nils-herrmann/clean_scirepeval huggingface dataset:
The Gouy-Stodola Theorem in Bioenergetic Analysis of Living Systems ( Irreversibility in Bioenergetics of Living Systems )

Thermodynamics studies the transformations of energy occurring in open systems. Living systems, with particular reference to cells, are complex systems in which energy transformations occur. Thermo-electro-chemical processes and transports occur across their border, the cells membranes. These processes take place with important differences between healthy and diseased states. In particular, different thermal and biochemical behaviours can be highlighted between these two states and they can be related to the energy transformations inside the living systems, in particular the metabolic behaviour. Moreover, living systems waste heat. This heat is the consequence of the internal irreversibility. Irreversibility is effectively studied by using the Gouy-Stodola theorem. Consequently, this approach can be introduced in the analysis of the states of living systems, in order to obtain a unifying approach to study them. Indeed, this approach allows us to consider living systems as black boxes and analyze only the inflows and outflows and their changes in relation to the modification of the environment, so information on the systems can be obtained by analyzing their behaviour in relation to the modification of external perturbations. This paper presents a review of the recent results obtained in the thermodynamics analysis of cell systems.
Efficient Solution Algorithms for Factored MDPs

This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and the transition model using a dynamic Bayesian network. This representation often allows an exponential reduction in the representation size of structured MDPs, but the complexity of exact solution algorithms for such MDPs can grow exponentially in the representation size. In this paper, we present two approximate solution algorithms that exploit structure in factored MDPs. Both use an approximate value function represented as a linear combination of basis functions, where each basis function involves only a small subset of the domain variables. A key contribution of this paper is that it shows how the basic operations of both algorithms can be performed efficiently in closed form, by exploiting both additive and context-specific structure in a factored MDP. A central element of our algorithms is a novel linear program decomposition technique, analogous to variable elimination in Bayesian networks, which reduces an exponentially large LP to a provably equivalent, polynomial-sized one. One algorithm uses approximate linear programming, and the second approximate dynamic programming. Our dynamic programming algorithm is novel in that it uses an approximation based on max-norm, a technique that more directly minimizes the terms that appear in error bounds for approximate MDP algorithms. We provide experimental results on problems with over 10 states, demonstrating a promising indication of the scalability of our approach, and compare our algorithm to an existing state-of-the-art approach, showing, in some problems, exponential gains in computation time.
Ellipse-specific direct least-square fitting

This work presents the rst direct method for specii-cally tting ellipses in the least squares sense. Previous approaches used either generic conic tting or relied on iterative methods to recover elliptic solutions. The proposed method is (i) ellipse-speciic, (ii) directly solved by a generalised eigen-system, (iii) has a desirable low-eccentricity bias, and (iv) is robust to noise. We provide a theoretical demonstration, several examples and the Matlab coding of the algorithm.
The colonization of land by animals: molecular phylogeny and divergence times among arthropods

The earliest fossil evidence of terrestrial animal activity is from the Ordovician, ~450 million years ago (Ma). However, there are earlier animal fossils, and most molecular clocks suggest a deep origin of animal phyla in the Precambrian, leaving open the possibility that animals colonized land much earlier than the Ordovician. To further investigate the time of colonization of land by animals, we sequenced two nuclear genes, glyceraldehyde-3-phosphate dehydrogenase and enolase, in representative arthropods and conducted phylogenetic and molecular clock analyses of those and other available DNA and protein sequence data. To assess the robustness of animal molecular clocks, we estimated the deuterostome-arthropod divergence using the arthropod fossil record for calibration and tunicate instead of vertebrate sequences to represent Deuterostomia. Nine nuclear and 15 mitochondrial genes were used in phylogenetic analyses and 61 genes were used in molecular clock analyses. Significant support was found for the unconventional pairing of myriapods (millipedes and centipedes) with chelicerates (spiders, scorpions, horseshoe crabs, etc.) using nuclear and mitochondrial genes. Our estimated time for the divergence of millipedes (Diplopoda) and centipedes (Chilopoda) was 442 ± 50 Ma, and the divergence of insects and crustaceans was estimated as 666 ± 58 Ma. Our results also agree with previous studies suggesting a deep divergence (~1100 – 900 Ma) for arthropods and deuterostomes, considerably predating the Cambrian Explosion seen in the animal fossil record. The consistent support for a close relationship between myriapods and chelicerates, using mitochondrial and nuclear genes and different methods of analysis, suggests that this unexpected result is not an artefact of analysis. We propose the name Myriochelata for this group of animals, which includes many that immobilize prey with venom. Our molecular clock analyses using arthropod fossil calibrations support earlier studies using vertebrate calibrations in finding that deuterostomes and arthropods diverged hundreds of millions of years before the Cambrian explosion. However, our molecular time estimate for the divergence of millipedes and centipedes is close to the divergence time inferred from fossils. This suggests that arthropods may have adapted to the terrestrial environment relatively late in their evolutionary history.
Extended Target Tracking Using Gaussian Processes with High-Resolution Automotive Radar

In this paper, an implementation of an extended target tracking filter using measurements from high-resolution automotive Radio Detection and Ranging (RADAR) is proposed. Our algorithm uses the Cartesian point measurements from the target's contour as well as the Doppler range rate provided by the RADAR to track a target vehicle's position, orientation, and translational and rotational velocities. We also apply a Gaussian Process (GP) to model the vehicle's shape. To cope with the nonlinear measurement equation, we implement an Extended Kalman Filter (EKF) and provide the necessary derivatives for the Doppler measurement. We then evaluate the effectiveness of incorporating the Doppler rate on simulations and on 2 sets of real data.
Neural Compositional Denotational Semantics for Question Answering

Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a KG and a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituent spans, culminating in a grounding for the complete sentence which answers the question. For example, to interpret “not green”, the model represents “green” as a set of KG entities and “not” as a trainable ungrounded vector—and then uses this vector to parameterize a composition function that performs a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent from endtask supervision. The model learns a variety of challenging semantic operators, such as quantifiers, disjunctions and composed relations, and infers latent syntactic structure. It also generalizes well to longer questions than seen in its training data, in contrast to RNN, its treebased variants, and semantic parsing baselines.
Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis

In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts and make the dataset public available for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators.
Distributed dynamic programming

We consider distributed algorithms for solving dynamic programming problems whereby several processors participate simultaneously in the computation while maintaining coordination by information exchange via communication links. A model of asynchronous distributed computation is developed which requires very weak assumptions on the ordering of computations, the timing of information exchange, the amount of local information needed at each computation node, and the initial conditions for the algorithm. The class of problems considered is very broad and includes shortest path problems, and finite and infinite horizon stochastic optimal control problems. When specialized to a shortest path problem the algorithm reduces to the algorithm originally implemented for routing of messages in the ARPANET.
Digital Forensics in the Age of Big Data: Challenges, Approaches, and Opportunities

The age of big data opens new opportunities in various fields. While the availability of a big dataset can be helpful in some scenarios, it introduces new challenges in digital forensics investigations. The existing tools and infrastructures cannot meet the expected response time when we investigate on a big dataset. Forensics investigators will face challenges while identifying necessary pieces of evidence from a big dataset, and collecting and analyzing those evidence. In this article, we propose the first working definition of big data forensics and systematically analyze the big data forensics domain to explore the challenges and issues in this forensics paradigm. We propose a conceptual model for supporting big data forensics investigations and present several use cases, where big data forensics can provide new insights to determine facts about criminal incidents.
Image Tagging with Social Assistance

Image tagging, also known as image annotation and image conception detection, has been extensively studied in the literature. However, most existing approaches can hardly achieve satisfactory performance owing to the deficiency and unreliability of the manually-labeled training data. In this paper, we propose a new image tagging scheme, termed social assisted media tagging (SAMT), which leverages the abundant user-generated images and the associated tags as the "social assistance" to learn the classifiers. We focus on addressing the following major challenges: (a) the noisy tags associated to the web images; and (b) the desirable robustness of the tagging model. We present a joint image tagging framework which simultaneously refines the erroneous tags of the web images as well as learns the reliable image classifiers. In particular, we devise a novel tag refinement module for identifying and eliminating the noisy tags by substantially exploring and preserving the low-rank nature of the tag matrix and the structured sparse property of the tag errors. We develop a robust image tagging module based on the l2,p-norm for learning the reliable image classifiers. The correlation of the two modules is well explored within the joint framework to reinforce each other. Extensive experiments on two real-world social image databases illustrate the superiority of the proposed approach as compared to the existing methods.
Focused, Aroused, but so Distractible: Temporal Perspectives on Multitasking and Communications

A common assumption in studies of interruptions is that one is focused in an activity and then distracted by other stimuli. We take the reverse perspective and examine whether one might first be in an attentional state that makes one susceptible to communications typically associated with distraction. We explore the confluence of multitasking and workplace communications from three temporal perspectives -- prior to an interaction, when tasks and communications are interleaved, and at the end of the day. Using logging techniques and experience sampling, we observed 32 employees in situ for five days. We found that certain attentional states lead people to be more susceptible to particular types of interaction. Rote work is followed by more Facebook or face-to-face interaction. Focused and aroused states are followed by more email. The more time in email and face-fo-face interaction, and the more total screen switches, the less productive people feel at the day's end. We present the notion of emotional homeostasis along with new directions for multitasking research.
Auxiliary Deep Generative Models

Deep generative models parameterized by neural networks have recently achieved state-ofthe-art performance in unsupervised and semisupervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge faster with better results. We show state-of-theart performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets.
Breaking Fitness Records without Moving: Reverse Engineering and Spoofing Fitbit

Tens of millions of wearable tness trackers are shipped yearly to consumers who routinely collect information about their exercising patterns. Smartphones push this health-related data to vendors' cloud platforms, enabling users to analyze summary statistics on-line and adjust their habits. Third-parties including health insurance providers now o er discounts and nancial rewards in exchange for such private information and evidence of healthy lifestyles. Given the associated monetary value, the authenticity and correctness of the activity data collected becomes imperative. In this paper, we provide an in-depth security analysis of the operation of tness trackers commercialized by Fitbit, the wearables market leader. We reveal an intricate security through obscurity approach implemented by the user activity synchronization protocol running on the devices we analyze. Although non-trivial to interpret, we reverse engineer the message semantics, demonstrate how falsi ed user activity reports can be injected, and argue that based on our discoveries, such attacks can be performed at scale to obtain nancial gains. We further document a hardware attack vector that enables circumvention of the end-to-end protocol encryption present in the latest Fitbit rmware, leading to the spoo ng of valid encrypted tness data. Finally, we give guidelines for avoiding similar vulnerabilities in future system designs.
Chaff Bugs: Deterring Attackers by Making Software Buggier

Sophisticated attackers find bugs in software, evaluate their exploitability, and then create and launch exploits for bugs found to be exploitable. Most efforts to secure software attempt either to eliminate bugs or to add mitigations that make exploitation more difficult. In this paper, we introduce a new defensive technique called chaff bugs, which instead target the bug discovery and exploit creation stages of this process. Rather than eliminating bugs, we instead add large numbers of bugs that are provably (but not obviously) non-exploitable. Attackers who attempt to find and exploit bugs in software will, with high probability, find an intentionally placed non-exploitable bug and waste precious resources in trying to build a working exploit. We develop two strategies for ensuring non-exploitability and use them to automatically add thousands of non-exploitable bugs to real-world software such as nginx and libFLAC; we show that the functionality of the software is not harmed and demonstrate that our bugs look exploitable to current triage tools. We believe that chaff bugs can serve as an effective deterrent against both human attackers and automated Cyber Reasoning Systems (CRSes).
Clipped Action Policy Gradient

Many continuous control tasks have bounded action spaces. When policy gradient methods are applied to such tasks, out-of-bound actions need to be clipped before execution, while policies are usually optimized as if the actions are not clipped. We propose a policy gradient estimator that exploits the knowledge of actions being clipped to reduce the variance in estimation. We prove that our estimator, named clipped action policy gradient (CAPG), is unbiased and achieves lower variance than the conventional estimator that ignores action bounds. Experimental results demonstrate that CAPG generally outperforms the conventional estimator, indicating that it is a better policy gradient estimator for continuous control tasks. The source code is available at https: //github.com/pfnet-research/capg.
Invertible Autoencoder for domain adaptation

The unsupervised image-to-image translation aims at finding a mapping between the source (A) and target (B) image domains, where in many applications aligned image pairs are not available at training. This is an ill-posed learning problem since it requires inferring the joint probability distribution from marginals. Joint learning of coupled mappings FAB : A → B and FBA : B → A is commonly used by the state-of-the-art methods, like CycleGAN (Zhu et al., 2017), to learn this translation by introducing cycle consistency requirement to the learning problem, i.e. FAB(FBA(B)) ≈ B and FBA(FAB(A)) ≈ A. Cycle consistency enforces the preservation of the mutual information between input and translated images. However, it does not explicitly enforce FBA to be an inverse operation to FAB. We propose a new deep architecture that we call invertible autoencoder (InvAuto) to explicitly enforce this relation. This is done by forcing an encoder to be an inverted version of the decoder, where corresponding layers perform opposite mappings and share parameters. The mappings are constrained to be orthonormal. The resulting architecture leads to the reduction of the number of trainable parameters (up to 2 times). We present image translation results on benchmark data sets and demonstrate state-of-the art performance of our approach. Finally, we test the proposed domain adaptation method on the task of road video conversion. We demonstrate that the videos converted with InvAuto have high quality and show that the NVIDIA neural-network-based end-toend learning system for autonomous driving, known as PilotNet, trained on real road videos performs well when tested on the converted ones.
Incentives for Tax Planning and Avoidance : Evidence from the Field Citation

We analyze survey responses from nearly 600 corporate tax executives to investigate firms’ incentives and disincentives for tax planning. While many researchers hypothesize that reputational concerns affect the degree to which managers engage in tax planning, this hypothesis is difficult to test with archival data. Our survey allows us to investigate reputational influences and indeed we find that reputational concerns are important – 69% of executives rate reputation as important and the factor ranks second in order of importance among all factors explaining why firms do not adopt a potential tax planning strategy. We also find that financial accounting incentives play a role. For example, 84% of publicly traded firms respond that top management at their company cares at least as much about the GAAP ETR as they do about cash taxes paid and 57% of public firms say that increasing earnings per share is an important outcome from a tax planning strategy.
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features

Relation classification is associated with many potential applications in the artificial intelligence area. Recent approaches usually leverage neural networks based on structure features such as syntactic or dependency features to solve this problem. However, high-cost structure features make such approaches inconvenient to be directly used. In addition, structure features are probably domaindependent. Therefore, this paper proposes a bidirectional long-short-term-memory recurrent-neuralnetwork (Bi-LSTM-RNN) model based on low-cost sequence features to address relation classification. This model divides a sentence or text segment into five parts, namely two target entities and their three contexts. It learns the representations of entities and their contexts, and uses them to classify relations. We evaluate our model on two standard benchmark datasets in different domains, namely SemEval-2010 Task 8 and BioNLP-ST 2016 Task BB3. In the former dataset, our model achieves comparable performance compared with other models using sequence features. In the latter dataset, our model obtains the third best results compared with other models in the official evaluation. Moreover, we find that the context between two target entities plays the most important role in relation classification. Furthermore, statistic experiments show that the context between two target entities can be used as an approximate replacement of the shortest dependency path when dependency parsing is not used.
Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network

Document-level sentiment classification is a fundamental problem which aims to predict a user’s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the sentiment and products which are evaluated. To address the issue, we propose a deep memory network for document-level sentiment classification which could capture the user and product information at the same time. To prove the effectiveness of our algorithm, we conduct experiments on IMDB and Yelp datasets and the results indicate that our model can achieve better performance than several existing methods.
Integration of ePortfolios in Learning Management Systems

The LMS plays a decisive role in most eLearning environments. Although they integrate many useful tools for managing eLearning activities, they must also be effectively integrated with other specialized systems typically found in an educational environment such as Repositories of Learning Objects or ePortfolio Systems. Both types of systems evolved separately but in recent years the trend is to combine them, allowing the LMS to benefit from using the ePortfolio assessment features. This paper details the most common strategies for integrating an ePortfolio system into an LMS: the data, the API and the tool integration strategies. It presents a comparative study of strategies based on the technical skills, degree of coupling, security features, batch integration, development effort, status and standardization. This study is validated through the integration of two of the most representative systems on each category respectively Mahara and Moodle.