nils-herrmann/clean_scirepeval huggingface dataset:
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms – such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) – requires significant manual effort. We propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-ofthe-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs. We also demonstrate TVM’s ability to target new accelerator back-ends, such as the FPGA-based generic deep learning accelerator. The system is open sourced and in production use inside several major companies.
Query optimization in microsoft SQL server PDW

In recent years, Massively Parallel Processors have increasingly been used to manage and query vast amounts of data. Dramatic performance improvements are achieved through distributed execution of queries across many nodes. Query optimization for such system is a challenging and important problem.
 In this paper we describe the Query Optimizer inside the SQL Server Parallel Data Warehouse product (PDW QO). We leverage existing QO technology in Microsoft SQL Server to implement a cost-based optimizer for distributed query execution. By properly abstracting metadata we can readily reuse existing logic for query simplification, space exploration and cardinality estimation. Unlike earlier approaches that simply parallelize the best serial plan, our optimizer considers a rich space of execution alternatives, and picks one based on a cost-model for the distributed execution environment. The result is a high-quality, effective query optimizer for distributed query processing in an MPP.
Taper structures for coupling into photonic crystal slab waveguides

We present an adiabatic taper design in three dimensions for coupling light into photonic crystal defect waveguides in a square lattice of circular dielectric rods. The taper is a two-stage structure in which the first stage makes the transition from a dielectric waveguide to a coupled-cavity waveguide. The second stage subsequently transforms the waveguide mode from an index-guided mode to a band-gap-guided mode. We discuss differences between the two-dimensional device and its three-dimensional slab version.
Patterns of Intelligent and Mobile Agents

Patterns of Intelligent and Mobile Agents Elizabeth A.Kendall, P.V. Murali Krishna, Chirag V. Pathak, C:B. Suresh Computer Systems Engineering, Royal Melbourne Institute Of Technology City Campus, GPO Box 2476V, Melbourne, VIC 3001 AUSTRALIA email : kendall@rmit.edu.au 1.  Agent systems must have foundation; one approach that successfully applied to other so&are is patterns. This paper collection of patterns for agents. 2.  Almost all agent development to date has a strong has been kinds of presents a
Anomaly-based bug prediction, isolation, and validation: an automated approach for software debugging

Software defects, commonly known as bugs, present a serious challenge for system reliability and dependability. Once a program failure is observed, the debugging activities to locate the defects are typically nontrivial and time consuming. In this paper, we propose a novel automated approach to pin-point the root-causes of software failures.
 Our proposed approach consists of three steps. The first step is bug prediction, which leverages the existing work on anomaly-based bug detection as exceptional behavior during program execution has been shown to frequently point to the root cause of a software failure. The second step is bug isolation, which eliminates false-positive bug predictions by checking whether the dynamic forward slices of bug predictions lead to the observed program failure. The last step is bug validation, in which the isolated anomalies are validated by dynamically nullifying their effects and observing if the program still fails. The whole bug prediction, isolation and validation process is fully automated and can be implemented with efficient architectural support. Our experiments with 6 programs and 7 bugs, including a real bug in the gcc 2.95.2 compiler, show that our approach is highly effective at isolating only the relevant anomalies. Compared to state-of-art debugging techniques, our proposed approach pinpoints the defect locations more accurately and presents the user with a much smaller code set to analyze.
Enhancing Light Extraction of Inorganic Scintillators Using Photonic Crystals

Inorganic scintillators are commonly used as sensors for ionizing radiation detectors in a variety of applications, ranging from particle and nuclear physics detectors, medical imaging, nuclear installations radiation control, homeland security, well oil logging and a number of industrial non-destructive investigations. For all these applications, the scintillation light produced by the energy deposited in the scintillator allows the determination of the position, the energy and the time of the event. However, the performance of these detectors is often limited by the amount of light collected on the photodetector. A major limitation comes from the fact that inorganic scintillators are generally characterized by a high refractive index, as a consequence of the required high density to provide the necessary stopping power for ionizing radiation. The index mismatch between the crystal and the surrounding medium (air or optical grease) strongly limits the light extraction efficiency because of total internal reflection (TIR), increasing the travel path and the absorption probability through multiple bouncings of the photons in the crystal. Photonic crystals can overcome this problem and produce a controllable index matching between the crystal and the output medium through an interface made of a thin nano-structured layer of optically-transparent high index material. This review presents a summary of the works aiming at improving the light collection efficiency of scintillators using photonic crystals since this idea was introduced 10 years ago.
Conceptual design of flapping-wing micro air vehicles.

Traditional micro air vehicles (MAVs) are miniature versions of full-scale aircraft from which their design principles closely follow. The first step in aircraft design is the development of a conceptual design, where basic specifications and vehicle size are established. Conceptual design methods do not rely on specific knowledge of the propulsion system, vehicle layout and subsystems; these details are addressed later in the design process. Non-traditional MAV designs based on birds or insects are less common and without well-established conceptual design methods. This paper presents a conceptual design process for hovering flapping-wing vehicles. An energy-based accounting of propulsion and aerodynamics is combined with a one degree-of-freedom dynamic flapping model. Important results include simple analytical expressions for flight endurance and range, predictions for maximum feasible wing size and body mass, and critical design space restrictions resulting from finite wing inertia. A new figure-of-merit for wing structural-inertial efficiency is proposed and used to quantify the performance of real and artificial insect wings. The impact of these results on future flapping-wing MAV designs is discussed in detail.
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases

Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that combine logic with probabilities. One prominent and highly expressive SRL model is Markov Logic Networks (MLNs), but the expressivity comes at the cost of learning complexity. Most of the current methods for learning MLN structure follow a two-step approach where first they search through the space of possible clauses (i.e. structures) and then learn weights via gradient descent for these clauses. We present a functional-gradient boosting algorithm to learn both the weights (in closed form) and the structure of the MLN simultaneously. Moreover most of the learning approaches for SRL apply the closed-world assumption, i.e., whatever is not observed is assumed to be false in the world. We attempt to open this assumption. We extend our algorithm for MLN structure learning to handle missing data by using an EM-based approach and show this algorithm can also be used to learn Relational Dependency Networks and relational policies. Our results in many domains demonstrate that our approach can effectively learn MLNs even in the presence of missing data.
A report on single exon genes (SEG) in eukaryotes.

Single exon genes (SEG) are archetypical of prokaryotes. Hence, their presence in intron-rich, multi-cellular eukaryotic genomes is perplexing. Consequently, a study on SEG origin and evolution is important. Towards this goal, we took the first initiative of identifying and counting SEG in nine completely sequenced eukaryotic organisms--four of which are unicellular (E. cuniculi, S. cerevisiae, S. pombe, P. falciparum) and five of which are multi-cellular (C. elegans, A. thaliana, D. melanogaster, M. musculus, H. sapiens). This exercise enabled us to compare their proportion in unicellular and multi-cellular genomes. The comparison suggests that the SEG fraction decreases with gene count (r = -0.80) and increases with gene density (r = 0.88) in these genomes. We also examined the distribution patterns of their protein lengths in different genomes.
Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns.
Freedom for bibliographic references: OpenCitations arise

Scholarly citations from one publication to another, expressed as reference lists within academic articles, are core elements of scholarly communication. Unfortunately, they usually can be accessed en masse only by paying significant subscription fees to commercial organizations, while those few services that do made them available for free impose strict limitations on their reuse. In this paper we provide an overview of the OpenCitations Project (http://opencitations.net) undertaken to remedy this situation, and of its main product, the OpenCitations Corpus, which is an open repository of accurate bibliographic citation data harvested from the scholarly literature, made available in RDF under a Creative Commons public domain dedication.
A 13-bit 200MS/s PIPELINE ADC in 0.13µm CMOS

This paper presents a 0.13μm SHA-less pipeline ADC with LMS calibration technique. The nonlinearity of the first three stages is calibrated with blind LMS algorithm. Opamps and switches are carefully considered and co-designed with the calibration system. Around 7LSB closed-loop nonlinearity of MDAC is achieved. Simulation shows the SNDR of the proposed ADC at 200MS/s sampling rate is 78dB with 3.13MHz input and 75dB with 83.13MHz input.
An Empirical Study of Bug Fixing Rate

Bug fixing is one of the most important activities in software development and maintenance. A software project often employs an issue tracking system such as Bugzilla to store and manage their bugs. In the issue tracking system, many bugs are invalid but take unnecessary efforts to identify them. In this paper, we mainly focus on bug fixing rate, i.e., The proportion of the fixed bugs in the reported closed bugs. In particular, we study the characteristics of bug fixing rate and investigate the impact of a reporter's different contribution behaviors to the bug fixing rate. We perform an empirical study on all reported bugs of two large open source software communities Eclipse and Mozilla. We find (1) the bug fixing rates of both projects are not high, (2) there exhibits a negative correlation between a reporter's bug fixing rate and the average time cost to close the bugs he/she reports, (3) the amount of bugs a reporter ever fixed has a strong positive impact on his/her bug fixing rate, (4) reporters' bug fixing rates have no big difference, whether their contribution behaviors concentrate on a few products or across many products, (5) reporters' bug fixing rates tend to increase as time goes on, i.e., Developers become more experienced at reporting bugs.
Trypanosoma cruzi: adaptation to its vectors and its hosts

American trypanosomiasis is a parasitic zoonosis that occurs throughout Latin America. The etiological agent, Trypanosoma cruzi, is able to infect almost all tissues of its mammalian hosts and spreads in the environment in multifarious transmission cycles that may or not be connected. This biological plasticity, which is probably the result of the considerable heterogeneity of the taxon, exemplifies a successful adaptation of a parasite resulting in distinct outcomes of infection and a complex epidemiological pattern. In the 1990s, most endemic countries strengthened national control programs to interrupt the transmission of this parasite to humans. However, many obstacles remain to the effective control of the disease. Current knowledge of the different components involved in elaborate system that is American trypanosomiasis (the protozoan parasite T. cruzi, vectors Triatominae and the many reservoirs of infection), as well as the interactions existing within the system, is still incomplete. The Triatominae probably evolve from predatory reduvids in response to the availability of vertebrate food source. However, the basic mechanisms of adaptation of some of them to artificial ecotopes remain poorly understood. Nevertheless, these adaptations seem to be associated with a behavioral plasticity, a reduction in the genetic repertoire and increasing developmental instability.
Exploratory Analysis of Social Media Prior to a Suicide Attempt

Tragically, an estimated 42,000 Americans died by suicide in 2015, each one deeply affecting friends and family. Very little data and information is available about people who attempt to take their life, and thus scientific exploration has been hampered. We examine data from Twitter users who have attempted to take their life and provide an exploratory analysis of patterns in language and emotions around their attempt. We also show differences between those who have attempted to take their life and matched controls. We find quantifiable signals of suicide attempts in the language of social media data and estimate performance of a simple machine learning classifier with these signals as a non-invasive analysis in a screening process.
Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment

Heatmap regression has been used for landmark localization for quite a while now. Most of the methods use a very deep stack of bottleneck modules for heatmap classification stage, followed by heatmap regression to extract the keypoints. In this paper, we present a single dendritic CNN, termed as Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN), where a classification network is followed by a second and modular classification network, trained in an end to end fashion to obtain accurate landmark points. Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. Extensive experimentation shows that conditioning on pose reduces the localization error by making it agnostic to face pose. The proposed model can be extended to yield variable number of landmark points and hence broadening its applicability to other datasets. Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto 15% reduction in error compared to state-of-the-art methods for extreme and medium pose face images from challenging datasets including AFLW, AFW, COFW and IBUG.
Predicting Latent Structured Intents from Shopping Queries

In online shopping, users usually express their intent through search queries. However, these queries are often ambiguous. For example, it is more likely (and easier) for users to write a query like “high-end bike” than “21 speed carbon frames jamis or giant road bike”. It is challenging to interpret these ambiguous queries and thus search result accuracy suffers. A user oftentimes needs to go through the frustrating process of refining search queries or self-teaching from possibly unstructured information. However, shopping is indeed a structured domain, that is composed of category hierarchy, brands, product lines, features, etc. It would be much better if a shopping site could understand users’ intent through this structure, present organized information, and then find the items with the right categories, brands or features. In this paper we study the problem of inferring the latent intent from unstructured queries and mapping them to structured attributes. We present a novel framework that jointly learns this knowledge from user consumption behaviors and product metadata. We present a hybrid Long Shortterm Memory (LSTM)  joint model that is accurate and robust, even though user queries are noisy and product catalog is rapidly growing. Our study is conducted on a largescale dataset from Google Shopping, that is composed of millions of items and user queries along with their click responses. Extensive qualitative and quantitative evaluation shows that the proposed model is more accurate, concise, and robust than multiple possible alternatives. In terms of information retrieval (IR) performance, our model is able to improve the quality of current Google Shopping production system, which is a very strong baseline.
Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications

Due to the popularity of smart mobile phones and context-aware technology, various contextual data relevant to users’ diverse activities with mobile phones is available around us. This enables the study on mobile phone data and context-awareness in computing, for the purpose of building data-driven intelligent mobile applications, not only on a single device but also in a distributed environment for the benefit of end users. Based on the availability of mobile phone data, and the usefulness of data-driven applications, in this paper, we discuss about mobile data science that involves in collecting the mobile phone data from various sources and building data-driven models using machine learning techniques, in order to make dynamic decisions intelligently in various day-to-day situations of the users. For this, we first discuss the fundamental concepts and the potentiality of mobile data science to build intelligent applications. We also highlight the key elements and explain various key modules involving in the process of mobile data science. This article is the first in the field to draw a big picture, and thinking about mobile data science, and it’s potentiality in developing various data-driven intelligent mobile applications. We believe this study will help both the researchers and application developers for building smart data-driven mobile applications, to assist the end mobile phone users in their daily activities. Received on 20 August 2018; accepted on 15 October 2018; published on 07 November 2018
Motion saliency outweighs other low-level features while watching videos

The importance of motion in attracting attention is well known. While watching videos, where motion is prevalent, how do we quantify the regions that are motion salient? In this paper, we investigate the role of motion in attention and compare it with the influence of other low-level features like image orientation and intensity. We propose a framework for motion saliency. In particular, we integrate motion vector information with spatial and temporal coherency to generate a motion attention map. The results show that our model achieves good performance in identifying regions that are moving and salient. We also find motion to have greater influence on saliency than other low-level features when watching videos.
Temperature dependence of the charge carrier mobility in disordered organic semiconductors at large carrier concentrations

I. I. Fishchuk,1 A. K. Kadashchuk,2,3 J. Genoe,2 Mujeeb Ullah,4 H. Sitter,4 Th. B. Singh,5 N. S. Sariciftci,6 and H. Bässler7 1Institute for Nuclear Research, National Academy of Sciences of Ukraine, Prospect Nauky 47, 03680 Kyiv, Ukraine 2IMEC, Kapeldreef 75, Heverlee, B-3001 Leuven, Belgium 3Institute of Physics, National Academy of Sciences of Ukraine, Prospect Nauky 46, 03028 Kyiv, Ukraine 4Institute of Semiconductor & Solid State Physics, Johannes Kepler University of Linz, A-4040 Linz, Austria 5Molecular and Health Technologies, CSIRO, Bayview Avenue Clayton, Victoria 3168, Australia 6Linz Institute for Organic Solar Cells (LIOS), Johannes Kepler University of Linz, A-4040 Linz, Austria 7Chemistry Department, Philipps-Universität Marburg, Hans-Meerwein-Strasse, D-35032 Marburg, Germany Received 18 May 2009; revised manuscript received 31 August 2009;
A novel speaker clustering algorithm via supervised affinity propagation

This paper addresses the problem of speaker clustering in telephone conversations. Recently, a new clustering algorithm named affinity propagation (AP) is proposed. It exhibits fast execution speed and finds clusters with low error. However, AP is an unsupervised approach which may make the resulting number of clusters different from the actual one. This deteriorates the speaker purity dramatically. This paper proposes a modified method named supervised affinity propagation (SAP), which automatically reruns the AP procedure to make the final number of clusters converge to the specified number. Experiments are carried out to compare SAP with traditional k-means and agglomerative hierarchical clustering on 4-hour summed channel conversations in the NIST 2004 Speaker Recognition Evaluation. Experiment results show that the SAP method leads to a noticeable speaker purity improvement with slight cluster purity decrease compared with AP.