nils-herrmann/clean_scirepeval huggingface dataset:
On dexterity and dexterous manipulation

This paper presents a high-level discussion of dexterity in robotic systems, focusing particularly on manipulation and hands. While it is generally accepted in the robotics community that dexterity is desirable and that end effectors with in-hand manipulation capabilities should be developed, there has been little, if any, formal description of why this is needed, particularly given the increased design and control complexity required. This discussion will overview various definitions of dexterity used in the literature and highlight issues related to specific metrics and quantitative analysis. It will also present arguments regarding why hand dexterity is desirable or necessary, particularly in contrast to the capabilities of a kinematically redundant arm with a simple grasper. Finally, we overview and illustrate the various classes of in-hand manipulation, and review a number of dexterous manipulators that have been previously developed. We believe this work will help to revitalize the dialogue on dexterity in the manipulation community and lead to further formalization of the concepts discussed here.
Sagittal plane biomechanics cannot injure the ACL during sidestep cutting.

Knee joint sagittal plane forces are a proposed mechanism of anterior cruciate ligament injury during sport movements such as sidestep cutting. Ligament force magnitudes for these movements however, remain unknown. The need to examine injury-causing events suggests elucidation via model-based investigations is possible. Using this approach, the current study determined whether sagittal plane knee loading during sidestep cutting could in isolation injure the anterior cruciate ligament.



Experiments were performed on subject-specific forward dynamic musculoskeletal models, generated from data obtained from 10 male and 10 female athletes. Models were optimized to simulate subject-specific cutting movements. Random perturbations (n=5000) were applied to initial contact conditions and quadriceps/hamstrings activation levels to simulate their effect on peak 3D knee loads. Injury via the sagittal plane mechanism was based on the criterion of an anterior drawer force greater than 2000 N.



Realistic neuromuscular perturbations produced significant increases in external knee anterior force and valgus and internal rotation moments. Peak anterior drawer force never exceeded 2000 N in any model, and thus failed to cause anterior cruciate ligament injuries. Valgus loads reached values that were high enough to rupture the ligament, occurring more frequently in females than in males.



Sagittal plane knee joint forces cannot rupture the anterior cruciate ligament during sidestep cutting. The interaction between muscle and joint mechanics and external ground reaction forces in this plane, places a ceiling on ligament loads. Valgus loading is a more likely injury mechanism, especially in females. Modifying sagittal plane biomechanics will thus unlikely contribute to the prevention of anterior cruciate ligament injuries.
Robust Parameter Estimation in Computer Vision

Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the data, occasional large-scale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techniques, some borrowed from the statistics literature and others described in the computer vision literature, have been used in solving these parameter estimation problems. Ideally, these techniques should effectively ignore the outliers and measurements from other populations, treating them as outliers, when estimating the parameters of a single population. Two frequently used techniques are least-median of squares (LMS) [ (1984), pp. 871–880] and M-estimators [Robust Statistics: The Approach Based on Influence Functions, F. R. Hampel et al.,  1986; Robust Statistics, P. J. Huber, John Wiley, 1981]. LMS handles large fractions of outliers, up to the theoretical limit of 50% for estimators invariant to affine changes to the data, but has low statistical efficiency. M-estimators have higher statistical efficiency but tolerate much lower percentages of outliers unless properly initialized. While robust estimators have been used in a variety of computer vision applications, three are considered here. In analysis of range images—images containing depth or X, Y , Z measurements at each pixel instead of intensity measurements—robust estimators have been used successfully to estimate surface model parameters in small image regions. In stereo and motion analysis, they have been used to estimate parameters of what is called the “fundamental matrix,” which characterizes the relative imaging geometry of two cameras imaging the same scene. Recently, robust estimators have been applied to estimating a quadratic image-to-image transformation model necessary to create a composite, “mosaic image” from a series of images of the human retina. In each case, a straightforward application of standard robust estimators is insufficient, and carefully developed extensions are used to solve the problem.
Building Highly-Optimized, Low-Latency Pipelines for Genomic Data Analysis

Next-generation sequencing has transformed genomics into a new paradigm of data-intensive computing. The deluge of genomic data needs to undergo deep analysis to mine biological information. Deep analysis pipelines often take days to run, which entails a long cycle for algorithm and method development and hinders future application for clinic use. In this project, we aim to bring big data technology to the genomics domain and innovate in this new domain to revolutionize its data crunching power. Our work includes the development of a deep analysis pipeline, a parallel platform for pipeline execution, and a principled approach to optimizing the pipeline. We also present some initial evaluation results using existing long-running pipelines at the New York Genome Center, as well as a variety of real use cases that we plan to build in the course of this project.
"Go to the ant": Engineering principles from natural multi-agent systems

Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their coordination, reintroducing many of the problems of complex system design and implementation that motivated increasing software localization in the first place. Naturally occurring systems of simple agents (such as populations of insects or other animals) suggest that this retreat is not necessary. This paper summarizes several studies of such systems, and derives from them a set of general principles that artificial multiagent systems can use to support overall system behavior significantly more complex than the behavior of the individuals agents.
IJRIC MULTI-LEVEL INVERTER : A LITERATURE SURVEY ON TOPOLOGIES AND CONTROL STRATEGIES

Multilevel inverters have been attracting in favor of academia as well as industry in the recent decade for high-power and medium-voltage energy control. In addition, they can synthesize switched waveforms with lower levels of harmonic distortion than an equivalently rated two-level converter.The multilevel concept is used to decrease the harmonic distortion in the output waveform without decreasing the inverter power output. This paper presents the most important topologies like diode-clamped inverter (neutralpoint clamped),capacitor-clamped (flying capacitor), and cascaded multilevel with separate dc sources.This paper also presents the most relevant modulation methods developed for this family of converters: multilevel sinusoidal pulsewidth modulation, multilevel selective harmonic elimination, and space-vector modulation.Authors strongly believe that this survey article will be very much useful to the researchers for finding out the relevant references in the field of topologies and modulation strategies of multilevel inverter.
Driving an autonomous car with eye tracking

This paper describes eyeDriver, a hardware and software setup to drive an autonomous car with eye movement. The movement of the operator’s iris is tracked with an infrared sensitive camera built onto a HED4 interface by SMI. The position of the iris is then propagated by eyeDriver to control the steering wheel of “Spirit of Berlin”, a completely autonomous car developed by the Free University of Berlin which is capable of unmanned driving in urban areas.
Implicit Argument Prediction with Event Knowledge

Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be generated automatically at scale. This allows us to use a neural model, which draws on narrative coherence and entity salience for predictions. We show that our model has superior performance on both synthetic and natural data.
Graspit! A versatile simulator for robotic grasping

A robotic grasping simulator, called Graspit!, is presented as versatile tool for the grasping community. The focus of the grasp analysis has been on force-closure grasps, which are useful for pick-and-place type tasks. This work discusses the different types of world elements and the general robot definition, and presented the robot library. The paper also describes the user interface of Graspit! and present the collision detection and contact determination system. The grasp analysis and visualization method were also presented that allow a user to evaluate a grasp and compute optimal grasping forces. A brief overview of the dynamic simulation system was provided.
Language Understanding for Text-based Games using Deep Reinforcement Learning

In this paper, we consider the task of learning control policies for text-based games. In these games, all interactions in the virtual world are through text and the underlying state is not observed. The resulting language barrier makes such environments challenging for automatic game players. We employ a deep reinforcement learning framework to jointly learn state representations and action policies using game rewards as feedback. This framework enables us to map text descriptions into vector representations that capture the semantics of the game states. We evaluate our approach on two game worlds, comparing against baselines using bag-ofwords and bag-of-bigrams for state representations. Our algorithm outperforms the baselines on both worlds demonstrating the importance of learning expressive representations.
Exploration from Demonstration for Interactive Reinforcement Learning

Reinforcement Learning (RL) has been effectively used to solve complex problems given careful design of the problem and algorithm parameters. However standard RL approaches do not scale particularly well with the size of the problem and often require extensive engineering on the part of the designer to minimize the search space. To alleviate this problem, we present a model-free policy-based approach called Exploration from Demonstration (EfD) that uses human demonstrations to guide search space exploration. We use statistical measures of RL algorithms to provide feedback to the user about the agent’s uncertainty and use this to solicit targeted demonstrations useful from the agent’s perspective. The demonstrations are used to learn an exploration policy that actively guides the agent towards important aspects of the problem. We instantiate our approach in a gridworld and a popular arcade game and validate its performance under different experimental conditions. We show how EfD scales to large problems and provides convergence speed-ups over traditional exploration and interactive learning methods.
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks

Automatic segmentation of liver lesions is a fundamental requirement towards the creation of computer aided diagnosis (CAD) and decision support systems (CDS). Traditional segmentation approaches depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in many segmentation problems primarily because they leverage a large labelled dataset to hierarchically learn the features that best correspond to the shallow visual appearance as well as the deep semantics of the areas to be segmented. However, FCNs based on a 16 layer VGGNet architecture have limited capacity to add additional layers. Therefore, it is challenging to learn more discriminative features among different classes for FCNs. In this study, we overcome these limitations using deep residual networks (ResNet) to segment liver lesions. ResNet contain skip connections between convolutional layers, which solved the problem of the training degradation of training accuracy in very deep networks and thereby enables the use of additional layers for learning more discriminative features. In addition, we achieve more precise boundary definitions through a novel cascaded ResNet architecture with multi-scale fusion to gradually learn and infer the boundaries of both the liver and the liver lesions. Our proposed method achieved 4th place in the ISBI 2017 Liver Tumor Segmentation Challenge by the submission deadline.
Associations between parental report of the home food environment and adolescent intakes of fruits, vegetables and dairy foods.

This study examines parental report of household food availability, parent dietary intake and associations with adolescent intakes of fruits, vegetables and dairy foods.



Cross-sectional study. Adolescents completed the Project EAT survey and the Youth Adolescent Food Frequency Questionnaire at school. Parents of adolescents were interviewed by telephone about the home food environment, eating habits and weight-related behaviours. General linear modelling was used to compare dietary intakes of adolescents across different levels of household food availability and parental intakes.



The study sample included 902 adolescents and their parent or guardian.



Many parents were not consuming the minimum number of daily recommended fruit (44.5%), vegetable (69.9%) or dairy (46.9%) servings. While most parents reported that fruits and vegetables were available at home (90.3%) and vegetables were usually served at dinner (87.0%), fewer parents reported milk was served at meals (66.6%). Soft drinks were usually available at home (56.8%). Among girls, household availability was positively associated with fruit and vegetable intake (ttrend=2.70, P<0.01) and soft drink availability was inversely associated with dairy intake (ttrend=2.08, P=0.04). Among boys, serving milk at meals was positively associated with dairy intake (ttrend=3.65, P<0.01). Parental intakes were positively associated with dairy intake for boys (ttrend=2.04, P=0.04), and with dairy (ttrend=2.43, P=0.01), vegetable (ttrend=3.72, P<0.01) and fruit (ttrend=3.17, P<0.01) intakes for girls.



Interventions designed to help adolescents improve consumption of fruits, vegetables and dairy foods may be enhanced by including a parental component aimed at increasing household availability and parents' intake of healthful food choices.
Describing a Knowledge Base

We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms stateof-the-art methods. The reconstructed KB achieves 68.8% 72.6% F-
Information Security Economics - and Beyond

The economics of information security has recently become a thriving and fast-moving discipline. As distributed systems are assembled from machines belonging to principals with divergent interests, incentives are becoming as important to dependability as technical design. The new field provides valuable insights not just into ‘security’ topics such as privacy, bugs, spam, and phishing, but into more general areas such as system dependability (the design of peer-to-peer systems and the optimal balance of effort by programmers and testers), and policy (particularly digital rights management). This research program has been starting to spill over into more general security questions (such as law-enforcement strategy), and into the interface between security and sociology. Most recently it has started to interact with psychology, both through the psychology-and-economics tradition and in response to phishing. The promise of this research program is a novel framework for analyzing information security problems – one that is both principled and effective.
IOT Based Smart Agriculture System

Smart agriculture is an emerging concept, because IOT sensors are capable of providing information about agriculture fields and then act upon based on the user input. In this Paper, it is proposed to develop a Smart agriculture System that uses advantages of cutting edge technologies such as Arduino, IOT and Wireless Sensor Network. The paper aims at making use of evolving technology i.e. IOT and smart agriculture using automation. Monitoring environmental conditions is the major factor to improve yield of the efficient crops. The feature of this paper includes development of a system which can monitor temperature, humidity, moisture and even the movement of animals which may destroy the crops in agricultural field through sensors using Arduino board and in case of any discrepancy send a SMS notification as well as a notification on the application developed for the same to the farmer’s smartphone using Wi-Fi/3G/4G. The system has a duplex communication link based on a cellularInternet interface that allows for data inspection and irrigation scheduling to be programmed through an android application. Because of its energy autonomy and low cost, the system has the potential to be useful in water limited geographically isolated areas.
Greedy Layer-Wise Training of Deep Networks

Deep multi-layer neural networks have many levels of non-linearities, which allows them to potentially represent very compactly highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient-based optimization starting from random initialization appears to often get stuck in poor solutions. Hinton et al. recently introduced a greedy layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this algorithm empirically and explore variants to better understand its success and extend it to cases where the inputs are continuous or where the structure of the input distribution is not revealing enough about the variable to be predicted in a supervised task.
Bridging Paxos and Blockchain Consensus

The distributed consensus problem has been extensively studied in the last four decades as an important problem in distributed systems. Recent advances in decentralized consensus and blockchain technology, however, arose from a disparate model and gave rise to disjoint knowledge-base and techniques than those in the classical consensus research. In this paper we make a case for bridging these two seemingly disparate approaches in order to help transfer the lessons learned from the classical distributed consensus world to the blockchain world and vice versa. To this end, we draw parallels between blockchain consensus and a classical consensus protocol, Paxos. We also survey prominent approaches to improving the throughput and providing instant irreversibility to blockchain consensus and show analogies to the techniques from classical consensus protocols. Finally, inspired by the central role formal methods played in the success of classical consensus research, we suggest more extensive use of formal methods in modeling the blockchains and smartcontracts.
Structural Ambiguity and Lexical Relations

From a certain (admittedly narrow) perspective, one of the annoying features of natural language is the ubiquitous syntactic ambiguity. For a computational model intended to assign syntactic descriptions to natural language text, this seem like a design defect. In general, when context and lexical content are taken into account, such syntactic ambiguity can be resolved: sentences used in context show, for the most part, little ambiguity. But the grammar provides many alternative analyses, and gives little guidance about resolving the ambiguity. Prepositional phrase attachment is the canonical case of structural ambiguity, as in the time worn example,
Blockchain Without Waste : Proof-of-Stake ∗

A blockchain constitutes a distributed ledger that records transactions across a network of agents. Blockchain’s value proposition requires that agents eventually agree on the ledger’s contents since payments possess risk otherwise. Restricted blockchains ensure this consensus by appointing a central authority to dictate payment validity. Permissionless blockchains (e.g. Bitcoin, Ethereum), however, admit no central authority and therefore face a non-trivial issue of inducing consensus endogenously. Nakamoto (2008) provided a temporary solution to the problem by invoking an economic mechanism known as Proof-of-Work (PoW). PoW, however, lacks sustainability, so, in recent years, a variety of alternatives have been proposed. This paper studies the most famous such alternative, Proof-of-Stake (PoS). I provide the first formal economic model of PoS and demonstrate that PoS induces consensus in equilibrium. My result arises because I endogenize blockchain coin prices. Propagating disagreement introduces the prospect of default and thereby reduces blockchain coin value which implies that stake-holders face an implicit cost from delaying consensus. PoS randomly selects a stake-holder to update the blockchain and provides her an explicit monetary incentive, a “block reward,” for her service. In the event of disagreement, block rewards constitute a perverse incentive, but I demonstrate that restricting updating ability to large stake-holders induces an equilibrium in which consensus obtains as soon as possible. I also demonstrate that consensus obtains eventually almost surely in any equilibrium so long as the blockchain employs a modest block reward schedule. My work reveals the economic viability of permissionless blockchains.