nils-herrmann/clean_scirepeval huggingface dataset:
Real-Time Multisensor Data Retrieval for Cloud Robotic Systems

Cloud technology elevates the potential of robotics with which robots possessing various capabilities and resources may share data and combine new skills through cooperation. With multiple robots, a cloud robotic system enables intensive and complicated tasks to be carried out in an optimal and cooperative manner. Multisensor data retrieval (MSDR) is one of the key fundamental tasks to share the resources. Having attracted wide attention, MSDR is facing severe technical challenges. For example, MSDR is particularly difficult when cloud cluster hosts accommodate unpredictable data requests triggered by multiple robots operating in parallel. In these cases, near real-time responses are essential while addressing the problem of the synchronization of multisensor data simultaneously. In this paper, we present a framework targeting near real-time MSDR, which grants asynchronous access to the cloud from the robots. We propose a market-based management strategy for efficient data retrieval. It is validated by assessing several quality-of-service (QoS) criteria, with emphasis on facilitating data retrieval in near real-time. Experimental results indicate that the MSDR framework is able to achieve excellent performance under the proposed management strategy in typical cloud robotic scenarios.
Image-Based Pothole Detection System for ITS Service and Road Management System

Potholes can generate damage such as flat tire and wheel damage, impact and damage of lower vehicle, vehicle collision, and major accidents. Thus, accurately and quickly detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. In this study, a pothole detection method based on twodimensional (2D) images is proposed for improving the existing method and designing a pothole detection system to be applied to ITS service and road management system. For experiments, 2D road images that were collected by a survey vehicle in Korea were used and the performance of the proposed method was compared with that of the existing method for several conditions such as road, recording, and brightness. The results are promising, and the information extracted using the proposed method can be used, not only in determining the preliminary maintenance for a road management system and in taking immediate action for their repair and maintenance, but also in providing alert information of potholes to drivers as one of ITS services.
Impairment in the cervical flexors: a comparison of whiplash and insidious onset neck pain patients.

There has been little investigation into whether or not differences exist in the nature of physical impairment associated with neck pain of whiplash and insidious origin. This study examined the neck flexor synergy during performance of the cranio-cervical flexion test, a test targeting the action of the deep neck flexors. Seventy-five volunteer subjects participated in this study and were equally divided between Group 1, asymptomatic control subjects, Group 2, subjects with insidious onset neck pain and Group 3, subjects with neck pain following a whiplash injury. The cranio-cervical flexion test was performed in five progressive stages of increasing cranio-cervical flexion range. Subjects' performance was guided by feedback from a pressure sensor inserted behind the neck which monitored the slight flattening of the cervical lordosis which occurs with the contraction of longus colli. Myoelectric signals (EMG) were detected from the muscles during performance of the test. The results indicated that both the insidious onset neck pain and whiplash groups had higher measures of EMG signal amplitude (normalized root mean square) in the sternocleidomastoid during each stage of the test compared to the control subjects (all P<0.05) and had significantly greater shortfalls from the pressure targets in the test stages (P<0.05). No significant differences were evident between the neck pain groups in either parameter indicating that this physical impairment in the neck flexor synergy is common to neck pain of both whiplash and insidious origin.
Recovering Semantic Traceability Links between APIs and Security Vulnerabilities: An Ontological Modeling Approach

Over the last decade, a globalization of the software industry took place, which facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the software engineering community, with not only components but also their problems and vulnerabilities being now shared. For example, vulnerabilities found in APIs no longer affect only individual projects but instead might spread across projects and even global software ecosystem borders. Tracing these vulnerabilities at a global scale becomes an inherently difficult task since many of the existing resources required for such analysis still rely on proprietary knowledge representation. In this research, we introduce an ontology-based knowledge modeling approach that can eliminate such information silos. More specifically, we focus on linking security knowledge with other software knowledge to improve traceability and trust in software products (APIs). Our approach takes advantage of the Semantic Web and its reasoning services, to trace and assess the impact of security vulnerabilities across project boundaries. We present a case study, to illustrate the applicability and flexibility of our ontological modeling approach by tracing vulnerabilities across project and resource boundaries.
Hippocampal SWR Activity Predicts Correct Decisions during the Initial Learning of an Alternation Task

The hippocampus frequently replays memories of past experiences during sharp-wave ripple (SWR) events. These events can represent spatial trajectories extending from the animal's current location to distant locations, suggesting a role in the evaluation of upcoming choices. While SWRs have been linked to learning and memory, the specific role of awake replay remains unclear. Here we show that there is greater coordinated neural activity during SWRs preceding correct, as compared to incorrect, trials in a spatial alternation task. As a result, the proportion of cell pairs coactive during SWRs was predictive of subsequent correct or incorrect responses on a trial-by-trial basis. This effect was seen specifically during early learning, when the hippocampus is essential for task performance. SWR activity preceding correct trials represented multiple trajectories that included both correct and incorrect options. These results suggest that reactivation during awake SWRs contributes to the evaluation of possible choices during memory-guided decision making.
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems.



We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations.



We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients.



The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.
Near optimum error correcting coding and decoding: turbo-codes

AbsfructThis paper presents a new family of convolutional codes, nicknamed turbo-codes, built from a particular concatenation of two recursive systematic codes, linked together by nonuniform interleaving. Decoding calls on iterative processing in which each component decoder takes advantage of the work of the other at the previous step, with the aid of the original concept of extrinsic information. For sufficiently large interleaving sizes, the correcting performance of turbo-codes, investigated by simulation, appears to be close to the theoretical limit predicted by Shannon.
How analysts cognitively “connect the dots”

As analysts attempt to make sense of a collection of documents, such as intelligence analysis reports, they need to “connect the dots” between pieces of information that may initially seem unrelated. We conducted a user study to analyze the cognitive process by which users connect pairs of documents and how they spatialize connections. Users created conceptual stories that connected the dots using a range of organizational strategies and spatial representations. Insights from our study can drive the design of data mining algorithms and visual analytic tools to support analysts' complex cognitive processes.
A statistical framework for fair predictive algorithms

Statistical and machine learning algorithms are increasingly used to inform decisions that have large impacts on individuals’ lives. Examples include hiring , predictive policing [13], pre-trial risk assessment of recidivism[6, 2], and risk of violence while incarcerated . In many of these cases, the outcome variable to which the predictive models are trained is observed with bias with respect to some legally protected classes. For example, police records do not constitute a representative sample of all crimes . In particular, black drug users are arrested at a rate that is several times that of white drug users despite the fact that black and white populations are estimated by public health officials to use drugs at roughly the same rate [11]. Algorithms trained on such data will produce predictions that are biased against groups that are disproportionately represented in the training data. Several approaches have been proposed to correct unfair predictive models. The simplest approach is to exclude the protected variable(s) from the analysis, under the belief that doing so will result in “race-neutral” predictions . Of course, simply excluding a protected variable is insufficient to avoid discriminatory predictions, as any included variables that are correlated with the protected variables still contain information about the protected characteristic. In the case of linear models, this phenomenon is well-known, and is referred to as omitted variable bias . Another approach that has been proposed in the computer science literature is to remove information about the protected variables from the set of covariates to be used in predictive models [7, 3]. A third alternative is to modify the outcome variable. For example,  use a naive Bayes classifier to rank each observation and perturb the outcome such that predictions produced by the algorithm are independent of the protected variable. A discussion of several more algorithms for binary protected and outcome variables can be found in . The approach we propose is most similar to [7], though we approach the problem from a statistical modeling perspective. We define a procedure consisting of a chain of conditional models. Within this framework, both protecting and adjusting variables of arbitrary type becomes natural. Whereas previous work has been limited to protecting only binary or categorical variables and adjusting a limited number of covariates, our proposed framework allows for an arbitrary number of variables
Signaling sarcasm: From hyperbole to hashtag

To avoid a sarcastic message being understood in its unintended literal meaning, in microtexts such as messages on Twitter.com sarcasm is often explicitly marked with a hashtag such as ‘#sarcasm’. We collected a training corpus of about 406 thousand Dutch tweets with hashtag synonyms denoting sarcasm. Assuming that the human labeling is correct (annotation of a sample indicates that about 90% of these tweets are indeed sarcastic), we train a machine learning classifier on the harvested examples, and apply it to a sample of a day’s stream of 2.25 million Dutch tweets. Of the 353 explicitly marked tweets on this day, we detect 309 (87%) with the hashtag removed. We annotate the top of the ranked list of tweets most likely to be sarcastic that do not have the explicit hashtag. 35% of the top250 ranked tweets are indeed sarcastic. Analysis indicates that the use of hashtags reduces the further use of linguistic markers for signaling sarcasm, such as exclamations and intensifiers. We hypothesize that explicit markers such as hashtags are the digital extralinguistic equivalent of non-verbal expressions that people employ in live interaction when conveying sarcasm. Checking the consistency of our finding in a language from another language family, we observe that in French the hashtag ‘#sarcasme’ has a similar polarity switching function, be it to a lesser extent.
Ship motion prediction for launch and recovery of air vehicles

Due to the random nature of the ship's motion in an open water environment, the deployment and the landing of air vehicles from a ship can often be difficult and even dangerous. The ability to reliably predict the motion will allow improvements in safety on board ships and facilitate more accurate deployment of vehicles off ships. This paper presents an investigation into the application of artificial neural network methods trained using singular value decomposition and genetic algorithms for the prediction of ship motion. It is shown that the artificial neural network produces excellent predictions and is able to predict the ship motion satisfactorily for up to 7 seconds.
Criteria for Human-Compatible AI in Two-Player Vision-Language Tasks

We propose rule-based search systems that outperform not only the state-of-the-art but the human performance, measured in accuracy, in GuessWhat?!, a vision-language game where either of two players can be a human. Although those systems achieve the high accuracy, they do not meet other requirements to be considered as an AI system that communicates effectively with humans. To clarify what they lack, we suggest the use of three criteria to enable effective communication with humans in vision-language tasks. These criteria also apply to other two-player vision-language tasks that require communication with humans, e.g., ReferIt.
Conceptualizing Employee Silence and Employee Voice as Multidimensional Constructs *

 Employees often have ideas, information, and opinions for constructive ways to improve work and work organizations. Sometimes these employees exercise voice and express their ideas, information, and opinions; and other times they engage in silence and withhold their ideas, information, and opinions. On the surface, expressing and withholding behaviours might appear to be polar opposites because silence implies not speaking while voice implies speaking up on important issues and problems in organizations. Challenging this simplistic notion, this paper presents a conceptual framework suggesting that employee silence and voice are best conceptualized as separate, multidimensional constructs. Based on employee motives, we differentiate three types of silence (Acquiescent Silence, Defensive Silence, and ProSocial Silence) and three parallel types of voice (Acquiescent Voice, Defensive Voice, and ProSocial Voice) where withholding important information is not simply the absence of voice. Building on this conceptual framework, we further propose that silence and voice have differential consequences to employees in work organizations. Based on fundamental differences in the overt behavioural cues provided by silence and voice, we present a series of propositions predicting that silence is more ambiguous than voice, observers are more likely to misattribute employee motives for silence than for voice, and misattributions for motives behind silence will lead to more incongruent consequences (both positive and negative) for employees (than for voice). We conclude by discussing implications for future research and for managers. Journal of Management Studies 40:6 September 2003 0022-2380
Boosting Variational Inference

Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions. For practical reasons, the family of distributions in VI is usually constrained so that it does not include the exact posterior, even as a limit point. Thus, no matter how long VI is run, the resulting approximation will not approach the exact posterior. We propose to instead consider a more flexible approximating family consisting of all possible finite mixtures of a parametric base distribution (e.g., Gaussian). For efficient inference, we borrow ideas from gradient boosting to develop an algorithm we call boosting variational inference (BVI). BVI iteratively improves the current approximation by mixing it with a new component from the base distribution family and thereby yields progressively more accurate posterior approximations as more computing time is spent. Unlike a number of common VI variants including mean-field VI, BVI is able to capture multimodality, general posterior covariance, and nonstandard posterior shapes.
A Unified Coding Framework for Distributed Computing with Straggling Servers

We propose a unified coding framework for distributed computing with straggling servers, by introducing a tradeoff between "latency of computation" and "load of communication" for some linear computation tasks. We show that the coded scheme of [1]-[3] that repeats the intermediate computations to create coded multicasting opportunities to reduce communication load, and the coded scheme of [4] that generates redundant intermediate computations to combat against straggling servers can be viewed as special instances of the proposed framework, by considering two extremes of this tradeoff: minimizing either the load of communication or the latency of computation individually. Furthermore, the latency-load tradeoff achieved by the proposed coded framework allows to systematically operate at any point on that tradeoff to perform distributed computing tasks. We also prove an information-theoretic lower bound on the latency- load tradeoff, which is shown to be within a constant multiplicative gap from the achieved tradeoff at the two end points.
Multi-objective Multi-view Spectral Clustering via Pareto Optimization

Traditionally, spectral clustering is limited to a single objective: finding the normalized min-cut of a single graph. However, many real-world datasets, such as scientific data (fMRI scans of different individuals), social data (different types of connections between people), web data (multi-type data), are generated from multiple heterogeneous sources. How to optimally combine knowledge from multiple sources to improve spectral clustering remains a developing area. Previous work on multi-view clustering formulated the problem as a single objective function to optimize, typically by combining the views under a compatibility assumption and requiring the users to decide the importance of each view a priori. In this work, we propose a multi-objective formulation and show how to solve it using Pareto optimization. The Pareto frontier captures all possible good cuts without requiring the users to set the “correct” parameter. The effectiveness of our approach is justified by both theoretical analysis and empirical results. We also demonstrate a novel application of our approach: resting-state fMRI analysis.
Fit4life: the design of a persuasive technology promoting healthy behavior and ideal weight

This is a critical design paper offering a possible scenario of use intended to provoke reflection about values and politics of design in persuasive computing. We describe the design of a system - Fit4Life - that encourages individuals to address the larger goal of reducing obesity in society by promoting individual healthy behaviors. Using the Persuasive Systems Design Model [26], this paper outlines the Fit4Life persuasion context, the technology, its use of persuasive messages, and an experimental design to test the system's efficacy. We also contribute a novel discussion of the ethical and sociocultural considerations involved in our design, an issue that has remained largely unaddressed in the existing persuasive technologies literature [29].
Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy.

Immunomodulatory drugs differ in mechanism-of-action from directly cytotoxic cancer therapies. Identifying factors predicting clinical response could guide patient selection and therapeutic optimization.



Patients (N = 41) with melanoma, non-small cell lung carcinoma (NSCLC), renal cell carcinoma (RCC), colorectal carcinoma, or castration-resistant prostate cancer were treated on an early-phase trial of anti-PD-1 (nivolumab) at one institution and had evaluable pretreatment tumor specimens. Immunoarchitectural features, including PD-1, PD-L1, and PD-L2 expression, patterns of immune cell infiltration, and lymphocyte subpopulations, were assessed for interrelationships and potential correlations with clinical outcomes.



Membranous (cell surface) PD-L1 expression by tumor cells and immune infiltrates varied significantly by tumor type and was most abundant in melanoma, NSCLC, and RCC. In the overall cohort, PD-L1 expression was geographically associated with infiltrating immune cells (P < 0.001), although lymphocyte-rich regions were not always associated with PD-L1 expression. Expression of PD-L1 by tumor cells and immune infiltrates was significantly associated with expression of PD-1 on lymphocytes. PD-L2, the second ligand for PD-1, was associated with PD-L1 expression. Tumor cell PD-L1 expression correlated with objective response to anti-PD-1 therapy, when analyzing either the specimen obtained closest to therapy or the highest scoring sample among multiple biopsies from individual patients. These correlations were stronger than borderline associations of PD-1 expression or the presence of intratumoral immune cell infiltrates with response.



Tumor PD-L1 expression reflects an immune-active microenvironment and, while associated other immunosuppressive molecules, including PD-1 and PD-L2, is the single factor most closely correlated with response to anti-PD-1 blockade.