nils-herrmann/clean_scirepeval huggingface dataset:
A Tractable Approach to Coverage and Rate in Cellular Networks

Cellular networks are usually modeled by placing the base stations on a grid, with mobile users either randomly scattered or placed deterministically. These models have been used extensively but suffer from being both highly idealized and not very tractable, so complex system-level simulations are used to evaluate coverage/outage probability and rate. More tractable models have long been desirable. We develop new general models for the multi-cell signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under very general assumptions, the resulting expressions for the downlink SINR CCDF (equivalent to the coverage probability) involve quickly computable integrals, and in some practical special cases can be simplified to common integrals (e.g., the Q-function) or even to simple closed-form expressions. We also derive the mean rate, and then the coverage gain (and mean rate loss) from static frequency reuse. We compare our coverage predictions to the grid model and an actual base station deployment, and observe that the proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate. In addition to being more tractable, the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.
FlashMob: distributed adaptive self-assembly

Autonomous systems need to support dynamic software adaptation in order to handle the complexity and unpredictability of the execution environment, and the changing needs of the end user. Although a number of approaches have been proposed, few address a key issue: that of distribution.
 In this paper we seek to overcome the limitations of centralised approaches. We build on our previous work on adaptive self-assembly within the three-layer model for autonomous systems to provide a decentralised technique for self-assembly. To achieve this in a fault-tolerant and scalable manner, we use a gossip protocol as a basis. While no central or leader node is aware of the full space of solutions, gossip ensures that agreement on a particular solution - in this case a component configuration - is reached in a logarithmic number of steps with respect to the size of the network.
Squeeze-and-Excitation Networks

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ~25% relative improvement over the winning entry of 2016. Code and models are available at https://github.com/hujie-frank/SENet.
Towards Online Spam Filtering in Social Networks

Online social networks (OSNs) are extremely popular among Internet users. Unfortunately, in the wrong hands, they are also effective tools for executing spam campaigns. In this paper, we present an online spam filtering system that can be deployed as a component of the OSN platform to inspect messages generated by users in real-time. We propose to reconstruct spam messages into campaigns for classification rather than examine them individually. Although campaign identification has been used for offline spam analysis, we apply this technique to aid the online spam detection problem with sufficiently low overhead. Accordingly, our system adopts a set of novel features that effectively distinguish spam campaigns. It drops messages classified as “spam” before they reach the intended recipients, thus protecting them from various kinds of fraud. We evaluate the system using 187 million wall posts collected from Facebook and 17 million tweets collected from Twitter. In different parameter settings, the true positive rate reaches 80.9% while the false positive rate reaches 0.19% in the best case. In addition, it stays accurate for more than 9 months after the initial training phase. Once deployed, it can constantly secure the OSNs without the need for frequent re-training. Finally, tested on a server machine with eight cores (Xeon E5520 2.2Ghz) and 16GB memory, the system achieves an average throughput of 1580 messages/sec and an average processing latency of 21.5ms on the Facebook dataset.
Learning Policies for Partially Observable Environments: Scaling Up

Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of pomdp's is motivated by a need to address realistic problems, existing techniques for nding optimal behavior do not appear to scale well and have been unable to nd satisfactory policies for problems with more than a dozen states. After a brief review of pomdp's, this paper discusses several simple solution methods and shows that all are capable of nding near-optimal policies for a selection of extremely small pomdp's taken from the learning literature. In contrast, we show that none are able to solve a slightly larger and noisier problem based on robot navigation. We nd that a combination of two novel approaches performs well on these problems and suggest methods for scaling to even larger and more complicated domains.
Automatic Inference of Finite-State Plant Models From Traces and Temporal Properties

Closed-loop model checking, a formal verification technique for industrial automation systems, increases the richness of specifications to be checked and reduces the state space to be verified compared to the open-loop case. To be applied, it needs the controller and the plant formal models to be coupled. There are approaches for controller synthesis, but little has been done regarding plant model construction. While manual plant modeling is time consuming and error-prone, discretizing a simulation model of the plant leads to state excess. This paper aims to solve the problem of automatic plant model construction from existing specification, which is represented in the form of plant behavior examples, or traces, and temporal properties. The proposed method, which is based on the translation of the problem to the Boolean satisfiability problem, is evaluated and shown to be applicable on several case study plant model synthesis tasks and on randomly generated problem instances.
Information Retrieval

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …
A 90nm-CMOS, 500Mbps, fully-integrated IR-UWB transceiver using pulse injection-locking for receiver phase synchronization

A fully-integrated, 3.1–5GHz Impulse-Radio UWB transceiver with on-chip flash ADC is designed in 90nm-CMOS. A new scheme for receiver phase acquisition is proposed that uses pulse injection-locking to synchronize the receive clock with the transmitted data, eliminating the need for clock/data recovery (CDR). Occupying 2mm2 die area, the transceiver achieves a maximum data rate of 500 Mbps, energy efficiency of 0.18nJ/b at 500Mbps, and a RX-BER of 10−3 across a distance of 10cm at 125Mbps.
Multiobjective cuckoo search for design optimization

Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we formulate a new cuckoo search for multiobjective optimization. We validate it against a set of multiobjective test functions, and then apply it to solve structural design problems such as beam design and disc brake design. In addition, we also analyze the main characteristics of the algorithm and their implications.
Relationship between Variants of One-Class Nearest Neighbors and Creating Their Accurate Ensembles

In one-class classification problems, only the data for the target class is available, whereas the data for the non-target class may be completely absent. In this paper, we study one-class nearest neighbor (OCNN) classifiers and their different variants. We present a theoretical analysis to show the relationships among different variants of OCNN that may use different neighbors or thresholds to identify unseen examples of the non-target class. We also present a method based on inter-quartile range for optimizing parameters used in OCNN in the absence of non-target data during training. Then, we propose two ensemble approaches based on random subspace and random projection methods to create accurate OCNN ensembles. We tested the proposed methods on 15 benchmark and real world domain-specific datasets and show that random-projection ensembles of OCNN perform best.
Satellite alerts track deforestation in real time

A satellite-based alert system could prove a potent weapon in the fight against deforestation. As few as eight hours after it detects that trees are being cut down, the system will send out e-mails warning that an area is endangered. That rapid response could enable environmental managers to catch illegal loggers before they damage large swathes of forest. " It's going to be very, very helpful, " says Brian Zutta Salazar, a remote-sensing scientist at the Peruvian Ministry of the Environment in Lima. Satellites are already valuable tools for monitoring deforestation; in recent decades, they have delivered consistent data on forest change over large and often remote areas. One such effort, the Real Time System for Detection of Deforestation, or DETER, has helped Brazil's government to reduce its deforestation rate by almost 80% since 2004, by alerting the country's environmental police to large-scale forest clearing. But DETER and other existing alert systems can be relatively slow to yield useful information. They use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite, which at its top resolution produces images with pixels covering an area 250 metres on each side, roughly equivalent to 10 football pitches. This is too big to spot small changes in land cover, so it can take computer programs that process MODIS data weeks or even months to detect that a forest is being cleared. " By the time MODIS picks up on it, it's almost too late, " says Peter Ellis, a forest-carbon scientist at the Nature Conservancy, a conservation group in Arlington, Virginia. Seeking to provide a sharper view, geographer Matthew Hansen of the University of Maryland in College Park and his colleagues published maps showing year-to-year changes in global forest cover from 2000 to 2012 (M. C. Hansen et al. Science 342, 850–853; 2013). The researchers relied on data from NASA's two active Landsat satellites, which together photograph every spot on Earth every eight days. Each pixel in a Landsat image is 30 metres on each side — roughly the size of a baseball diamond. This means that an area covered by just one MODIS pixel is captured in roughly 70 smaller Landsat pixels. Hansen and his team wrote data-processing software that can use these higher-resolution images to recognize a disturbance as small as a road snaking its way through a previously untouched forest, something that often appears before clear-cutting begins. " …
Nexus : A GPU Cluster for Accelerating Neural Networks for Video Analysis

We address the problem of serving Deep Neural Networks (DNNs) efficiently from a cluster of GPUs. In order to realize the promise of very low-cost processing made by accelerators such as GPUs, it is essential to run them at sustained high utilization. Doing so requires cluster-scale resource management that performs detailed scheduling of GPUs, reasoning about groups of DNN invocations that need to be co-scheduled, and moving from the conventional whole-DNN execution model to executing fragments of DNNs. Nexus is a fully implemented system that includes these innovations. On large-scale case studies on 16 GPUs, Nexus shows 1.8-41× better throughput than state-of-the-art systems while staying within latency constraints >99% of the time.
Stack-propagation: Improved Representation Learning for Syntax

Traditional syntax models typically leverage part-of-speech (POS) information by constructing features from hand-tuned templates. We demonstrate that a better approach is to utilize POS tags as a regularizer of learned representations. We propose a simple method for learning a stacked pipeline of models which we call “stack-propagation”. We apply this to dependency parsing and tagging, where we use the hidden layer of the tagger network as a representation of the input tokens for the parser. At test time, our parser does not require predicted POS tags. On 19 languages from the Universal Dependencies, our method is 1.3% (absolute) more accurate than a state-of-the-art graph-based approach and 2.7% more accurate than the most comparable greedy model.
Sleep deprivation amplifies reactivity of brain reward networks, biasing the appraisal of positive emotional experiences.

Appropriate interpretation of pleasurable, rewarding experiences favors decisions that enhance survival. Conversely, dysfunctional affective brain processing can lead to life-threatening risk behaviors (e.g., addiction) and emotion imbalance (e.g., mood disorders). The state of sleep deprivation continues to be associated with maladaptive emotional regulation, leading to exaggerated neural and behavioral reactivity to negative, aversive experiences. However, such detrimental consequences are paradoxically aligned with the perplexing antidepressant benefit of sleep deprivation, elevating mood in a proportion of patients with major depression. Nevertheless, it remains unknown how sleep loss alters the dynamics of brain and behavioral reactivity to rewarding, positive emotional experiences. Using functional magnetic resonance imaging (fMRI), here we demonstrate that sleep deprivation amplifies reactivity throughout human mesolimbic reward brain networks in response to pleasure-evoking stimuli. In addition, this amplified reactivity was associated with enhanced connectivity in early primary visual processing pathways and extended limbic regions, yet with a reduction in coupling with medial frontal and orbitofrontal regions. These neural changes were accompanied by a biased increase in the number of emotional stimuli judged as pleasant in the sleep-deprived group, the extent of which exclusively correlated with activity in mesolimbic regions. Together, these data support a view that sleep deprivation not only is associated with enhanced reactivity toward negative stimuli, but imposes a bidirectional nature of affective imbalance, associated with amplified reward-relevant reactivity toward pleasure-evoking stimuli also. Such findings may offer a neural foundation on which to consider interactions between sleep loss and emotional reactivity in a variety of clinical mood disorders.
Parallel Coordinate Descent Methods for Big Data Optimization

In this work we show that randomized (block) coordinate descent methods can be accelerated by parallelization when applied to the problem of minimizing the sum of a partially separable smooth convex function and a simple separable convex function. The theoretical speedup, as compared to the serial method, and referring to the number of iterations needed to approximately solve the problem with high probability, is a simple expression depending on the number of parallel processors and a natural and easily computable measure of separability of the smooth component of the objective function. In the worst case, when no degree of separability is present, there may be no speedup; in the best case, when the problem is separable, the speedup is equal to the number of processors. Our analysis also works in the mode when the number of blocks being updated at each iteration is random, which allows for modeling situations with busy or unreliable processors. We show that our algorithm is able to solve a LASSO problem involving a matrix with 20 billion nonzeros in 2 hours on a large memory node with 24 cores.
Automatic detection of player's identity in soccer videos using faces and text cues

In soccer videos, most significant actions are usually followed by close--up shots of players that take part in the action itself. Automatically annotating the identity of the players present in these shots would be considerably valuable for indexing and retrieval applications. Due to high variations in pose and illumination across shots however, current face recognition methods are not suitable for this task. We show how the inherent multiple media structure of soccer videos can be exploited to understand the players' identity without relying on direct face recognition. The proposed method is based on a combination of interest point detector to "read" textual cues that allow to label a player with its name, such as the number depicted on its jersey, or the superimposed text caption showing its name. Players not identified by this process are then assigned to one of the labeled faces by means of a face similarity measure, again based on the appearance of local salient patches. We present results obtained from soccer videos taken from various recent games between national teams.
Splitting Compounds by Semantic Analogy

Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. In this paper, we investigate whether distributional semantics in the form of word embeddings can enable a deeper, i.e., more knowledge-rich, processing of compounds than the standard string-based methods. We present an unsupervised approach that exploits regularities in the semantic vector space (based on analogies such as “bookshop is to shop as bookshelf is to shelf”) to produce compound analyses of high quality. A subsequent compound splitting algorithm based on these analyses is highly effective, particularly for ambiguous compounds. German to English machine translation experiments show that this semantic analogy-based compound splitter leads to better translations than a commonly used frequency-based method.
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora

Mining cluster evolution from multiple correlated time-varying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical Dirichlet processes (EvoHDP) to discover interesting cluster evolution patterns from such text data. We formulate the EvoHDP as a series of hierarchical Dirichlet processes~(HDP) by adding time dependencies to the adjacent epochs, and propose a cascaded Gibbs sampling scheme to infer the model. This approach can discover different evolving patterns of clusters, including emergence, disappearance, evolution within a corpus and across different corpora. Experiments over synthetic and real-world multiple correlated time-varying data sets illustrate the effectiveness of EvoHDP on discovering cluster evolution patterns.
Bayesian Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an expert. IRL is motivated by situations where knowledge of the rewards is a goal by itself (as in preference elicitation) and by the task of apprenticeship learning (learning policies from an expert). In this paper we show how to combine prior knowledge and evidence from the expert’s actions to derive a probability distribution over the space of reward functions. We present efficient algorithms that find solutions for the reward learning and apprenticeship learning tasks that generalize well over these distributions. Experimental results show strong improvement for our methods over previous heuristic-based approaches.
User-oriented Bayesian identification and its configuration

Identification of tracked objects is a key capability of automated surveillance and information systems for air, surface and subsurface (maritime), and ground environments, improving situational awareness and offering decision support to operational users. The Bayesian-based identification data combining process (IDCP) provides an effective instrument for fusion of uncertain identity indications from various sources. A user-oriented approach to configuration of the process is introduced, which enables operators to adapt the IDCP to changing identification needs in varying operational scenarios. Application of results from cognitive psychology and decision theory provides good access to retrieval of Bayesian data and makes configuration easily feasible to operational experts.