nils-herrmann/clean_scirepeval huggingface dataset:
Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations

We present a large scale unified natural language inference (NLI) dataset for providing insight into how well sentence representations capture distinct types of reasoning. We generate a large-scale NLI dataset by recasting 11 existing datasets from 7 different semantic tasks. We use our dataset of approximately half a million context-hypothesis pairs to test how well sentence encoders capture distinct semantic phenomena that are necessary for general language understanding. Some phenomena that we consider are event factuality, named entity recognition, figurative language, gendered anaphora resolution, and sentiment analysis, extending prior work that included semantic roles and frame semantic parsing. Our dataset will be available at https:// www.decomp.net, to grow over time as additional resources are recast.
Disease Forecasting System Using Data Mining Methods

The healthcare industry collects large amounts of healthcare information which cannot be mined to find unknown information for efficient evaluation. Discovery of buried patterns frequently goes unexploited. Heart disease is a term for defining a huge amount of healthcare conditions that are related to the heart. This medicinal condition defines the unpredicted health conditions that directly control all the parts of the heart. Different data mining techniques such as association rule mining, classification, clustering are used to predict the heart disease in health care industry. The heart disease database is preprocessed to make the mining process more efficient. The preprocessed data is clustered using clustering algorithms like K-means to cluster relevant data in database. Maximal Frequent Item set Algorithm (MAFIA) is used for mining maximal frequent patterns in heart disease database. The frequent patterns can be classified using C4.5 algorithm as training algorithm using the concept of information entropy. The results showed that the designed prediction system is capable of predicting the heart attack successfully.
What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties

Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. “Downstream” tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.
Multi-class pattern classification using neural networks

Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and competitive study in multi-class neural learning with focuses on issues including neural network architecture, encoding schemes, training methodology and training time complexity. Our study includes multi-class pattern classification using either a system of multiple neural networks or a single neural network, and modeling pattern classes using one-against-all, one-against-one, one-againsthigher-order, and P-against-Q. We also discuss implementations of these approaches and analyze training time complexity associated with each approach. We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data.
Design and evaluation of GaN-based dual-phase interleaved MHz critical mode PFC converter

This paper presents the design consideration and performance evaluation of gallium nitride (GaN) high electron mobility transistor (HEMT) based dual-phase interleaved MHz critical conduction mode (CRM) power factor correction (PFC) converter. A 1.2kW 1-3MHz interleaved boost PFC converter prototype is built with 97.9% peak efficiency and 120W/in3 power density. The significant impact of MHz frequency is demonstrated as dramatically size reduction of boost inductor and electro-magnetic interference (EMI) filter. Several inductor designs are discussed. The corner frequency of EMI filter is pushed to several hundreds of kHz. Finally, the limitation of conventional boost PFC converter is discussed as high conduction loss on diode rectifier bridge and high switching loss caused by valley switching, which is negligible in other low frequency PFC converter but significant in MHz PFC converter. The totem-pole bridgeless PFC converter is introduced to further improve the efficiency with no rectifier bridge and zero-voltage switching (ZVS) extension strategy.
DSP based all-digital resolver-to-digital conversion using DSRF-PLL

Nowadays all-digital resolver-to-digital conversion is popularly invested for space-limited applications. However time delay is inevitable in demodulation part when frequency shifting algorithm used. Furthermore, the angle calculation result is affected by time delay. So the synchronous demodulation is analyzed in this paper to void time delay. The demodulated sine and cosine signals always exist with amplitude and quadrature errors. In order to eliminate the influence of these two errors, the double synchronous reference frame-based phase-locked loop (DSRF-PLL) is investigated in the angle calculation part. DSRF-PLL removes the angular position error caused by the amplitude error and makes the position error caused by the quadrature error to be a constant value, which can be easily compensated in the software. So the angular position can be detected with no time delay and error. The presented all-digital resolver-to-digital conversion scheme is verified by the simulation and experimental results at the end of this paper.
Data Mining Approach For Subscription-Fraud Detection in Telecommunication Sector

This paper implements a probability based method for fraud detection in telecommunication sector. We used Naïve-Bayesian classification to calculate the probability and an adapted version of KL-divergence to identify the fraudulent customers on the basis of subscription. Each user’s data corresponds to one record in the database. Since, the data involves continuous numerical values, the NaïveBayesian classification for continuous values is used. This methodology overcomes the problem of existing system, which classifies the best customer as fraudulent customers, as it works on a threshold based method.
Structure mapping in the comparison process.

Carrying out similarity and analogy comparisons can be modeled as the alignment and mapping of structured representations. In this article we focus on three aspects of comparison that are central in structure-mapping theory. All three are controversial. First, comparison involves structured representations. Second, the comparison process is driven by a preference for connected relational structure. Third, the mapping between domains is rooted in semantic similarity between the relations that characterize the domains. For each of these points, we review supporting evidence and discuss some challenges raised by other researchers. We end with a discussion of the role of structure mapping in other cognitive processes.
Image Retrieval: Content versus Context

In this paper, we introduce a new approach to image retrieval. This new approach takes the best from two worlds, combines image features (content) and words from collateral text (context) into one semantic space. Our approach uses Latent Semantic Indexing, a method that uses co-occurrence statistics to uncover hidden semantics. This paper shows how this method, that has proven successful in both monolingual and cross lingual text retrieval, can be used for multi-modal and cross-modal information retrieval. Experiments with an on-line newspaper archive show that Latent Semantic Indexing can outperform both content based and context based approaches and that it is a promising approach for indexing visual and multi-modal data.
Reconfigurable Terahertz Leaky-Wave Antenna Using Graphene-Based High-Impedance Surface

The concept of graphene-based two-dimensional leaky-wave antenna (LWA), allowing both frequency tuning and beam steering in the terahertz band, is proposed in this paper. In its design, a graphene sheet is used as a tuning part of the high-impedance surface (HIS) that acts as the ground plane of such 2-D LWA. It is shown that, by adjusting the graphene conductivity, the reflection phase of the HIS can be altered effectively, thus controlling the resonant frequency of the 2-D LWA over a broad band. In addition, a flexible adjustment of its pointing direction can be achieved over a wide range, while keeping the operating frequency fixed. Transmission-line methods are used to accurately predict the antenna reconfigurable characteristics, which are further verified by means of commercial full-wave analysis tools.
User Profile Based Personalized Research Paper Recommendation System Using Top-K Query

Researchers spent lots of time in searching published articles relevant to their project. Though having similar interest in projects researches perform individual and time overwhelming searches. But researchers are unable to control the results obtained from earlier search process, whereas they can share the results afterwards. We propose a research paper recommender system by enhancing existing search engines with recommendations based on preceding searches performed by others researchers that avert time absorbing searches. Top-k query algorithm retrieves best answers from a potentially large record set so that we find the most accurate records from the given record set that matches the filtering keywords. KeywordsRecommendation System, Personalization, Profile, Top-k query, Steiner Tree
Pupil Fluctuations Track Fast Switching of Cortical States during Quiet Wakefulness

Neural responses are modulated by brain state, which varies with arousal, attention, and behavior. In mice, running and whisking desynchronize the cortex and enhance sensory responses, but the quiescent periods between bouts of exploratory behaviors have not been well studied. We found that these periods of "quiet wakefulness" were characterized by state fluctuations on a timescale of 1-2 s. Small fluctuations in pupil diameter tracked these state transitions in multiple cortical areas. During dilation, the intracellular membrane potential was desynchronized, sensory responses were enhanced, and population activity was less correlated. In contrast, constriction was characterized by increased low-frequency oscillations and higher ensemble correlations. Specific subtypes of cortical interneurons were differentially activated during dilation and constriction, consistent with their participation in the observed state changes. Pupillometry has been used to index attention and mental effort in humans, but the intracellular dynamics and differences in population activity underlying this phenomenon were previously unknown.
Voxel-Based Assessment of Printability of 3D Shapes

Printability, the capability of a 3D printer to closely reproduce a 3D model, is a complex decision involving several geometrical attributes like local thickness, shape of the thin regions and their surroundings, and topology with respect to thin regions. We present a method for assessment of 3D shape printability which efficiently and effectively computes such attributes. Our method uses a simple and efficient voxel-based representation and associated computations. Using tools from multi-scale morphology and geodesic analysis, we propose several new metrics for various printability problems. We illustrate our method with results taken from a real-life application.
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images

Understanding the nature of dark energy, the mysterious force driving the accelerated expansion of the Universe, is a major challenge of modern cosmology. The next generation of cosmological surveys, specifically designed to address this issue, rely on accurate measurements of the apparent shapes of distant galaxies. However, shape measurement methods suffer from various unavoidable biases and therefore will rely on a precise calibration to meet the accuracy requirements of the science analysis. This calibration process remains an open challenge as it requires large sets of high quality galaxy images. To this end, we study the application of deep conditional generative models in generating realistic galaxy images. In particular we consider variations on conditional variational autoencoder and introduce a new adversarial objective for training of conditional generative networks. Our results suggest a reliable alternative to the acquisition of expensive high quality observations for generating the calibration data needed by the next generation of cosmological surveys. The last two decades have greatly clarified the contents of the Universe, while leaving several large mysteries in our cosmological model. We now have compelling evidence that the expansion rate of the Universe is accelerating, suggesting that the vast majority of the total energy content of the Universe is the so-called dark energy. Yet we lack an understanding of what dark energy actually is, which provides one of the main motivations behind the next generation of cosmological surveys such as LSST LSST Science Collaboration et al. (2009), Euclid Laureijs et al. (2011) and WFIRST Green et al. (2012). These billion dollar projects are specifically designed to shed light on the nature of dark energy by probing the Universe through the weak gravitational lensing effect –i.e., the minute deflection of the light from distant objects by the intervening massive large scale structures of the Universe. On cosmological scales, this lensing effect causes very small but coherent deformations of background galaxy images, which appear slightly sheared, providing a way to statistically map the matter distribution in the Universe. To measure the lensing signal, future surveys will image and measure the shapes of billions of galaxies, significantly driving down statistical errors compared to the current generation of surveys, to the level where dark energy models may become distinguishable. However, the quality of this analysis hinges on the accuracy of the shape measurement algorithms tasked with estimating the ellipticities of the galaxies in the survey. This point is particularly crucial to the success of these missions, as any unaccounted for measurement biases in their ensemble averages would impact the final cosmological analysis and potentially lead to false conclusions. In order to detect and/or calibrate any such biases, future surveys will heavily rely on image simulations, closely mimicking real observations but with a known ground truth lensing signal.
Simulation-Based Algorithms for Markov Decision Processes

Title of Dissertation: Simulation-Based Algorithms for Markov Decision Processes Ying He, Doctor of Philosophy, 2002 Dissertation directed by: Professor Steven I. Marcus Department of Electrical & Computer Engineering Professor Michael C. Fu Department of Decision & Information Technologies Problems of sequential decision making under uncertainty are common in manufacturing, computer and communication systems, and many such problems can be formulated as Markov Decision Processes (MDPs). Motivated by a capacity expansion and allocation problem in semiconductor manufacturing, we formulate a fab-level decision making problem using a finite-horizon transient MDP model that can integrate life cycle dynamics of the fab and provide a trade-off between immediate and future benefits and costs. However, for large and complicated systems formulated as MDPs, the classical methodology to compute optimal policies, dynamic programming, suffers from the so-called “curse of dimensionality” (computational requirement increases exponentially with number of states /controls) and “curse of modeling” (an explicit model for the cost structure and/or the transition probabilities is not available). In problem settings to which our approaches apply, instead of the explicit transition probabilities, outputs are available from either a simulation model or from the actual system. Our methodology is first to find the structure of optimal policies for some special cases, and then to use the structure to construct parameterized heuristic policies for more general cases and implement simulationbased algorithms to determine parameters of the heuristic policies. For the fab-level decision-making problem, we analyze the structure of the optimal policy for a special “one-machine, two-product” case, and discuss the applicability of simulation-based algorithms. We develop several simulation-based algorithms for MDPs to overcome the difficulties of “curse of dimensionality” and “curse of modeling”, considering both theoretical and practical issues. First, we develop a simulation-based policy iteration algorithm for average cost problems under a unichain assumption, relaxing the common recurrent state assumption. Second, for weighted cost problems, we develop a new two-timescale simulation-based gradient algorithms based on perturbation analysis, provide a theoretical convergence proof, and compare it with two recently proposed simulation-based gradient algorithms. Third, we propose two new Simultaneous Perturbation Stochastic Approximation (SPSA) algorithms for weighted cost problems and verify their effectiveness via simulation; then, we consider a general SPSA algorithm for function minimization and show its convergence under a weaker assumption: the function does not have to be differentiable. To Yingjiu and my parents ...
Modeling and Adaptive Control of an Omni-Mecanum-Wheeled Robot

The complete dynamics model of a four-Mecanum-wheeled robot considering mass eccentricity and friction uncertainty is derived using the Lagrange’s equation. Then based on the dynamics model, a nonlinear stable adaptive control law is derived using the backstepping method via Lyapunov stability theory. In order to compensate for the model uncertainty, a nonlinear damping term is included in the control law, and the parameter update law with σ-modification is considered for the uncertainty estimation. Computer simulations are conducted to illustrate the suggested control approach.
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales. The dialogue manager consists of: (1) a top-level dialogue policy that selects among subtasks or options, (2) a low-level dialogue policy that selects primitive actions to complete the subtask given by the top-level policy, and (3) a global state tracker that helps ensure all cross-subtask constraints be satisfied. Experiments on a travel planning task with simulated and real users show that our approach leads to significant improvements over three baselines, two based on handcrafted rules and the other based on flat deep reinforcement learning.
Predicting the popularity of instagram posts for a lifestyle magazine using deep learning

In this paper we use a Deep Neural Network (DNN) trained on data collected from the visual media-sharing social platform Instagram account of a popular Indian lifestyle magazine to predict the popularity of future posts. This predicted popularity of the post can be used to decide advertising rates and measure performance metrics important for publishing strategy decisions. The DNN primarily uses growth rate in subscriber base, tags associated with the post, time of day when the post was made, day of the week, color descriptors of the image, time between current and previous post, popularity of previous post as features for prediction. This covers majority of the causes of variation in popularity. Mini-batch gradient descend method is used to learn the weights in DNN and the objective function is cross-entropy. The network performs acceptable for real world applications and tolerances are within acceptable limits for the application.
Mindful emotion regulation: An integrative review.

This review aims to integrate the constructs of mindfulness and emotion regulation. Research into both of these areas is relatively new, and while several reviews have emerged for each area independently, none has directly proposed a conceptual integration. The current review explores how key axioms and assumptions of traditional psychological models of emotion regulation and the psychological interventions that are derived from them (e.g., cognitive behavior therapy) differ fundamentally from mindfulness-based approaches in terms of the underlying processes they address. Accordingly, mindfulness and emotion regulation are each reviewed, followed by a conceptual integration. Fundamental difficulties arising from the attempt to integrate the two domains are highlighted, especially as to the "reality" of thoughts, the relationship between thoughts and emotions, and the need to move beyond a valence model of emotion. Finally, a model is proposed outlining the likely critical processes and mechanisms that underlie "mindful emotion regulation."
Noninvasive cardiac output estimation using a novel photoplethysmogram index

Cardiac output (CO) monitoring is essential for indicating the perfusion status of the human cardiovascular system under different physiological conditions. However, it is currently limited to hospital use due to the need for either skilled operators or big, expensive measurement devices. Therefore, in this paper we devise a new CO indicator which can easily be incorporated into existing wearable devices. To this end, we propose an index, the inflection and harmonic area ratio (IHAR), from standard photoplethysmographic (PPG) signals, which can be used to continuously monitor CO. We evaluate the success of our index by testing on sixteen normotensive subjects before and after bicycle exercise. The results showed a strong intra-subject correlation between IHAR and COimp measured by the bio-impedance method in fifteen subjects (mean r = 3D 0.82, p<0.01). After least squares linear regression, the precision between COimp and CO estimated from IHAR (COIHAR) was 1.40 L/min. The total percentage error of the results was 16.2%, which was well below the clinical acceptance limit of 30%. The results suggest that IHAR is a promising indicator for wearable and noninvasive CO monitoring.