nils-herrmann/clean_scirepeval huggingface dataset:
Millimeter Wave Communications for Future Mobile Networks

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user. Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.
WaterCooler: exploring an organization through enterprise social media

As organizations scale up, their collective knowledge increases, and the potential for serendipitous collaboration between members grows dramatically. However, finding people with the right expertise or interests becomes much more difficult. Semi-structured social media, such as blogs, forums, and bookmarking, present a viable platform for collaboration-if enough people participate, and if shared content is easily findable. Within the trusted confines of an organization, users can trade anonymity for a rich identity that carries information about their role, location, and position in its hierarchy.
 This paper describes WaterCooler, a tool that aggregates shared internal social media and cross-references it with an organization's directory. We deployed WaterCooler in a large global enterprise and present the results of a preliminary user study. Despite the lack of complete social networking affordances, we find that WaterCooler changed users' perceptions of their workplace, made them feel more connected to each other and the company, and redistributed users' attention outside their own business groups.
Autonomous drifting using simulation-aided reinforcement learning

We introduce a framework that combines simple and complex continuous state-action simulators with a real-world robot to efficiently find good control policies, while minimizing the number of samples needed from the physical robot. The framework combines the strengths of various simulation levels by first finding optimal policies in a simple model, and then using that solution to initialize a gradient-based learner in a more complex simulation. The policy and transition dynamics from the complex simulation are in turn used to guide the learning in the physical world. A method is developed for transferring information gathered in the physical world back to the learning agent in the simulation. The new information is used to re-evaluate whether the original simulated policy is still optimal given the updated knowledge from the real-world. This reverse transfer is critical to minimizing samples from the physical world. The new framework is demonstrated on a robotic car learning to perform controlled drifting maneuvers. A video of the car's performance can be found at https: //youtu.be/opsmd5yuBF0.
Individual Tree Delineation in Windbreaks Using Airborne-Laser-Scanning Data and Unmanned Aerial Vehicle Stereo Images

This letter is aimed to compare the performance of canopy height models (CHMs) derived from airborne laser scanning (ALS) data and unmanned aerial vehicle (UAV) stereo images in the extraction of individual tree height and crown size. Treetops were identified using the local maximum algorithm from the Gaussian filtered CHMs. A parabola fitting was used to determine the crown size. Factors affecting the delineation results, such as point cloud density and the spatial distribution and growing status of trees, were analyzed. The results showed that the UAV stereo images, together with the ALS-derived digital elevation model (DEM), can achieve better performance than ALS data alone based on our data set. The match ratio between delineated and field-measured trees varied significantly, with the highest ratio of 66.94% obtained by UAV in the young aspen forest and the lowest ratio of 33.76% obtained by ALS in the old forest. Aside from the influence of point density, this letter also shed light on the important role that the spatial distribution and growing status of trees play in the delineation of individual trees. To conclude, integrating UAV stereo images with the ALS-derived DEM is effective in delineating individual tree attributes in small-scale windbreaks, which provides some suggestions for the future management of agriculture land.
Organizational learning and communities of practice: Toward a unified view of working, learning, and innovation

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An Agile Approach to Building RISC-V Microprocessors

The final phase of CMOS technology scaling provides continued increases in already vast transistor counts, but only minimal improvements in energy efficiency, thus requiring innovation in circuits and architectures. However, even huge teams are struggling to complete large, complex designs on schedule using traditional rigid development flows. This article presents an agile hardware development methodology, which the authors adopted for 11 RISC-V microprocessor tape-outs on modern 28-nm and 45-nm CMOS processes in the past five years. The authors discuss how this approach enabled small teams to build energy-efficient, cost-effective, and industry-competitive high-performance microprocessors in a matter of months. Their agile methodology relies on rapid iterative improvement of fabricatable prototypes using hardware generators written in Chisel, a new hardware description language embedded in a modern programming language. The parameterized generators construct highly customized systems based on the free, open, and extensible RISC-V platform. The authors present a case study of one such prototype featuring a RISC-V vector microprocessor integrated with a switched-capacitor DC-DC converter alongside an adaptive clock generator in a 28-nm, fully depleted silicon-on-insulator process.
Graph-based Knowledge Representation - Computational Foundations of Conceptual Graphs

Spend your few moment to read a book even only few pages. Reading book is not obligation and force for everybody. When you don't want to read, you can get punishment from the publisher. Read a book becomes a choice of your different characteristics. Many people with reading habit will always be enjoyable to read, or on the contrary. For some reasons, this graph based knowledge representation computational foundations of conceptual graphs advanced information and knowledge processing tends to be the representative book in this website.
Motion-Sensorless Control of BLDC-PM Motor With Offline FEM-Information-Assisted Position and Speed Observer

This paper proposes and investigates an offline finite-element-method (FEM)-assisted position and speed observer for brushless dc permanent-magnet (PM) (BLDC-PM) motor drive sensorless control based on the line-to-line PM flux linkage estimation. The zero crossing of the line-to-line PM flux linkage occurs right in the middle of two commutation points (CPs) and is used as a basis for the position and speed observer. The position between CPs is obtained by comparing the estimated line-to-line PM flux with the FEM-calculated line-to-line PM flux. Even if the proposed observer relies on the fundamental model of the machine, a safe starting strategy under heavy load torque, called I-f control, is used, with seamless transition to the proposed sensorless control. The I-f starting method allows low-speed sensorless control, without knowing the initial position and without machine parameter identification. Digital simulations and experimental results are shown, demonstrating the reliability of the FEM-assisted position and speed observer for BLDC-PM motor sensorless control operation.
A Large Public Corpus of Web Tables containing Time and Context Metadata

The Web contains vast amounts of HTML tables. Most of these tables are used for layout purposes, but a small subset of the tables is relational, meaning that they contain structured data describing a set of entities [2]. As these relational Web tables cover a very wide range of different topics, there is a growing body of research investigating the utility of Web table data for completing cross-domain knowledge bases [6], for extending arbitrary tables with additional attributes [7, 4], as well as for translating data values . The existing research shows the potentials of Web tables. However, comparing the performance of the different systems is difficult as up till now each system is evaluated using a different corpus of Web tables and as most of the corpora are owned by large search engine companies and are thus not accessible to the public. In this poster, we present a large public corpus of Web tables which contains over 233 million tables and has been extracted from the July 2015 version of the CommonCrawl. By publishing the corpus as well as all tools that we used to extract it from the crawled data, we intend to provide a common ground for evaluating Web table systems. The main difference of the corpus compared to an earlier corpus that we extracted from the 2012 version of the CommonCrawl as well as the corpus extracted by Eberius et al.  from the 2014 version of the CommonCrawl is that the current corpus contains a richer set of metadata for each table. This metadata includes table-specific information such as table orientation, table caption, header row, and key column, but also context information such as the text before and after the table, the title of the HTML page, as well as timestamp information that was found before and after the table. The context information can be useful for recovering the semantics of a table [7]. The timestamp information is crucial for fusing time-depended data, such as alternative population numbers for a city [8].
Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis

Color deconvolution has emerged as a popular method for color unmixing as a pre-processing step for image analysis of digital pathology images. One deficiency of this approach is that the stain matrix is pre-defined which requires specific knowledge of the data. This paper presents an unsupervised Sparse Non-negative Matrix Factorization (SNMF) based approach for color unmixing. We evaluate this approach for color unmixing of breast pathology images. Compared to Non-negative Matrix Factorization (NMF), the sparseness constraint imposed on coefficient matrix aims to use more meaningful representation of color components for separating stained colors. In this work SNMF is leveraged for decomposing pure stained color in both Immunohistochemistry (IHC) and Hematoxylin and Eosin (H&E) images. SNMF is compared with Principle Component Analysis (PCA), Independent Component Analysis (ICA), Color Deconvolution (CD), and Non-negative Matrix Factorization (NMF) based approaches. SNMF demonstrated improved performance in decomposing brown diaminobenzidine (DAB) component from 36 IHC images as well as accurately segmenting about 1400 nuclei and 500 lymphocytes from H & E images.
Interference-driven resource management for GPU-based heterogeneous clusters

GPU-based clusters are increasingly being deployed in HPC environments to accelerate a variety of scientific applications. Despite their growing popularity, the GPU devices themselves are under-utilized even for many computationally-intensive jobs. This stems from the fact that the typical GPU usage model is one in which a host processor periodically offloads computationally intensive portions of an application to the coprocessor. Since some portions of code cannot be offloaded to the GPU (for example, code performing network communication in MPI applications), this usage model results in periods of time when the GPU is idle. GPUs could be time-shared across jobs to "fill" these idle periods, but unlike CPU resources such as the cache, the effects of sharing the GPU are not well understood. Specifically, two jobs that time-share a single GPU will experience resource contention and interfere with each other. The resulting slow-down could lead to missed job deadlines. Current cluster managers do not support GPU-sharing, but instead dedicate GPUs to a job for the job's lifetime.
 In this paper, we present a framework to predict and handle interference when two or more jobs time-share GPUs in HPC clusters. Our framework consists of an analysis model, and a dynamic interference detection and response mechanism to detect excessive interference and restart the interfering jobs on different nodes. We implement our framework in Torque, an open-source cluster manager, and using real workloads on an HPC cluster, show that interference-aware two-job colocation (although our method is applicable to colocating more than two jobs) improves GPU utilization by 25%, reduces a job's waiting time in the queue by 39% and improves job latencies by around 20%.
Stochastic Adversarial Video Prediction

Being able to predict what may happen in the future requires an in-depth understanding of the physical and causal rules that govern the world. A model that is able to do so has a number of appealing applications, from robotic planning to representation learning. However, learning to predict raw future observations, such as frames in a video, is exceedingly challenging—the ambiguous nature of the problem can cause a naively designed model to average together possible futures into a single, blurry prediction. Recently, this has been addressed by two distinct approaches: (a) latent variational variable models that explicitly model underlying stochasticity and (b) adversarially-trained models that aim to produce naturalistic images. However, a standard latent variable model can struggle to produce realistic results, and a standard adversariallytrained model underutilizes latent variables and fails to produce diverse predictions. We show that these distinct methods are in fact complementary. Combining the two produces predictions that look more realistic to human raters and better cover the range of possible futures. Our method outperforms prior and concurrent work in these aspects.
Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests

Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and fields of view. By combining and refining state-of-the-art techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements. Kidneys are localized with random forests following a coarse-to-fine strategy. Their initial positions detected with global contextual information are refined with a cascade of local regression forests. A classification forest is then used to obtain a probabilistic segmentation of both kidneys. The final segmentation is performed with an implicit template deformation algorithm driven by these kidney probability maps. Our method has been validated on a highly heterogeneous database of 233 CT scans from 89 patients. 80% of the kidneys were accurately detected and segmented (Dice coefficient > 0.90) in a few seconds per volume.
A Probabilistic Model of Lexical and Syntactic Access and Disambiguation

The problems of access – retrieving linguistic structure from some mental grammar – and disambiguation – choosing among these structures to correctly parse ambiguous linguistic input – are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of garden-path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic levels. For example psycholinguistic theories of lexical access and idiom access and parsing theories of syntactic rule access have almost no commonality in methodology or coverage of psycholinguistic data. This paper presents a single probabilistic algorithm which models both the access and disambiguation of linguistic knowledge. The algorithm is based on a parallel parser which ranks constructions for access, and interpretations for disambiguation, by their conditional probability. Low-ranked constructions and interpretations are pruned through beam-search; this pruning accounts, among other things, for the garden-path effect. I show that this motivated probabilistic treatment accounts for a wide variety of psycholinguistic results, arguing for a more uniform representation of linguistic knowledge and for the use of probabilisticallyenriched grammars and interpreters as models of human knowledge of and processing of language.
A 100 GHz FMCW MIMO radar system for 3D image reconstruction

We present a frequency modulated continuous wave (FMCW) multiple input multiple output (MIMO) radar demonstrator system operating in the W-band at frequencies around 100 GHz. It consists of a two dimensional sparse array together with hardware for signal generation and image reconstruction that we will describe in more detail. The geometry of the sparse array was designed with the help of simulations to the aim of imaging at distances of just a few up to more than 150 meters. The FMCW principle is used to extract range information. To obtain information in both cross-range directions a back-propagation algorithm is used and further explained in this paper. Finally, we will present first measurements and explain the calibration process.
Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the capability of recommender systems to help users discover items they presumably do not know yet and which match their long-term preference profiles. In reality, however, recommendations can be helpful for customers also for other reasons, for example, when they remind them of items they were recently interested in or when they point site visitors to items that are currently discounted. In this work, we first adopt a systematic statistical approach to analyze what makes recommendations effective in practice and then propose ways of operationalizing these insights into novel recommendation algorithms. Our data analysis is based on log data of a large e-commerce site. It shows that various factors should be considered in parallel when selecting items for recommendation, including their match with the customer’s shopping interests in the previous sessions, the general popularity of the items in the last few days, as well as information about discounts. Based on these analyses we propose a novel algorithm that combines a neighborhood-based scheme with a deep neural network to predict the relevance of items for a given shopping session.
Distributed Ledger Technology: Blockchain Compared to Directed Acyclic Graph

Nowadays, blockchain is becoming a synonym for distributed ledger technology. However, blockchain is only one of the specializations in the field and is currently well-covered in existing literature, but mostly from a cryptographic point of view. Besides blockchain technology, a new paradigm is gaining momentum: directed acyclic graphs. The contribution presented in this paper is twofold. Firstly, the paper analyzes distributed ledger technology with an emphasis on the features relevant to distributed systems. Secondly, the paper analyses the usage of directed acyclic graph paradigm in the context of distributed ledgers, and compares it with the blockchain-based solutions. The two paradigms are compared using representative implementations: Bitcoin, Ethereum and Nano. We examine representative solutions in terms of the applied data structures for maintaining the ledger, consensus mechanisms, transaction confirmation confidence, ledger size, and scalability.
Dataset, Ground-Truth and Performance Metrics for Table Detection Evaluation

Table detection is an important task in the field of document analysis. It has been extensively studied since a couple of decades. Various kinds of document mediums are involved, from scanned images to web pages, from plain texts to PDF files. Numerous algorithms published bring up a challenging issue: how to evaluate algorithms in different context. Currently, most work on table detection conducts experiments on their in-house dataset. Even the few sources of online datasets are targeted at image documents only. Moreover, Precision and recall measurement are usual practice in order to account performance based on human evaluation. In this paper, we provide a dataset that is representative, large and most importantly, publicly available. The compatible format of the ground truth makes evaluation independent of document medium. We also propose a set of new measures, implement them, and open the source code. Finally, three existing table detection algorithms are evaluated to demonstrate the reliability of the dataset and metrics.