Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast approximation scheme for fractional covering problems with variable upper bounds

We present the first combinatorial approximation scheme for mixed positive packing and covering linear programs that yields a pure approximation guarantee. Our algorithm returns solutions that simultaneously satisfy general positive covering constraints and packing constraints that are variable upper bounds. The returned solution has positive linear objective function value at most 1 + ε times the optimal value.Our approximation scheme is based on Lagrangian-relaxation methods. Previous such approximation schemes for mixed packing and covering problems does not simultaneously satisfy packing and covering constraints exactly. We show how to exactly satisfy general positive covering constraints simultaneously with variable upper bounds.A natural set of problems that our work addresses are linear programs for various network design problems: generalized Steiner network, vertex connectivity, directed connectivity, capacitated network design, group Steiner forest. These are all NP-hard problems for which there are approximation algorithms that round the solution to the corresponding linear program. Solving the linear program is often the computational bottleneck in these problems, and thus a fast approximation scheme for the LP relaxation means faster approximation algorithms.For the special case of survivable network design, we introduce a new modification of the push-relabel maximum flow algorithm that allows us to perform each iteration in amortized O(m + n log n) time, instead of one maximum flow per iteration that is implied by the straight forward adaptation of our general algorithm. (m is the number of edges and n is the number of vertices in the network.) In conjunction with an observation that reduces the number of iterations to {log n for f0} constraint matrices, the modification allows us to obtain an algorithm that is faster than existing exact or approximate algorithms by a factor of at least O(m) and by a factor of O(m log n) if the number of demand pairs is Ω(n).
Measures of Temporal Pattern Complexity

In this study, three measures of temporal pattern complexity were compared with regard to their perceptual validity. The first measure, based on the work of Tanguiane (1993), uses the idea that a temporal pattern can be described in terms of (elaborations of) more simple patterns, which occur simultaneously at different levels. The second measure is based on the complexity measure for finite sequences proposed by Lempel and Ziv (1976), which is related to the number of steps in a self-delimiting production process by which such a sequence is presumed to be generated. The third measure, newly developed here, is rooted in the theoretical framework of rhythm perception of Povel and Essens (1985). It takes into account the ease of coding a temporal pattern and the complexity of the segments resulting from this coding. The perceptual validity of the three measures was evaluated in an experiment in which subjects judged the complexity of 35 temporal patterns. Correlations between the three measures and the collected complexity judgments indicated that the third measure is a much better predictor of temporal pattern complexity than the other two measures. This is probably due to the fact that this measure, unlike the other two, is based on an empirically tested model of rhythm perception that takes into account the isochronous frame against which the rhythm is perceived. Reasons for the differences in performance between the three measures are discussed.
Some Findings on the Covariance Structure of WaveletCoe cients : Theory and Models in a Bayesian

In this paper some theoretical results regarding the wavelet coeecients co-variance structure between and within diierent scales are presented. These ndings, generally applicable to the problem of modeling wavelet coeecients, are used here in the context of wavelet shrinkage where the purpose is to estimate a function from noisy data. The approach taken is Bayesian and uses an hierarchical structure. Applications to benchmark simulated signals are presented.
Efficient dynamic pruning on largest scores first (LSF) retrieval

Inverted index traversal techniques have been studied in addressing the query processing performance challenges of web search engines, but still leave much room for improvement. In this paper, we focus on the inverted index traversal on document-sorted indexes and the optimization technique called dynamic pruning, which can efficiently reduce the hardware computational resources required. We propose another novel exhaustive index traversal scheme called largest scores first (LSF) retrieval, in which the candidates are first selected in the posting list of important query terms with the largest upper bound scores and then fully scored with the contribution of the remaining query terms. The scheme can effectively reduce the memory consumption of existing term-at-atime (TAAT) and the candidate selection cost of existing document-at-a-time (DAAT) retrieval at the expense of revisiting the posting lists of the remaining query terms. Preliminary analysis and implementation show comparable performance between LSF and the two well-known baselines. To further reduce the number of postings that need to be revisited, we present efficient rank safe dynamic pruning techniques based on LSF, including two important optimizations called list omitting (LSF_LO) and partial scoring (LSF_PS) that make full use of query term importance. Finally, experimental results with the TREC GOV2 collection show that our new index traversal approaches reduce the query latency by almost 27% over the WAND baseline and produce slightly better results compared with the MaxScore baseline, while returning the same results as exhaustive evaluation.
How Spatial Segmentation improves the Multimodal Geo-Tagging

In this paper we present a hierarchical, multi-modal approach in combination with dierent granularity levels for the Placing Task at the MediaEval benchmark 2012. Our approach makes use of external resources like gazetteers to extract toponyms in the metadata and of visual and textual features to identify similar content. First, the bounderies detection recognizes the country and its dimension to speed up the estimation and to eliminate geographical ambiguity. Next, we prepared a training database to group them together into geographical regions and to build a hierarchical model. The fusion of visual and textual methods for dierent granularities is used to classify the videos’ location into possible regions. At the end the Flickr videos are tagged with the geo-information of the most similar training image within the regions that is previously ltered by the probabilistic model for each test video.
Wild recommendations: Presenting citizens relevant content based on use patterns and context

In this paper we present field experience from the in-the-wild deployment of the recommendation mechanism of CLIO, a novel urban computing system that allows forming and interacting with the collective city memory. Our goal has been to study how users perceive relevant content and in which way the context affects the content they want to explore. We had the opportunity to evaluate for a long-term CLIO in two different cities, in Greece and Finland. The recommendation mechanism of CLIO exploits intelligent techniques in order to present users suitable memories. Intelligence in CLIO is primarily grounded on its ontology-based context model, a reasoning and inference mechanism that support context-awareness and finally a rule-based system producing content recommendations for users based on their random interactions with the system. Our findings during two distinct phases of evaluation shed light on how users consider their profile and preferences, how they perceive relevant content and how the context affects their selections. These results allowed us to develop a recommendation mechanism for the CLIO system, that produces "wild" recommendations for users based on their random interactions with CLIO.
The digital dream lab: tabletop puzzle blocks for exploring programmatic concepts

Tangible interaction links digital data with physical forms to support embodied use. Puzzle pieces, which their inherent physical syntax of connectable elements, provide a powerful and expressive metaphor on which to construct such tangible systems. Prior work has explored this potential in the domain of edutainment systems for children aimed at tasks such as learning logic, programming or organizational skills. Although this work is promising, it has largely focused on relatively advanced concepts and children of ages 7-12 years. The work presented in this paper adopts the same perspective but focuses on young children (5 and under) and a simpler range of concepts relating to the clustering and manipulation of data. To achieve this it presents the design (including results from a series of six formative field studies) and implementation of the Digital Dream Lab tabletop puzzle block system. This system, intended for installation in a museum, engages young children (aged 4-5) to explore simple programmatic concepts and the link between the physical and virtual world. The paper closes with design recommendations of future work targeting this goal, setting and age group.
Exploring Host-based Software Defined Networking and its Applications

Network operators need detailed understanding of their networks in order to ensure functionality and to mitigate security risks. Unfortunately, legacy networks are poorly suited to providing this understanding. While the software-defined networking paradigm has the potential to, existing switch-based implementations are unable to scale sufficiently to provide information in a fine-grained. Furthermore, as switches are inherently blind to the inner workings of hosts, significantly hindering an operator’s ability to understand the true context behind network traffic. In this work, we explore a host-based software-defined networking implementation. We evaluation our implementation, showing that it is able to scale beyond the capabilities of a switch-based implementation. Furthermore, we discuss various detailed network policies that network operators can write and enforce which are impossible in a switch-based implementation. We also implement and discuss an anti-reconnaissance system that can be deployed without any additional components.
Adaptive Task Checkpointing and Replication: Toward Efficient Fault-Tolerant Grids

A grid is a distributed computational and storage environment often composed of heterogeneous autonomously managed subsystems. As a result, varying resource availability becomes commonplace, often resulting in loss and delay of executing jobs. To ensure good grid performance, fault tolerance should be taken into account. Commonly utilized techniques for providing fault tolerance in distributed systems are periodic job checkpointing and replication. While very robust, both techniques can delay job execution if inappropriate checkpointing intervals and replica numbers are chosen. This paper introduces several heuristics that dynamically adapt the above mentioned parameters based on information on grid status to provide high job throughput in the presence of failure while reducing the system overhead. Furthermore, a novel fault-tolerant algorithm combining checkpointing and replication is presented. The proposed methods are evaluated in a newly developed grid simulation environment dynamic scheduling in distributed environments (DSiDE), which allows for easy modeling of dynamic system and job behavior. Simulations are run employing workload and system parameters derived from logs that were collected from several large-scale parallel production systems. Experiments have shown that adaptive approaches can considerably improve system performance, while the preference for one of the solutions depends on particular system characteristics, such as load, job submission patterns, and failure frequency.
A Multitask Point Process Predictive Model

Point process data are commonly observed in fields like healthcare and the social sciences. Designing predictive models for such event streams is an under-explored problem, due to often scarce training data. In this work we propose a multitask point process model, leveraging information from all tasks via a hierarchical Gaussian process (GP). Nonparametric learning functions implemented by a GP, which map from past events to future rates, allow analysis of flexible arrival patterns. To facilitate efficient inference, we propose a sparse construction for this hierarchical model, and derive a variational Bayes method for learning and inference. Experimental results are shown on both synthetic data and as well as real electronic health-records data.
Generating subtour constraints for the TSP from pure integer solutions

The traveling salesman problem (TSP) is one of the most prominent combinatorial optimization problems. Given a complete graph G = (V, E) and non-negative distances d for every edge, the TSP asks for a shortest tour through all vertices with respect to the distances d. The method of choice for solving the TSP to optimality is a branch-and-bound-and-cut approach. Usually the integrality constraints are relaxed first and all separation processes to identify violated inequalities are done on fractional solutions. In our approach we try to exploit the impressive performance of current ILPsolvers and work only with integer solutions without ever interfering with fractional solutions. We stick to a very simple ILP-model and relax the subtour constraints only. The resulting problem is solved to integer optimality, violated constraints (which are trivial to find) are added and the process is repeated until a feasible solution is found. In order to speed up the algorithm we pursue several attempts to find as many relevant subtours as possible. These attempts are based on the clustering of vertices with additional insights gained from empirical observations and random graph theory. Computational results are performed on test instances taken from the TSPLIB95 and on random Euclidean graphs.
The Alteration of Hippocampal BDNF Expression Is Associated with Anxiety-Like Behavior Following the Injury to the Sciatic Nerve

Background: BDNF is a critical regulator of the cognitive functions of the brain. According to the modulatory role of BDNF in the chronic pain state, this study focused on changes in BDNF expression in the hippocampus and anxiety-like behavior, following an injury to the sciatic nerve. Methods: 30 adult male Wistar rats were randomly divided into three subsets as neuropathic, sham, and control. The neuropathic pain was induced due to chronic constriction injury of the sciatic nerve. Anxiety-like behavior was observed using the open field and elevated plus-maze tests. The level of BDNF was determined by real-time polymerase chain reaction analysis. Results: The results showed that the pain threshold decreased two weeks after CCI. Simultaneously, there was a decrease in the BDNF levels (P < 0.001, F = 15. 03). In addition, the anxiety-like behaviors increased in the open field (P < 0.01, F = 25.67; P < 0.05, F = 23.02) and elevated plus-maze (P < 0.05, F = 6.01; P < 0.001, F = 5.95) tests. In the third week after CCI, the increased pain threshold was accompanied by an increase in the level of BDNF, latency to leave the first quadrant, number of rearing behaviors (P < 0.05, F = 26.07, F = 24.04), and time spent in open arms (P < 0.05, F = 5.30) while time spent in closed arms decreased (P < 0.05, F = 4.70). Conclusions: Alteration in BDNF expression is involved in CCI-induced anxiety-like behavior.
Successful process implementation

We propose measuring software process improvement (SPI) success through implementation success - the extent to which initiatives lead to actual changes in software engineering practice. First, without implementation success, SPI success is impossible. Second, only when implementation succeeds can we see how SPI initiatives affect software practices. Third, implementation success is easy to assess. Finally, focusing on implementation success is a pragmatic way to steer SPI initiatives toward success. We studied the approach and outcome of 18 different SPI initiatives conducted over a five-year period at the telecom company Ericsson AB, based in Gothenburg, Sweden. Doing so gave us insight into how SPI initiatives can best 1) ensure stakeholder commitment 2) support organizational learning 3) distribute resources over different activities 4) manage customer relations.
LiDAR based relative pose and covariance estimation for communicating vehicles exchanging a polygonal model of their shape

Relative localization between autonomous vehicles is an important issue for accurate cooperative localization. It is also essential for obstacle avoidance or platooning. Thanks to communication between vehicles, additional information, such as vehicle model and dimension, can be transmitted to facilitate this relative localization process. In this paper, we present and compare different algorithms to solve this problem based on LiDAR points and the pose and model communicated by another vehicle. The core part of the algorithm relies on iterative minimization tested with two methods and different model associations using point-to-point and point-to-line distances. This work compares the accuracy, the consistency and the number of iterations needed to converge for the different algorithms in different scenarios, e.g. straight lane, two lanes and curved lane driving.
Maximizing Multicell Capacity Using Distributed Power Allocation and Scheduling

Joint optimization of transmit power and scheduling in wireless data networks promises significant system-wide capacity gains. However, this problem is known to be NP-hard and thus difficult to tackle in practice. We analyze this problem for the downlink of a multicell full reuse network with the goal of maximizing the overall network capacity. We propose a distributed power allocation and scheduling algorithm which provides significant capacity gain for any finite number of users. This distributed cell coordination scheme, in effect, achieves a form of dynamic spectral reuse, whereby the amount of reuse varies as a function of the underlying channel conditions and only limited inter-cell signaling is required.
Timescales of Sensory- and Decision-Related Activity in the Middle Temporal and Medial Superior Temporal Areas

The contribution of sensory neurons to perceptual decisions about external stimulus events has received much attention, but it is less clear how sensory responses are integrated over time to produce decisions that are both rapid and reliable. To address this issue, we recorded from middle temporal area and medial superior temporal area neurons in rhesus macaques performing a task requiring the detection and discrimination of unpredictable speed changes. We examined how neuronal activity encoded the sign of the speed change and predicted the animals' behavioral judgments and reaction times, with a focus on the timescales over which neuronal activity is informative. False detection trials, on which animals reported a speed change even though none had occurred, were grouped according to the animals' discrimination judgment. By comparing the neuronal responses between the two groups of false detection trials, we were able to predict the animals' choices from the sensory activity of single neurons at levels significantly better than chance. These choice probability measurements were strongest using spike counts in an 80 ms window ending 150 ms before a choice saccade began, but significant choice probabilities were observed in windows as short as 10 ms. While the maximum deviation in spiking rate following a speed change is evident in the transient response, averaging neuronal activity in longer time windows can be more informative about both the stimulus and the animals' behavioral judgments. Thus the timescales found in this study represent a trade-off between producing rapid reactions and overcoming the noise inherent in short time windows.
Iterated Local Search with Guided Mutation

Guided mutation uses the idea of estimation of distribution algorithms to improve conventional mutation operators. It combines global statistical information and the location information of good individual solutions for generating new trial solutions. This paper suggests using guided mutation in iterative local search. An experimental comparison between a conventional iterated local search (CILS) and an iterated local search with guided mutation has been conducted on four classes of the test instances of the quadratic assignment problem.
Post-Learning Optimization of Tree Ensembles for Efficient Ranking

Learning to Rank (LtR) is the machine learning method of choice for producing high quality document ranking functions from a ground-truth of training examples. In practice, efficiency and effectiveness are intertwined concepts and trading off effectiveness for meeting efficiency constraints typically existing in large-scale systems is one of the most urgent issues. In this paper we propose a new framework, named CLEaVER, for optimizing machine-learned ranking models based on ensembles of regression trees. The goal is to improve efficiency at document scoring time without affecting quality. Since the cost of an ensemble is linear in its size, CLEaVER first removes a subset of the trees in the ensemble, and then fine-tunes the weights of the remaining trees according to any given quality measure. Experiments conducted on two publicly available LtR datasets show that CLEaVER is able to prune up to 80% of the trees and provides an efficiency speed-up up to 2.6x without affecting the effectiveness of the model.
Neural Network-Based Artificial Bandwidth Expansion of Speech

The limited bandwidth of 0.3-3.4 kHz in current telephone systems reduces both the quality and the intelligibility of speech. Artificial bandwidth expansion is a method that expands the bandwidth of the narrowband speech signal in the receiving end of the transmission link by adding new frequency components to the higher frequencies, i.e., up to 8 kHz. In this paper, a new method for artificial bandwidth expansion, termed Neuroevolution Artificial Bandwidth Expansion (NEABE) is proposed. The method uses spectral folding to create the initial spectral components above the telephone band. The spectral envelope is then shaped in the frequency domain, based on a set of parameters given by a neural network. Subjective listening tests were used to evaluate the performance of the proposed algorithm, and the results showed that NEABE speech was preferred over narrowband speech in about 80% of the test cases
NightWatch: Integrating Lightweight and Transparent Cache Pollution Control into Dynamic Memory Allocation Systems

Cache pollution, by which weak-locality data unduly replaces strong-locality data, may notably degrade application performance in a shared-cache multicore machine. This paper presents NightWatch, a cache management subsystem that provides general, transparent and low-overhead pollution control to applications. NightWatch is based on the observation that data within the same memory chunk or chunks within the same allocation context often share similar locality property. NightWatch embodies this observation by online monitoring current cache locality to predict future behavior and restricting potential cache polluters proactively. We have integrated NightWatch into two popular allocators, tcmalloc and ptmalloc2. Experiments with SPEC CPU2006 show that NightWatch improves application performance by up to 45% (18% on average), with an average monitoring overhead of 0.57% (up to 3.02%).