nils-herrmann/clean_scirepeval huggingface dataset:
Distance functions on digital pictures

This paper describes algori thms for comput ing various functions on a digital picture which depend on the distance to a given subset of the picture. The algorithms involve local operations which are performed repeatedly, "in parallel", on every picture element and its immediate neighbors. Applications to the detection of "clusters" and "regularities" in a picture, and to the dissection of a region into "'pieces", are also described. 1. DISTANCE F U N C T I O N S LET A be the set of all pairs of integers (i,j). The func t ion f f rom A x A into the nonnegative integers is called (a) Positive definite, if f (x, y) = 0 if and only if x = y. (b) Symmetric, if f (x, y) = f(y, x) for all x, y in A. (c) Triangular, i ff(x, z) < f(x, y)+f(y, z) for all x, y, z in A. If f satisfies (a--c), it is called a distance function. Proposition 1. If l a n d g satisfy (a), so do f + g and m a x ( f g); similarly for (b) and (c).  Le t f ( x , y )+g(x ,y ) = 0; then s i n c e f > 0 and g > 0, we must have f (x ,y ) = g ( x , y ) = 0 , so that x = y . Clearly f ( y , x ) + g ( y , x ) = f ( x , y ) + g ( x , y ) , while f ( x , z )+ g(x, z) < f (x , y) + f(y , z) + g(x, y) + g(y, z) = f (x , y) + g(x, y) + f (y , z) + g(y, z). The proofs for max(f, g) are analogous. COROLLARY. If f l . . . . . f , satisfy (a), (b) or (c), so d o f t + . . . +fn and max(f l . . . . . f,). Proposition 2. f[(i, j), (h, k)] = l i hL and g[(i, j), (h, k)] = I J kl are distance functions.  (a)-(b) are clear; as for (c), note that f[(m, n), (p, q)] = f [ (m+r, n), (p+r, q)] for any m, so that it suffices to prove f[(i, jk(h,k)] <fE(i,j),(O,O)]+f[(h,k),(O,O)], i.e. l i -hl <<_ Iil +lhl, which is a well known property of absolute value, and similarly for g. COROLLARY. dl[(i,j), (h, k)] = l i -hl +l j k [ and d2[(i,j), (h, k)] = max( l i -h l , I j k [ ) are distance functions. (d I is sometimes called city block distance.) Proposition 3. The set of (i,j) at distance < r from (0, 0) is: (a) For d l : A diamond (i.e. a square with sides inclined at +45 °) with vertices at (0, _ r) and (+ r, 0) (b) For d2 : A square (with sides horizontal and vertical) with vertices at (r, + r ) and ( r , _+r). Proof." (a) This is the set of (i,j) such that li[ +lJl -< rz thus ]Jl -< r, and for any such j we h a v e l j ] r < i < r l j l .
Video super-resolution based on spatial-temporal recurrent residual networks

In this paper, we propose a new video Super-Resolution (SR) method by jointly modeling intra-frame redundancy and inter-frame motion context in a unified deep network. Different from conventional methods, the proposed Spatial-Temporal Recurrent Residual Network (STR-ResNet) investigates both spatial and temporal residues, which are represented by the difference between a high resolution (HR) frame and its corresponding low resolution (LR) frame and the difference between adjacent HR frames, respectively. This spatial-temporal residual learning model is then utilized to connect the intra-frame and inter-frame redundancies within video sequences in a recurrent convolutional network and to predict HR temporal residues in the penultimate layer as guidance to benefit estimating the spatial residue for video SR. Extensive experiments have demonstrated that the proposed STR-ResNet is able to efficiently reconstruct videos with diversified contents and complex motions, which outperforms the existing video SR approaches and offers new state-of-the-art performances on benchmark datasets.
Chisel: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially Hashed Signed Distance Fields

We describe CHISEL: a system for real-time housescale (300 square meter or more) dense 3D reconstruction onboard a Google Tango  mobile device by using a dynamic spatially-hashed truncated signed distance field[2] for mapping, and visual-inertial odometry for localization. By aggressively culling parts of the scene that do not contain surfaces, we avoid needless computation and wasted memory. Even under very noisy conditions, we produce high-quality reconstructions through the use of space carving. We are able to reconstruct and render very large scenes at a resolution of 2-3 cm in real time on a mobile device without the use of GPU computing. The user is able to view and interact with the reconstruction in real-time through an intuitive interface. We provide both qualitative and quantitative results on publicly available RGB-D datasets , and on datasets collected in real-time from two devices.
Blood pressure estimation from pulse wave velocity measured on the chest

Recently, monitoring of blood pressure fluctuation in the daily life is focused on in the hypertension care area to predict the risk of cardiovascular and cerebrovascular disease events. In this paper, in order to propose an alternative system to the existed ambulatory blood pressure monitoring (ABPM) sphygmomanometer, we have developed a prototype of small wearable device consisting of electrocardiogram (ECG) and photopelthysmograph (PPG) sensors. In addition, it was examined whether blood pressure can be estimated based on pulse wave transit time (PWTT) only by attaching that device on the surface of the chest. We indicated that our system could also sense tendency of time-dependent change of blood pressure by measuring pulse of vessel over the sternum while its propagation distance is short.
COMPENDIUM : a text summarisation tool for generating summaries of multiple purposes , domains , and genres

In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, singleand multi-document summaries can be produced; as the output, the summaries can be extractive or abstractiveoriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.
Coarse-to-Fine Question Answering for Long Documents

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-ofthe-art models. While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences. Inspired by how people first skim the document, identify relevant parts, and carefully read these parts to produce an answer, we combine a coarse, fast model for selecting relevant sentences and a more expensive RNN for producing the answer from those sentences. We treat sentence selection as a latent variable trained jointly from the answer only using reinforcement learning. Experiments demonstrate the state of the art performance on a challenging subset of the WIKIREADING dataset (Hewlett et al., 2016) and on a new dataset, while speeding up the model by 3.5x-6.7x.
A robust, input voltage adaptive and low energy consumption level converter for sub-threshold logic

A new level converter (LC) is proposed for logic voltage shifting between sub-threshold voltage to normal high voltage. By employing 2 PMOS diodes, the LC shows good operation robustness with sub-threshold logic input. The switching delay of the proposed LC can adapt with the input logic voltage which is more suitable for power aware systems. With a simpler circuit structure, the energy consumption of the LC is smaller than that of the existing sub-threshold LC. Simulation results demonstrate the performance improvement and energy reduction of the proposed LC. Test chip was fabricated using 0.18 mum CMOS process. Measurement results show that our proposed LC can operate correctly with an input at as low as 127 mV and an output voltage at 1.8V.
Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor".



This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record.



The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs.



Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.
Microstrip to Parallel Strip Balun as Spiral Antenna Feed

This paper presents the design and implementation of a microstrip to parallel strip balun which are frequently used as balanced antennas feed. This wideband balun transition is composed of a parallel strip which is connected to the spiral antenna and a microstrip line where the width of the ground plane is gradually reduced to eventually resemble the parallel strip. The taper accomplishes the mode and impedance transformation. This balun has significantly improved bandwidth characteristics. The entire circuit was fabricated on RT Duriod 5880 substrate. The circuit designs were simulated and optimised using CST Microwave Studio and the simulated results are compared with the measured results. The back-to-back microstrip to parallel strip has a return loss of better than 10 dB over a wide bandwidth from 1.75 to 15 GHz. The performance of the proposed balun was validated with the spiral antenna. The measured results were compared with the simulated results and it shows that the antenna operates well in wideband frequency range from 2.5 to 15 GHz.
Anti-steganalysis for image on convolutional neural networks

Nowadays, convolutional neural network (CNN) based steganalysis methods achieved great performance. While those methods are also facing security problems. In this paper, we proposed an attack scheme aiming at CNN based steganalyzer including two different attack methods 1) the LSB-Jstego Gradient Based Attack; 2) LSB-Jstego Evolutionary Algorithms Based Attack. The experiment results show that the attack strategies could achieve 96.02% and 90.25% success ratio separately on the target CNN. The proposed attack scheme is an effective way to fool the CNN based steganalyzer and in addition demonstrates the vulnerability of the neural networks in steganalysis.
Designing social presence of social actors in human computer interaction

This study examines the interaction effect between user factors and media factors on feelings of social presence which are critical in the design of virtual reality systems and human computer interfaces. Both Experiment 1 and Experiment 2 show that matching synthesized voice personality to user personality positively affects users' (especially extrovert users') feelings of social presence. Experiment 2 also reveals that users feel a stronger sense of social presence when the personality of synthesized voice matches the personality of textual content than when those two are mismatched. In both experiments, extrovert voice induces a stronger sense of presence than introvert voice. These results provide strong evidence for human's automatic social responses to artificial representations possessing humanistic properties such as language and personality. Finally, we discuss various applications of these findings in the design of human computer interfaces, as well as in the study of presence.
On Measures of Entropy and Information

2 Mutual information 3 Mutual information . . . . . . . . . . . . . . . . . 3 Multivariate mutual information . . . . . . . . . 4 Interaction information . . . . . . . . . . . . . . . 5 Conditional mutual information . . . . . . . . . 5 Binding information . . . . . . . . . . . . . . . . 6 Residual entropy . . . . . . . . . . . . . . . . . . 6 Total correlation . . . . . . . . . . . . . . . . . . . 6 Lautum information . . . . . . . . . . . . . . . . 6 Uncertainty coefficient . . . . . . . . . . . . . . .
Fabrication of 2D-extruded fractal structures using repeated corner lithography and etching

We present a new wafer scale fabrication procedure for the creation of complex 2D-extruded geometries in silicon by a combination of repeated anisotropic etching of silicon and a 3D lithographic technique called corner lithography. Using corner lithography it is possible, by means of a nano-masking step, to select which corners are opened, depending on specific angles relative to the substrate normal. In this way complex 3D structures can be created in a repetitive manner. In this article we apply this procedure to the etching of trench like structures in a <;100> silicon wafer. Depending on which angles open in corner lithography, it is possible to etch just downward, downward and sideward, or a sequential combination of these two options. Examples of all three routes have been created on a micron/sub-micron scale. By depositing a conformal layer, the smallest hollow features will be auto-closed, thus forming buried channels. Continuing this deposition, automatically new closed channels will be formed with increasing diameter and wall thickness. These channel structures could find application in e.g. continuous flow microreactors.
Towards Automated Cost-Efficient Data Management for Federated Cloud Services

Cloud computing has transformed the accessibility and usage of information technology resources, by offering them as services via the Internet. Cloud service provisioning spans across infrastructure, platform and software levels. The management of the services at these levels is based on monitoring. The analysis of monitoring data provides insight into Cloud operations in order to make informed decisions. Due to the emergence of numerous heterogenous Cloud platforms with proprietary APIs, service and monitoring data are being formatted using diverse and mostly incompatible data interchange formats. This results to interoperability issues and makes the analysis of monitoring data from multi-Cloud service deployments difficult to handle. The existing research efforts on data interchange formats have been mainly focused on general performance analyses. Little or no effort has been channelled towards a combination of multiple data interchange formats based on data type to achieve efficient serialisation that can facilitate interoperability in federated Clouds, and also reduce the size of data and bandwidth utilisation cost. This paper addresses these issues by presenting automated framework that is capable of automatically selecting the most suitable data interchange formats for achieving an efficient formatting and serialisation outcome. The goal of the framework is to enable robust and transparent communication within and between multiple Cloud deployments. Based on three use case scenarios, we evaluate the proposed framework to demonstrate its efficacy in formatting and serialising data.
An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction

Data mining techniques have been applied magnificently in many fields including business, science, the Web, cheminformatics, bioinformatics, and on different types of data such as textual, visual, spatial, real-time and sensor data. Medical data is still information rich but knowledge poor. There is a lack of effective analysis tools to discover the hidden relationships and trends in medical data obtained from clinical records. This paper reviews the stateof-the-art research on heart disease diagnosis and prediction. Specifically in this paper, we present an overview of the current research being carried out using the data mining techniques to enhance heart disease diagnosis and prediction including decision trees, Naive Bayes classifiers, K-nearest neighbour classification (KNN), support vector machine (SVM), and artificial neural networks techniques. Results show that SVM and neural networks perform positively high to predict the presence of coronary heart diseases (CHD). Decision trees after features reduction is the best recommended classifier to diagnose cardiovascular disease (CVD). Still the performance of data mining techniques to detect coronary arteries diseases (CAD) is not encouraging (between 60%-75%) and further improvements should be pursued.
Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News

Fake news are nowadays an issue of pressing concern, given their recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in early 2017 to encourage the development of machine learning-based classification systems for stance detection (i.e., for identifying whether a particular news article agrees, disagrees, discusses, or is unrelated to a particular news headline), thus helping in the detection and analysis of possible instances of fake news. This article presents a novel approach to tackle this stance detection problem, based on the combination of string similarity features with a deep neural network architecture that leverages ideas previously advanced in the context of learning efficient text representations, document classification, and natural language inference. Specifically, we use bi-directional Recurrent Neural Networks (RNNs), together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article. These representations are then combined/compared, complemented with similarity features inspired on other FNC-1 approaches, and passed to a final layer that predicts the stance of the article towards the headline. We also explore the use of external sources of information, specifically large datasets of sentence pairs originally proposed for training and evaluating natural language inference methods, in order to pre-train specific components of the neural network architecture (e.g., the RNNs used for encoding sentences). The obtained results attest to the effectiveness of the proposed ideas and show that our model, particularly when considering pre-training and the combination of neural representations together with similarity features, slightly outperforms the previous state-of-the-art.
Parallel Database Recovery for Multicore Main-Memory Databases

Main-memory database systems for multicore servers can ach ieve excellent performance when processing massive volume of OL TP workloads. But crash-resilience mechanism, or namely logg ingand-replay, can either degrade the performance of transact ion processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it i s possible to achieve speedy failure recovery without introduci ng any costly logging overhead to the execution of online transact ions. We propose TARS, a parallel log-recovery mechanism that is specifically designed for lightweight, coarse-grained commandlogging approach. TARS leverages a combination of static and dynamic analyses to parallelize the log replay: at compile time, T ARS decomposes stored procedures by carefully analyzing depende ncies within and across programs; at recovery time, a re-executio n schedule with a high degree of parallelism is attained through lig tweight one-pass scans over transaction parameter values. As such, recovery latency is remarkably minimized. We evaluated T ARS in a main-memory database prototype running on a 40-core serve r. Compared to the state-of-the-art mechanisms, T ARS yields significantly higher recovery rate without compromising the effici en y of transaction processing.
Deep keyphrase generation with a convolutional sequence to sequence model

Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. Most previous studies realize the automatic keyphrase extraction through dividing the source text into multiple chunks and then rank and select the most suitable ones. These approaches ignore the deep semantics behind the text and could not predict the keyphrases not appearing in the source text. A sequence to sequence model to generate keyphrases from vocabulary could solve the issues above. However, traditional sequence to sequence model based on recurrent neural network(RNN) suffers from low efficiency problem. We propose an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations can be completely parallelized over all elements so as to better exploit the GPU hardware. Our use of gated linear units alleviates gradient propagation and we equip each decoder layer with a separate attention model. Moreover, we incorporate a copying mechanism to handle out-of-vocabulary phrases. In experiments, we evaluate our model on six datasets, and our proposed model is demonstrated to outperform state-of-the-art baseline models consistently and significantly, both on extracting the keyphrases existing in the source text and generating the absent keyphrases based on the sematic meaning of the text.
Question Answering on SQuAD

In this project, we exploit several deep learning architectures in Question Answering field, based on the newly released Stanford Question Answering dataset (SQuAD)[7]. We introduce a multi-stage process that encodes context paragraphs at different levels of granularity, uses co-attention mechanism to fuse representations of questions and context paragraphs, and finally decodes the co-attention vectors to get the answers. Our best model gets 62.23% F1 score and 48.72% EM score on the test set.