nils-herrmann/clean_scirepeval huggingface dataset:
Human ES cell-derived neural rosettes reveal a functionally distinct early neural stem cell stage.

Neural stem cells (NSCs) yield both neuronal and glial progeny, but their differentiation potential toward multiple region-specific neuron types remains remarkably poor. In contrast, embryonic stem cell (ESC) progeny readily yield region-specific neuronal fates in response to appropriate developmental signals. Here we demonstrate prospective and clonal isolation of neural rosette cells (termed R-NSCs), a novel NSC type with broad differentiation potential toward CNS and PNS fates and capable of in vivo engraftment. R-NSCs can be derived from human and mouse ESCs or from neural plate stage embryos. While R-NSCs express markers classically associated with NSC fate, we identified a set of genes that specifically mark the R-NSC state. Maintenance of R-NSCs is promoted by activation of SHH and Notch pathways. In the absence of these signals, R-NSCs rapidly lose rosette organization and progress to a more restricted NSC stage. We propose that R-NSCs represent the first characterized NSC stage capable of responding to patterning cues that direct differentiation toward region-specific neuronal fates. In addition, the R-NSC-specific genetic markers presented here offer new tools for harnessing the differentiation potential of human ESCs.
Multilevel PWM Methods at Low Modulation Indices

When utilized at low amplitude modulation indices, existing multilevel carrier-based PWM strategies have no special provisions for this operating region, and several levels of the inverter go unused. This paper proposes some novel multilevel PWM strategies to take advantage of the multiple levels in both a diodeclamped inverter and a cascaded H-bridges inverter by utilizing all of the levels in the inverter even at low modulation indices. Simulation results show what effects the different strategies have on the active device utilization. A prototype 6-level diode-clamped inverter and an 11-level cascaded H-bridges inverter have been built and controlled with the novel PWM strategies proposed in this paper.
Performance analysis of visualmarkers for indoor navigation systems

The massive diffusion of smartphones, the growing interest in wearable devices and the Internet of Things, and the exponential rise of location based services (LBSs) have made the problem of localization and navigation inside buildings one of the most important technological challenges of recent years. Indoor positioning systems have a huge market in the retail sector and contextual advertising; in addition, they can be fundamental to increasing the quality of life for citizens if deployed inside public buildings such as hospitals, airports, and museums. Sometimes, in emergency situations, they can make the difference between life and death. Various approaches have been proposed in the literature. Recently, thanks to the high performance of smartphones’ cameras, marker-less and marker-based computer vision approaches have been investigated. In a previous paper, we proposed a technique for indoor localization and navigation using both Bluetooth low energy (BLE) and a 2D visual marker system deployed into the floor. In this paper, we presented a qualitative performance evaluation of three 2D visual markers, Vuforia, ArUco marker, and AprilTag, which are suitable for real-time applications. Our analysis focused on specific case study of visual markers placed onto the tiles, to improve the efficiency of our indoor localization and navigation approach by choosing the best visual marker system.
Optimizing F-measure: A Tale of Two Approaches

F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then predicts labels with maximum expected F-measure. In this paper, we investigate the theoretical justifications and connections for these two approaches, and we study the conditions under which one approach is preferable to the other using synthetic and real datasets. Given accurate models, our results suggest that the two approaches are asymptotically equivalent given large training and test sets. Nevertheless, empirically, the EUM approach appears to be more robust against model misspecification, and given a good model, the decision-theoretic approach appears to be better for handling rare classes and a common domain adaptation scenario.
Multi-View Learning of Word Embeddings via CCA

Recently, there has been substantial interest in using large amounts of unlabeled data to learn word representations which can then be used as features in supervised classifiers for NLP tasks. However, most current approaches are slow to train, do not model the context of the word, and lack theoretical grounding. In this paper, we present a new learning method, Low Rank Multi-View Learning (LR-MVL) which uses a fast spectral method to estimate low dimensional context-specific word representations from unlabeled data. These representation features can then be used with any supervised learner. LR-MVL is extremely fast, gives guaranteed convergence to a global optimum, is theoretically elegant, and achieves state-ofthe-art performance on named entity recognition (NER) and chunking problems.
TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data

Time-oriented data play an essential role in many Visual Analytics scenarios such as extracting medical insights from collections of electronic health records or identifying emerging problems and vulnerabilities in network traffic. However, many software libraries for Visual Analytics treat time as a flat numerical data type and insufficiently tackle the complexity of the time domain such as calendar granularities and intervals. Therefore, developers of advanced Visual Analytics designs need to implement temporal foundations in their application code over and over again. We present TimeBench, a software library that provides foundational data structures and algorithms for time-oriented data in Visual Analytics. Its expressiveness and developer accessibility have been evaluated through application examples demonstrating a variety of challenges with time-oriented data and long-term developer studies conducted in the scope of research and student projects.
Using Simulation and Evolutionary Algorithms to Evaluate the Design of Mix Strategies of Decoy and Jammers in Anti-Torpedo Tactics

When a submarine uses an anti-torpedo tactic, it is a matter of life or death. In terms of diesel submarine, the torpedo has the advantage of high speed, and acoustic homing to target. The disadvantages of submarine are the not-so-fast evasive speed, and the limited capability of torpedo counter-measure systems. There are two types of countermeasures: decoys and jammers. A successful anti-torpedo tactic should consist of the deployment of mixed decoys and jammers and the coordination with the submarine's maneuver. This paper would like to discuss the anti-torpedo tactics from the classical viewpoint. A simulation scenario is implemented in order to study the interaction among the submarine, torpedo, decoy and jammers. After applying the evolutionary algorithm, it is interesting to discover some points about anti-torpedo tactics using a mix of decoys and jammers that would make a significant contribution to the survivability of submarine in the torpedoes engagement scenario.
Parasitic Inductance and Capacitance-Assisted Active Gate Driving Technique to Minimize Switching Loss of SiC MOSFET

High di/dt and dv/dt of SiC MOSFET cause a considerable amount of overshoot in device voltage and current during switching transients in the presence of inverter layout parasitic inductance and load parasitic capacitance. The excessive overshoots in device voltage and current cause failure of the device. Moreover, these uncontrolled overshoots increase the switching loss in the inverter. It is difficult to reduce parasitic inductance beyond a certain point. This paper proposes an active gate driving technique, which allows inverter to operate with moderate amount of layout parasitic inductance and load parasitic capacitance. The proposed technique dramatically reduces switching loss of the SiC MOSFET with the help of existing parasitic elements. The proposed switching loss reduction technique is termed as quasi zero switching. The developed active gate driver has been tested in a double pulse test setup and a 10 kW two-level voltage source inverter driving an induction motor.
Feature integration analysis of bag-of-features model for image retrieval

One of the biggest challenges in content based image retrieval is to solve the problem of “semantic gaps” between low-level features and high-level semantic concepts. In this paper, we aim to investigate various combinations of mid-level features to build an effective image retrieval system based on the bag-offeatures (BoF) model. Specifically, we study two ways of integrating the SIFT and LBP descriptors, HOG and LBP descriptors, respectively. Based on the qualitative and quantitative evaluations on two benchmark datasets, we show that the integrations of these features yield complementary and substantial improvement on image retrieval even with noisy background and ambiguous objects. Two integration models are proposed: the patch-based integration and image-based integration. By using a weighted K-means clustering algorithm, the image-based SIFT-LBP integration achieves the best performance on the given benchmark problems comparing to the existing algorithms. & 2013 Elsevier B.V. All rights reserved.
The impact of credit scoring on consumer lending

We study the adoption of automated credit scoring at a large auto finance company and the changes it enabled in lending practices. Credit scoring appears to have increased profits by roughly a thousand dollars per loan. We identify two distinct benefits of risk classification: the ability to screen high-risk borrowers and the ability to target more generous loans to lower-risk borrowers. We show that these had effects of similar magnitude. We also document that credit scoring compressed profitability across dealerships, and provide evidence consistent with the view that credit scoring may have substituted for varying qualities of local information.
English, Devnagari and Urdu Text Identification

In a multi-lingual multi-script country like India, a single text line of a document page may contain words of two or more scripts. For the Optical Character Recognition of such a document page it is necessary to identify different scripts from the document. In this paper, an automatic technique for word -wise identification of English, Devnagari and Urdu scripts from a single document is proposed. Here, at first, the document is segmented into lines and then the lines are segmented into possible words. Using characteristics of different scripts, the identification scheme is developed.
Toward a Unified Ontology of Cloud Computing

Progress of research efforts in a novel technology is contingent on having a rigorous organization of its knowledge domain and a comprehensive understanding of all the relevant components of this technology and their relationships. Cloud computing is one contemporary technology in which the research community has recently embarked. Manifesting itself as the descendant of several other computing research areas such as service-oriented architecture, distributed and grid computing, and virtualization, cloud computing inherits their advancements and limitations. Towards the end-goal of a thorough comprehension of the field of cloud computing, and a more rapid adoption from the scientific community, we propose in this paper an ontology of this area which demonstrates a dissection of the cloud into five main layers, and illustrates their interrelations as well as their inter-dependency on preceding technologies. The contribution of this paper lies in being one of the first attempts to establish a detailed ontology of the cloud. Better comprehension of the technology would enable the community to design more efficient portals and gateways for the cloud, and facilitate the adoption of this novel computing approach in scientific environments. In turn, this will assist the scientific community to expedite its contributions and insights into this evolving computing field.
A new vector evaluated PBIL algorithm for reinsurance analytics

The purpose of this paper is to evaluate the performance of a new multiobjective algorithm called Vector Evaluated Population Based Incremental Learning (VEPBIL). The new algorithm was applied in solving a real world application named Reinsurance Contract Optimization (RCO), which is a multiobjective problem consisting of maximizing two conflicting functions: expected return and risk. The VEPBIL was tested on two instances of the problem composed by 7 and 15 layers of real anonymized data. In order to evaluate the algorithm, metrics such as hyper volume, number of solutions and coverage were used. A comparisons against Vector Evaluated Differential evolution (VEDE) is also carried out. The comparison has shown that VEPBIL can dominate about 70% and 50% of solutions from VEDE using 7 and 15 layers respectively, whereas VEDE dominates about 10% and 30% of solutions in the way around.
S3: A Symbolic String Solver for Vulnerability Detection in Web Applications

Motivated by the vulnerability analysis of web programs which work on string inputs, we present S3, a new symbolic string solver. Our solver employs a new algorithm for a constraint language that is expressive enough for widespread applicability. Specifically, our language covers all the main string operations, such as those in JavaScript. The algorithm first makes use of a symbolic representation so that membership in a set defined by a regular expression can be encoded as string equations. Secondly, there is a constraint-based generation of instances from these symbolic expressions so that the total number of instances can be limited. We evaluate S3 on a well-known set of practical benchmarks, demonstrating both its robustness (more definitive answers) and its efficiency (about 20 times faster) against the state-of-the-art.
How do training and competition workloads relate to injury? The workload-injury aetiology model.

Injury aetiology models that have evolved over the previous two decades highlight a number of factors which contribute to the causal mechanisms for athletic injuries. These models highlight the pathway to injury, including (1) internal risk factors (eg, age, neuromuscular control) which predispose athletes to injury, (2) exposure to external risk factors (eg, playing surface, equipment), and finally (3) an inciting event, wherein biomechanical breakdown and injury occurs. The most recent aetiological model proposed in 2007 was the first to detail the dynamic nature of injury risk, whereby participation may or may not result in injury, and participation itself alters injury risk through adaptation. However, although training and competition workloads are strongly associated with injury, existing aetiology models neither include them nor provide an explanation for how workloads alter injury risk. Therefore, we propose an updated injury aetiology model which includes the effects of workloads. Within this model, internal risk factors are differentiated into modifiable and non-modifiable factors, and workloads contribute to injury in three ways: (1) exposure to external risk factors and potential inciting events, (2) fatigue, or negative physiological effects, and (3) fitness, or positive physiological adaptations. Exposure is determined solely by total load, while positive and negative adaptations are controlled both by total workloads, as well as changes in load (eg, the acute:chronic workload ratio). Finally, we describe how this model explains the load-injury relationships for total workloads, acute:chronic workload ratios and the training load-injury paradox.
Learning Markov Logic Networks Using Structural Motifs

Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extreme computational costs, and thus are unable to represent complex regularities in data. To address this problem, we present LSM, the first MLN structure learner capable of efficiently and accurately learning long clauses. LSM is based on the observation that relational data typically contains patterns that are variations of the same structural motifs. By constraining the search for clauses to occur within motifs, LSM can greatly speed up the search and thereby reduce the cost of finding long clauses. LSM uses random walks to identify densely connected objects in data, and groups them and their associated relations into a motif. Our experiments on three real-world datasets show that our approach is 2-5 orders of magnitude faster than the state-of-the-art ones, while achieving the same or better predictive performance.
Analysis and design of block cipher constructions

This thesis is dedicated to symmetric cryptographic algorithms. The major focus of the work is on block ciphers themselves as well as on hash functions and message authentication codes based on block ciphers. Three main approaches to the cryptanalysis of symmetric cryptographic algorithms are pursued. First, several block cipher constructions are analyzed mathematically using statistical cryptanalysis. Second, practical attacks on real-world symmetric cryptosystems are considered. Finally, novel cryptanalytic techniques using side-channel leakage are studied with applications to block ciphers and message authentication codes. Differential and linear cryptanalyses are well-known statistical attacks on block ciphers. This thesis studies the security of unbalanced Feistel networks with contracting MDS diffusion with respect to differential and linear cryptanalysis. Upper bounds on the differential trail probabilities and linear probabilities of linear trails in such constructions are proven. It is shown that such unbalanced Feistel networks can be highly efficient and are comparable to many known balanced Feistel network constructions with respect to differential and linear cryptanalysis. Ultra-lightweight substitution-permutation networks with diffusion layers based on the co-design of S-boxes and bit permutations are proposed. This results in lightweight block ciphers and block cipher based compression functions for hash functions designed and analyzed. These constructions have very small footprint and can be efficiently implemented on the majority of RFID tags This work also studies practical attacks on real-world symmetric cryptographic systems. Attacks are proposed on the KeeLoq block cipher and authentication systems widely used for automotive access control and component identification. Cryptanalysis of the A5/2 stream cipher used for protecting GSM connections worldwide is performed. Linear slide attacks on KeeLoq are proposed resulting in the fastest known attack on the KeeLoq block cipher working for all keys. Severe weaknesses of the KeeLoq key management are identified. The KeeLoq real-world authentication protocols for access control and component identification are also analyzed. A special-purpose hardware architecture for attacking A5/2 is developed that allows for real-time key recovery within one second for different GSM channels. This engine is based on an optimized hardware algorithm for fast Gaussian elimination over binary finite fields.
StorkCloud: data transfer scheduling and optimization as a service

Wide-area transfer of large data sets is still a big challenge despite the deployment of high-bandwidth networks with speeds reaching 100 Gbps. Most users fail to obtain even a fraction of theoretical speeds promised by these networks. Effective usage of the available network capacity has become increasingly important for wide-area data movement. We have developed a "data transfer scheduling and optimization system as a Cloud-hosted service", StorkCloud, which will mitigate the large-scale end-to-end data movement bottleneck by efficiently utilizing underlying networks and effectively scheduling and optimizing data transfers. In this paper, we present the initial design and prototype implementation of StorkCloud, and show its effectiveness in large dataset transfers across geographically distant storage sites, data centers, and collaborating institutions.
A lightweight infrastructure for graph analytics

Several domain-specific languages (DSLs) for parallel graph analytics have been proposed recently. In this paper, we argue that existing DSLs can be implemented on top of a general-purpose infrastructure that (i) supports very fine-grain tasks, (ii) implements autonomous, speculative execution of these tasks, and (iii) allows application-specific control of task scheduling policies. To support this claim, we describe such an implementation called the Galois system.
 We demonstrate the capabilities of this infrastructure in three ways. First, we implement more sophisticated algorithms for some of the graph analytics problems tackled by previous DSLs and show that end-to-end performance can be improved by orders of magnitude even on power-law graphs, thanks to the better algorithms facilitated by a more general programming model. Second, we show that, even when an algorithm can be expressed in existing DSLs, the implementation of that algorithm in the more general system can be orders of magnitude faster when the input graphs are road networks and similar graphs with high diameter, thanks to more sophisticated scheduling. Third, we implement the APIs of three existing graph DSLs on top of the common infrastructure in a few hundred lines of code and show that even for power-law graphs, the performance of the resulting implementations often exceeds that of the original DSL systems, thanks to the lightweight infrastructure.
Integration of a Wireless I/O Interface for PROFIBUS and PROFINET for Factory Automation

Cost related to the wiring of sensors and actuators in the manufacturing industry is rapidly increasing with the number of field devices. Complex engineering as well as cable wear and tear are important factors. For over a decade, fieldbuses have been the dominating approach to simplify installation, reduce engineering efforts, and increase reliability at the field level. The next step in this process is to introduce wireless communication in certain problematic parts of the field network. In order to do this, a reliable real-time communication system, designed to cope with the harsh environments where factory automation systems are employed, is required. The wireless interface for sensors and actuators (WISA), with its two subconcepts WISA-power and WISA-com, is such a system. This paper discusses how the WISA concept can be efficiently integrated into wired field networks for factory automation-both state-of-the-art [process fieldbus decentral peripherals (PROFIBUS DP)] and new (PROFINET IO) networks. We propose amendments to WISA, which will improve the 802.11b/g coexistence and harmonize the integration of WISA for sensor/actuator communication in, e.g., PROFINET IO systems. The suggested improvements relate to both the radio protocol in order to improve coexistence with other wireless technologies as well as concepts for the integration of WISA into the field network of PROFINET/PROFIBUS.