nils-herrmann/clean_scirepeval huggingface dataset:
Beating the adaptive bandit with high probability

We provide a principled way of proving Õ(√T) high-probability guarantees for partial-information (bandit) problems over arbitrary convex decision sets. First, we prove a regret guarantee for the full-information problem in terms of “local” norms, both for entropy and self-concordant barrier regularization, unifying these methods. Given one of such algorithms as a black-box, we can convert a bandit problem into a full-information problem using a sampling scheme. The main result states that a high-probability Õ(√T) bound holds whenever the black-box, the sampling scheme, and the estimates of missing information satisfy a number of conditions, which are relatively easy to check. At the heart of the method is a construction of linear upper bounds on confidence intervals. As applications of the main result, we provide the first known efficient algorithm for the sphere with an Õ(√T) high-probability bound. We also derive the result for the n-simplex, improving the O(√nT log(nT)) bound of Auer et al  by replacing the log T term with log log T and closing the gap to the lower bound of Ω(√nT). While Õ(√T) high-probability bounds should hold for general decision sets through our main result, construction of linear upper bounds depends on the particular geometry of the set; we believe that the sphere example already exhibits the necessary ingredients. The guarantees we obtain hold for adaptive adversaries (unlike the in-expectation results of ) and the algorithms are efficient, given that the linear upper bounds on confidence can be computed.
Social CLWPR: A socially enhanced position based routing protocol for handling misbehaviour inVANETs

In this paper, we enhance CLWPR, a cross-layer optimized position-based routing protocol for urban VANET environments, introducing social aspects to its design so as to efficiently address misbehaving (selfish or malicious) nodes that intentionally drop packets and ultimately promote cooperation in the network. The proposed Social CLWPR protocol favors nodes with close favorable social relationship (family members, friends, users with trust connections) as next forwarder nodes, while nodes with explicit distrust connections as indicated from online social media are not preferred in the network. Social CLWPR performance is comparatively evaluated against CLWPR and it demonstrates improved performance in terms of packet delivery ratio.
RWTH OCR : A Large Vocabulary Optical 1 Character Recognition System for Arabic

We present a novel large vocabulary OCR system, which implements a 5 confidenceand margin-based discriminative training approach for model adap6 tation of an HMM based recognition system to handle multiple fonts, different 7 handwriting styles, and their variations. Most current HMM approaches are HTK 8 based systems which are maximum-likelihood (ML) trained and which try to adapt 9 their models to different writing styles using writer adaptive training, unsupervised 10 clustering, or additional writer specific data. Here, discriminative training based 11 on the Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) 12 criteria are used instead. For model adaptation during decoding, an unsupervised 13 confidence-based discriminative training within a two-pass decoding process is pro14 posed. Additionally, we use neural network based features extracted by a hierar15 chical multi-layer-perceptron (MLP) network either in a hybrid MLP/HMM ap16 proach or to discriminatively retrain a Gaussian HMM system in a tandem approach. 17 The proposed framework and methods are evaluated for closed-vocabulary isolated 18 handwritten word recognition on the IfN/ENIT Arabic handwriting database, where 19 the word-error-rate is decreased by more than 50% relative compared to a ML 20 trained baseline system. Preliminary results for large-vocabulary Arabic machine 21 printed text recognition tasks are presented on a novel publicly available newspaper 22 database. 23 RWTH Aachen University Human Language Technology and Pattern Recognition Ahornstr 55, D-52056 Aachen, Germany Tel.: +49-241-80-21613 Fax: +49-241-80-22219
Combining EEG Data with Place and Plausibility Responses as an Approach to Measuring Presence in Outdoor Virtual Environments

Outdoor virtual environments (OVEs) are becoming increasingly popular, as they allow a sense of presence in places that are inaccessible or protected from human intervention. These virtual environments (VEs) need to address physical modalities other than vision and hearing. We analyze the influence of four different physical modalities (vision, hearing, haptics, and olfaction) on the sense of presence on a virtual journey through the sea and the Laurissilva Forest of Funchal, Portugal. We applied Slater et al.'s (2010) method together with data gathered by the Emotiv EPOC EEG in an OVE setting. In such a setting, the combination of haptics and hearing are more important than the typical virtual environment (vision and hearing) in terms of place and plausibility illusions. Our analysis is particularly important for designers interested in crafting similar VEs because we classify different physical modalities according to their importance in enhancing presence.
An ant system based on moderate search for TSP

Ant Colony Optimization (ACO) algorithms often suffer from criticism for the local optimum and premature convergence. In order to overcome these inherent shortcomings shared by most ACO algorithms, we divide the ordinary ants into two types: the utilizationoriented ants and the exploration-oriented ants. The utilization-oriented ants focus on constructing solutions based on the learned experience like ants in many other ACO algorithms. On the other hand, inspired by the adaptive behaviors of some real-world Monomorium ant species who tend to select paths with moderate pheromone concentration, a novel search strategy, that is, a completely new transition rule is designed for the exploration-oriented ants to explore more unknown solutions. In addition, a new corresponding update strategy is also employed. Moreover, applying the new search strategy and update strategy, we propose an improved version of ACO algorithm— Moderate Ant System. This improved algorithm is experimentally turned out to be effective and competitive.
Barbed sutures in facial rejuvenation.

Self-retaining barbed sutures, innovations for nonsurgical facial and neck rejuvenation, are currently available as short APTOS threads or long WOFFLES threads. The author uses APTOS threads for malar rounding, facial tightening and firming, and uses WOFFLES threads as a sling, suspending ptotic facial tissues to the firm, dense tissues of the temporal scalp.
Consumer's Online Shopping Influence Factors and Decision-Making Model

Previous research on online consumer behavior has mostly been confined to the perceived risk which is used to explain those barriers for purchasing online. However, perceived benefit is another important factor which influences consumers’ decision when shopping online. As a result, an integrated consumer online shopping decision-making model is developed which contains three elements—Consumer, Product, and Web Site. This model proposed relative factors which influence the consumers’ intention during the online shopping progress, and divided them into two different dimensions—mentally level and material level. We tested those factors with surveys, from both online volunteers and offline paper surveys with more than 200 samples. With the help of SEM, the experimental results show that the proposed model and method can be used to analyze consumer’s online shopping decision-making process effectively.
Creation of spiking neuron models applied in pattern recognition problems

Some spiking neuron models have proved to solve different linear and non-linear pattern recognition problems. Indeed, only one spiking neuron can generate comparable results as classical artificial neural network. However, depending on the classification problem, one spiking model could be better or less efficient than other. In this paper we propose a methodology to create spiking neuron models using Gene  The new models created are applied in eight pattern recognition problems. The results obtained are compared with previous results generated adopting the Izhikevich spiking neuron model. This first effort will help us to generate spiking neuron models which will be adaptable to a specific pattern recognition problem.
Phone Merging For Code-Switched Speech Recognition

Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of codeswitched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2% WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.
Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets

In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as finding constrained dominant sets from a graph. That is, given a constraint set and a graph, CDSC generates cluster (or clique), which forms a compact and coherent set that contains a subset of the constraint set. The approach is based on a parametrized family of quadratic programs that generalizes the standard quadratic optimization problem. In addition to having a unified framework that simultaneously solves withinand across-camera tracking, the third layer helps link broken tracks of the same person occurring during within-camera tracking. A standard algorithm to extract constrained dominant set from a graph is given by the so-called replicator dynamics whose computational complexity is quadratic per step which makes it handicapped for large-scale applications. In this work, we propose a fast algorithm, based on dynamics from evolutionary game theory, which is efficient and salable to large-scale real-world applications. We have tested this approach on a very large and challenging dataset (namely, MOTchallenge DukeMTMC) and show that the proposed framework outperforms the current state of the art. Even though the main focus of this paper is on multi-target tracking in non-overlapping cameras, proposed approach can also be applied to solve re-identification problem. Towards that end, we also have performed experiments on MARS, one of the largest and challenging video-based person re-identification dataset, and have obtained excellent results. These experiments demonstrate the general applicability of the proposed framework for non-overlapping across-camera tracking and person re-identification tasks.
A Functional Dynamic Boltzmann Machine

Dynamic Boltzmann machines (DyBMs) are recently developed generative models of a time series. They are designed to learn a time series by efficient online learning algorithms, whilst taking long-term dependencies into account with help of eligibility traces, recursively updatable memory units storing descriptive statistics of all the past data. The current DyBMs assume a finitedimensional time series and cannot be applied to a functional time series, in which the dimension goes to infinity (e.g., spatiotemporal data on a continuous space). In this paper, we present a functional dynamic Boltzmann machine (F-DyBM) as a generative model of a functional time series. A technical challenge is to devise an online learning algorithm with which F-DyBM, consisting of functions and integrals, can learn a functional time series using only finite observations of it. We rise to the above challenge by combining a kernel-based function approximation method along with a statistical interpolation method and finally derive closed-form update rules. We design numerical experiments to empirically confirm the effectiveness of our solutions. The experimental results demonstrate consistent error reductions as compared to baseline methods, from which we conclude the effectiveness of F-DyBM for functional time series prediction.
Dirichlet Processes for Joint Learning of Morphology and PoS Tags

This paper presents a joint model for learning morphology and part-of-speech (PoS) tags simultaneously. The proposed method adopts a finite mixture model that groups words having similar contextual features thereby assigning the same PoS tag to those words. While learning PoS tags, words are analysed morphologically by exploiting similar morphological features of the learned PoS tags. The results show that morphology and PoS tags can be learned jointly in a fully unsupervised setting.
Patterns, Causes, and Consequences of Anthropocene Defaunation

Anthropocene defaunation, the global extinction of faunal species and populations and the decline in abundance of individuals within populations, has been predominantly documented in terrestrial ecosystems, but indicators suggest defaunation has been more severe in freshwater ecosystems. Marine defaunation is in a more incipient stage, yet pronounced effects are already apparent and its rapid acceleration seems likely. Defaunation now impacts the planet’s wildlife with profound cascading consequences, ranging from local to global coextinctions of interacting species to the loss of ecological services critical for humanity. Slowing defaunation will require aggressively reducing animal overexploitation and habitat destruction; mitigating climate disruption; and stabilizing the impacts of human population growth and uneven resource consumption. Given its omnipresence, defaunation should receive status of major global environmental change and should be addressed with the same urgency as deforestation, pollution, and climatic change. Global action is needed to prevent defaunation’s current trajectory from catalyzing the planet’s sixth major extinction. 333 Review in Advance first posted online on August 26, 2016. (Changes may still occur before final publication online and in print.) Changes may still occur before final publication online and in print A nn u. R ev . E co l. E vo l. Sy st . 2 01 6. 47 . D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y U ni ve rs ity o f C al if or ni a Sa nt a B ar ba ra o n 10 /2 0/ 16 . F or p er so na l u se o nl y. ES47CH15-Dirzo ARI 19 August 2016 14:55
Optical interconnects for extreme scale computing systems

Large-scale high performance computing is permeating nearly every corner of modern applications spanning from scientific research and business operations, to medical diagnostics, and national security. All these communities rely on computer systems to process vast volumes of data quickly and efficiently, yet progress toward increased computing power has experienced a slowdown in the last number of years. The sheer cost and scale, stemming from the need for extreme parallelism, are among the reasons behind this stall. In particular, very large-scale, ultra-high bandwidth interconnects, essential for maintaining computation performance, represent an increasing portion of the total cost budget. Photonic systems are often cited as ways to break through the energy-bandwidth limitations of conventional electrical wires toward drastically improving interconnect performance. This paper presents an overview of the challenges associated with large-scale interconnects, and reviews how photonic technologies can contribute to addressing these challenges. We review some important aspects of photonics that should not be underestimated in order to truly reap the benefits of cost and power reduction.
Teaching Software Engineering Principles to K-12 Students: A MOOC on Scratch

In the last few years, many books, online puzzles, apps and games have been created to teach young children programming. However, most of these do not introduce children to broader concepts from software engineering, such as debugging and code quality issues like smells, duplication, refactoring and naming. To address this, we designed and ran an online introductory Scratch programming course in which we teach elementary programming concepts and software engineering concepts simultaneously. In total 2,220 children actively participated in our course in June and July of 2016, most of which (73%) between the ages of 7 and 11. In this paper we describe our course design and analyze the resulting data. More specifically, we investigate whether 1) students find programming concepts more difficult than software engineering concepts, 2) there are age-related differences in their performance, and 3) we can predict successful course completion. Our results show that there is no difference in students' scores between the programming concepts and the software engineering concepts, suggesting that it is indeed possible to teach these concepts to this age group. We also find that students over 12 years of age perform significantly better in questions related to operators and procedures. Finally, we identify the factors from the students' profile and their behaviour in the first week of the course that can be used to predict its successful completion.
Graviola: a novel promising natural-derived drug that inhibits tumorigenicity and metastasis of pancreatic cancer cells in vitro and in vivo through altering cell metabolism.

Pancreatic tumors are resistant to conventional chemotherapies. The present study was aimed at evaluating the potential of a novel plant-derived product as a therapeutic agent for pancreatic cancer (PC). The effects of an extract from the tropical tree Annona Muricata, commonly known as Graviola, was evaluated for cytotoxicity, cell metabolism, cancer-associated protein/gene expression, tumorigenicity, and metastatic properties of PC cells. Our experiments revealed that Graviola induced necrosis of PC cells by inhibiting cellular metabolism. The expression of molecules related to hypoxia and glycolysis in PC cells (i.e. HIF-1α, NF-κB, GLUT1, GLUT4, HKII, and LDHA) were downregulated in the presence of the extract. In vitro functional assays further confirmed the inhibition of tumorigenic properties of PC cells. Overall, the compounds that are naturally present in a Graviola extract inhibited multiple signaling pathways that regulate metabolism, cell cycle, survival, and metastatic properties in PC cells. Collectively, alterations in these parameters led to a decrease in tumorigenicity and metastasis of orthotopically implanted pancreatic tumors, indicating promising characteristics of the natural product against this lethal disease.
Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study

Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden.



The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy.



A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability.



Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants.



Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms.
Underwater Vehicle Obstacle Avoidance and Path Planning Using a MultiBeam Forward Looking Sonar

This paper describes a new framework for segmentation of sonar images, tracking of underwater objects and motion estimation. This framework is applied to the design of an obstacle avoidance and path planning system for underwater vehicles based on a multi-beam forward looking sonar sensor. The real-time data flow (acoustic images) at the input of the system is first segmented and relevant features are extracted. We also take advantage of the real-time data stream to track the obstacles in following frames to obtain their dynamic characteristics. This allows us to optimize the preprocessing phases in segmenting only the relevant part of the images. Once the static (size and shape) as well as dynamic characteristics (velocity, acceleration, ...) of the obstacles have been computed, we create a representation of the vehicle’s workspace based on these features. This representation uses constructive solid geometry(CSG) to create a convex set of obstacles defining the workspace. The tracking takes also into account obstacles which are no longer in the field of view of the sonar in the path planning phase. A well-proven nonlinear search (sequential quadratic programming) is then employed, where obstacles are expressed as constraints in the search space. This approach is less affected by local minima than classical methods using potential fields. The proposed system is not only capable of obstacle avoidance but also of path planning in complex environments which include fast moving obstacles. Results obtained on real sonar data are shown and discussed. Possible applications to sonar servoing and real-time motion estimation are also discussed.