . doi: 10.1002/(SICI)1097-0266(199704)18:4<303::AID-SMJ869>3.0.CO;2-G
A Novel Filter-Bank Multicarrier Scheme to Mitigate the Intrinsic Interference: Application to MIMO Systems

Filter-bank multicarrier (FBMC) transmission system was proposed as an alternative approach to orthogonal frequency division multiplexing (OFDM) system since it has a higher spectral efficiency. One of the characteristics of FBMC is that the demodulated transmitted symbols are accompanied by interference terms caused by the neighboring transmitted data in time-frequency domain. The presence of this interference is an issue for some multiple-input multiple-output (MIMO) schemes and until today their combination with FBMC remains an open problem. We can cite, among these techniques, the Alamouti scheme and the maximum likelihood detection (MLD) with spatial multiplexing (SM). In this paper, we shall propose a new FBMC scheme and transmission strategy in order to avoid this interference term. This proposed scheme (called FFT-FBMC) transforms the FBMC system into an equivalent system formulated as OFDM regardless of some residual interference. Thus, any OFDM transmission technique can be performed straightforwardly to the proposed FBMC scheme with a corresponding complexity growth compared to the classical FBMC. First, we will develop the FFT-FBMC in the case of single-input single-output (SISO) configuration. Then, we extend its application to SM-MIMO configuration with MLD and Alamouti coding scheme. Simulation results show that FFT-FBMC can almost reach the OFDM performance, but it remains slightly outperformed by OFDM.
Subpixel Photometric Stereo

Conventional photometric stereo recovers one normal direction per pixel of the input image. This fundamentally limits the scale of recovered geometry to the resolution of the input image, and cannot model surfaces with subpixel geometric structures. In this paper, we propose a method to recover subpixel surface geometry by studying the relationship between the subpixel geometry and the reflectance properties of a surface. We first describe a generalized physically-based reflectance model that relates the distribution of surface normals inside each pixel area to its reflectance function. The distribution of surface normals can be computed from the reflectance functions recorded in photometric stereo images. A convexity measure of subpixel geometry structure is also recovered at each pixel, through an analysis of the shadowing attenuation. Then, we use the recovered distribution of surface normals and the surface convexity to infer subpixel geometric structures on a surface of homogeneous material by spatially arranging the normals among pixels at a higher resolution than that of the input image. Finally, we optimize the arrangement of normals using a combination of belief propagation and MCMC based on a minimum description length criterion on 3D textons over the surface. The experiments demonstrate the validity of our approach and show superior geometric resolution for the recovered surfaces.
Small-Signal Modeling of Active-Clamped ZVS Current-Fed Full-Bridge Isolated DC/DC Converter and Control System Implementation Using PSoC

The small-signal analysis of an active-clamped zero-voltage-switching current-fed isolated dc/dc converter is presented in this paper. State-space averaging is used to derive the small-signal model. A closed-loop controller is designed to regulate the converter output over a wide range of power (1:10) and source voltage (1:2) with fixed frequency duty cycle modulation. Bode plots are presented using MATLAB and PSIM 9.0 to show the frequency response of the converter with and without a controller. The designed controller is implemented on the mixed-signal (analog and digital) processor Cypress Programmable System-on-Chip (PSoC) 5. All components are integrated in Cypress PSoC 5 which is compact, economical, and easily configurable. A 300-W converter prototype is developed and tested with the implemented controller to observe the performance of the system. Experimental results for step change in load for different input voltage conditions show the high performance of the controller, satisfactory transient performance of the converter, and voltage regulation with continued safe operation.
The Impact on Individualizing Student Models on Necessary Practice Opportunities

When modeling student learning, tutors that use the Knowledge Tracing framework often assume that all students have the same set of model parameters. We find that when fitting parameters to individual students, there is significant variation among the individual’s parameters. We examine if this variation is important in terms of instructional decisions by computing the difference in the expected number of practice opportunities required if mastery is assessed using an individual student’s own estimated model parameters, compared to the population model. In the dataset considered, we find that a significant portion of students are expected to perform twice as many practice opportunities if the student is modeled using a population-based model, compared to the number needed if the student’s own model parameters were used. We also find an additional significant portion of students will be likely to receive less practice opportunities than needed, implying that such students will be advanced too early. Though further work on additional datasets is needed to explore this issue in more depth, our results suggest that considering individual variation in student parameters may have important implications for the instructional decisions made in intelligent tutoring systems that use a Knowledge Tracing model.
A formal framework for scenario development in support of environmental decision-making

Scenarios are possible future states of the world that represent alternative plausible conditions under different assumptions. Often, scenarios are developed in a context relevant to stakeholders involved in their applications since the evaluation of scenario outcomes and implications can enhance decision-making activities. This paper reviews the state-of-the-art of scenario development and proposes a formal approach to scenario development in environmental decision-making. The discussion of current issues in scenario studies includes advantages and obstacles in utilizing a formal scenario development framework, and the different forms of uncertainty inherent in scenario development, as well as how they should be treated. An appendix for common scenario terminology has been attached for clarity. Major recommendations for future research in this area include proper consideration of uncertainty in scenario studies in particular in relation to stakeholder relevant information, construction of scenarios that are more diverse in nature, and sharing of information and resources among the scenario development research community.
Incremental Exact Min-Cut in Poly-logarithmic Amortized Update Time

We present a deterministic incremental algorithm for exactly maintaining the size of a minimum cut with Õ(1) amortized time per edge insertion and O(1) query time. This result partially answers an open question posed by Thorup [Combinatorica 2007]. It also stays in sharp contrast to a polynomial conditional lower-bound for the fully-dynamic weighted minimum cut problem. Our algorithm is obtained by combining a recent sparsification technique of Kawarabayashi and Thorup  and an exact incremental algorithm of Henzinger []. We also study space-efficient incremental algorithms for the minimum cut problem. Concretely, we show that there exists an O(n logn/ε2) space Monte-Carlo algorithm that can process a stream of edge insertions starting from an empty graph, and with high probability, the algorithm maintains a (1 + ε)-approximation to the minimum cut. The algorithm has Õ(1) amortized update-time and constant query-time.
Functional connectivity of human striatum: a resting state FMRI study.

Classically regarded as motor structures, the basal ganglia subserve a wide range of functions, including motor, cognitive, motivational, and emotional processes. Consistent with this broad-reaching involvement in brain function, basal ganglia dysfunction has been implicated in numerous neurological and psychiatric disorders. Despite recent advances in human neuroimaging, models of basal ganglia circuitry continue to rely primarily upon inference from animal studies. Here, we provide a comprehensive functional connectivity analysis of basal ganglia circuitry in humans through a functional magnetic resonance imaging examination during rest. Voxelwise regression analyses substantiated the hypothesized motor, cognitive, and affective divisions among striatal subregions, and provided in vivo evidence of a functional organization consistent with parallel and integrative loop models described in animals. Our findings also revealed subtler distinctions within striatal subregions not previously appreciated by task-based imaging approaches. For instance, the inferior ventral striatum is functionally connected with medial portions of orbitofrontal cortex, whereas a more superior ventral striatal seed is associated with medial and lateral portions. The ability to map multiple distinct striatal circuits in a single study in humans, as opposed to relying on meta-analyses of multiple studies, is a principal strength of resting state functional magnetic resonance imaging. This approach holds promise for studying basal ganglia dysfunction in clinical disorders.
Graph mining: A survey of graph mining techniques

Data mining is comprised of many data analysis techniques. Its basic objective is to discover the hidden and useful data pattern from very large set of data. Graph mining, which has gained much attention in the last few decades, is one of the novel approaches for mining the dataset represented by graph structure. Graph mining finds its applications in various problem domains, including: bioinformatics, chemical reactions, Program flow structures, computer networks, social networks etc. Different data mining approaches are used for mining the graph-based data and performing useful analysis on these mined data. In literature various graph mining approaches have been proposed. Each of these approaches is based on either classification; clustering or decision trees data mining techniques. In this study, we present a comprehensive review of various graph mining techniques. These different graph mining techniques have been critically evaluated in this study. This evaluation is based on different parameters. In our future work, we will provide our own classification based graph mining technique which will efficiently and accurately perform mining on the graph structured data.
Learning to Segment Object Candidates

Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier. Such approaches have been shown they can be fast, while achieving the state of the art in detection performance. In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model is trained jointly with two objectives: given an image patch, the first part of the system outputs a class-agnostic segmentation mask, while the second part of the system outputs the likelihood of the patch being centered on a full object. At test time, the model is efficiently applied on the whole test image and generates a set of segmentation masks, each of them being assigned with a corresponding object likelihood score. We show that our model yields significant improvements over state-of-theart object proposal algorithms. In particular, compared to previous approaches, our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training. Unlike all previous approaches for generating object masks, we do not rely on edges, superpixels, or any other form of low-level segmentation.
From perception to pleasure: music and its neural substrates.

Music has existed in human societies since prehistory, perhaps because it allows expression and regulation of emotion and evokes pleasure. In this review, we present findings from cognitive neuroscience that bear on the question of how we get from perception of sound patterns to pleasurable responses. First, we identify some of the auditory cortical circuits that are responsible for encoding and storing tonal patterns and discuss evidence that cortical loops between auditory and frontal cortices are important for maintaining musical information in working memory and for the recognition of structural regularities in musical patterns, which then lead to expectancies. Second, we review evidence concerning the mesolimbic striatal system and its involvement in reward, motivation, and pleasure in other domains. Recent data indicate that this dopaminergic system mediates pleasure associated with music; specifically, reward value for music can be coded by activity levels in the nucleus accumbens, whose functional connectivity with auditory and frontal areas increases as a function of increasing musical reward. We propose that pleasure in music arises from interactions between cortical loops that enable predictions and expectancies to emerge from sound patterns and subcortical systems responsible for reward and valuation.
Attention Modeling for Targeted Sentiment

Neural network models have been used for target-dependent sentiment analysis. Previous work focus on learning a target specific representation for a given input sentence which is used for classification. However, they do not explicitly model the contribution of each word in a sentence with respect to targeted sentiment polarities. We investigate an attention model to this end. In particular, a vanilla LSTM model is used to induce an attention value of the whole sentence. The model is further extended to differentiate left and right contexts given a certain target following previous work. Results show that by using attention to model the contribution of each word with respect to the target, our model gives significantly improved results over two standard benchmarks. We report the best accuracy for this task.