Miracle-dz/UniFAR-FTUs huggingface dataset:
CrowdReranking: exploring multiple search engines for visual search reranking

Most existing approaches to visual search reranking predominantly focus on mining information within the initial search results. However, the initial ranked list cannot provide enough cues for reranking by itself due to the typically unsatisfying visual search performance. This paper presents a new method for visual search reranking called CrowdReranking, which is characterized by mining relevant visual patterns from image search results of multiple search engines which are available on the Internet. Observing that different search engines might have different data sources for indexing and methods for ranking, it is reasonable to assume that there exist different search results yet certain common visual patterns relevant to a given query among those results. We first construct a set of visual words based on the local image patches collected from multiple image search engines. We then explicitly detect two kinds of visual patterns, i.e., salient and concurrent patterns, among the visual words. Theoretically, we formalize reranking as an optimization problem on the basis of the mined visual patterns and propose a close-form solution. Empirically, we conduct extensive experiments on several real-world search engines and one benchmark dataset, and show that the proposed CrowdReranking is superior to the state-of-the-art works.
A Learning Probabilistic Approach for Object Segmentation

This paper proposes a new method for figure-ground image segmentation based on a probabilistic learning approach of the object shape. Historically, segmentation is mostly defined as a data-driven bottom-up process, where pixels are grouped into regions/objects according to objective criteria, such as region homogeneity, etc. In particular, it aims at creating a partition of the image into contiguous, homogenous regions. In the proposed work, we propose to incorporate prior knowledge about the object shape and category to segment the object from the background. The segmentation process is composed of two parts. In the first part, object shape models are built using sets of object fragments. The second part starts by first segmenting an image into homogenous regions using the mean-shift algorithm. Then, several object hypotheses are tested and validated using the different object shape models as supporting information. As an output, our algorithm identifies the object category, position, as well as its optimal segmentation. Experimental results show the capacity of the approach to segment several object categories.
WCET-Aware Dynamic I-Cache Locking for a Single Task

Caches are widely used in embedded systems to bridge the increasing speed gap between processors and off-chip memory. However, caches make it significantly harder to compute the worst-case execution time (WCET) of a task. To alleviate this problem, cache locking has been proposed. We investigate the WCET-aware I-cache locking problem and propose a novel dynamic I-cache locking heuristic approach for reducing the WCET of a task. For a nonnested loop, our approach aims at selecting a minimum set of memory blocks of the loop as locked cache contents by using the min-cut algorithm. For a loop nest, our approach not only aims at selecting a minimum set of memory blocks of the loop nest as locked cache contents but also finds a good loading point for each selected memory block. We propose two algorithms for finding a good loading point for each selected memory block, a polynomial-time heuristic algorithm and an integer linear programming (ILP)-based algorithm, further reducing the WCET of each loop nest. We have implemented our approach and compared it to two state-of-the-art I-cache locking approaches by using a set of benchmarks from the MRTC benchmark suite. The experimental results show that the polynomial-time heuristic algorithm for finding a good loading point for each selected memory block performs almost equally as well as the ILP-based algorithm. Compared to the partial locking approach proposed in Ding et al. [2012], our approach using the heuristic algorithm achieves the average improvements of 33%, 15%, 9%, 3%, 8%, and 11% for the 256B, 512B, 1KB, 4KB, 8KB, and 16KB caches, respectively. Compared to the dynamic locking approach proposed in Puaut [2006], it achieves the average improvements of 9%, 19%, 18%, 5%, 11%, and 16% for the 256B, 512B, 1KB, 4KB, 8KB, and 16KB caches, respectively.
From Off-line to On-line Handwriting Recognition

On-line handwriting includes more information on the time order of the writing signal and on the dynamics of the writing process than off-line handwriting. Therefore, on-line recognition systems achieve higher recognition rates. This can be concluded from results reported in the literature, and has been demonstrated empirically as part of this work. We propose a new approach for recovering the time order of the off-line writing signal. Starting from an over-segmentation of the off-line handwriting into regular and singular parts, the time ordering of these parts and recognition of the word are performed simultaneously. This approach, termed “ OrdRec”, is based on a graph description of the handwriting signal and a recognition process using Hidden Markov Models (HMM). A complete omni-scriptor isolated word recognition system has been developed. Using a dynamic lexicon and models for upper and lower case characters, our system can process binary and gray value word images of any writing style (script, cursive, or mixed). Using a dual handwriting data base which features both the on-line and the off-line signal for each of the 30 000 words written by about 700 scriptors, we have shown experimentally that such an off-line recognition system, using the recovered time order information, can achieve recognition performances close to those of an on-line recognition system.
The target cell nucleus is not required for cell-mediated granzyme- or Fas-based cytotoxicity

The requirement for target cell nuclei in the two apoptotic death pathways used by cytotoxic lymphocytes was tested using model effector systems in which the granzyme and Fas pathways of target damage are isolated. Mast cell tumors expressing granzymes A and B in addition to cytolysin/perforin lysed tumor target cells about 10-fold more efficiently than comparable effector cells without granzymes. Enucleated cytoplast targets derived from these cells were also lysed with a similar 10-fold effect of granzymes. In contrast to cytoplasts, effector granzyme expression did not influence lysis of red cell targets. The Fas pathway was assessed using the selected cytotoxic T lymphocyte hybridoma subline d11S, which lysed target cells expressing Fas but not those lacking Fas. Similarly, cytoplasts derived from Fas+ but not Fas- cells were also readily lysed by these effector cells. Thus, neither the nucleus itself nor the characteristic apoptotic nuclear damage associated with the two major cell death pathways used by cytotoxic lymphocytes are required for cell death per se.
Early validation analyses of atmospheric profiles from EOS MLS on the aura Satellite

We present results of early validation studies using retrieved atmospheric profiles from the Earth Observing System Microwave Limb Sounder (MLS) instrument on the Aura satellite. "Global" results are presented for MLS measurements of atmospheric temperature, ozone, water vapor, hydrogen chloride, nitrous oxide, nitric acid, and carbon monoxide, with a focus on the January-March 2005 time period. These global comparisons are made using long-standing global satellites and meteorological datasets, as well as some measurements from more recently launched satellites. Comparisons of MLS data with measurements from the Ft. Sumner, NM, September 2004 balloon flights are also presented. Overall, good agreement is obtained, often within 5% to 10%, but we point out certain issues to resolve and some larger systematic differences; some artifacts in the first publicly released MLS (version 1.5) dataset are noted. We comment briefly on future plans for validation and software improvements.
The user-subjective approach to personal information management systems

In this article we suggest a user-subjective approach to Personal Information Management (PIM) system design. This approach advocates that PIM systems relate to the subjective value-added attributes that the user gives to the data stored in the PIM system. These attributes should facilitate system use: help the user find the information item again, recall it when needed, and use it effectively in the next interaction with the item. Driven from the user-subjective approach are three generic principles which are described and discussed: (a) The subjective classification principle, stating that all information items related to the same subjective topic should be classified together regardless of their technological format; (b) The subjective importance principle, proposing that the subjective importance of information should determine its degree of visual salience and accessibility; and (c) The subjective context principle, suggesting that information should be retrieved and viewed by the user in the same context in which it was previously used. We claim that these principles are only sporadically implemented in operating systems currently available on personal computers, and demonstrate alternatives for interface design.
Cognitive function in early clinical phase huntington disease after rivastigmine treatment.

BACKGROUND In Huntington disease (HD) patients receiving rivastigmine treatment improvement of behavioral symptoms and of cognitive function (assessed with screening diagnostic instruments) has been reported. The aim of the present study was to verify such improvement in cognitive function by cognitive function assessment with a detailed neuropsychological battery covering all relevant cognitive systems expected to be impaired in early phase HD. SUBJECTS AND METHODS Eighteen (18) HD patients entered the study and were randomly allocated to the rivastigmine and placebo group. All subjects underwent neuropsychological assessment at baseline. Follow-up neuropsychological assessment was applied after 6 months of rivastigmine or placebo treatment. Eighteen (18) healthy controls entered the study to control for practice effect and underwent neuropsychological assessment at baseline and after 6 months, without treatment. The neuropsychological battery consisted of assessment tools that are sensitive to cognitive impairment seen in early phase HD: CTMT, SDMT, Stroop (attention and information control), RFFT, TOL, Verbal fluency (executive functioning), CVLT-II, RCFT (learning and memory). Effect of rivastigmine and possible effect of practice was assessed using the mixed ANOVA model. RESULTS No statistically significant effect of rivastigmine treatment on cognitive function in HD patients was detected. There was no evidence for practice or placebo effect. CONCLUSIONS Detailed neuropsychological assessment did not confirm previously reported effect of rivastigmine treatment on cognitive function in HD patients. The limitations of our study are, in particular, small sample size and the lack of a single measure of relevant cognitive functioning in HD patients. Instead of focusing solely on statistical significance, a clinical relevance study is proposed to clarify the issue of rivastigmine effects in HD.
Dielectric-fibre surface waveguides for optical frequencies

A dielectric fibre with a refractive index higher than its surrounding region is a form of dielectric waveguide which represents a possible medium for the guided transmission of energy at optical frequencies. The particular type of dielectric-fibre waveguide discussed is one with a circular cross-section. The choice of the mode of propagation for a fibre waveguide used for communication purposes is governed by consideration of loss characteristics and information capacity. Dielectric loss, bending loss and radiation loss are discussed, and mode stability, dispersion and power handling are examined with respect to information capacity. Physical-realisation aspects are also discussed. Experimental investigations at both optical and microwave wavelengths are included.
Filtering Information Extraction via User-Contributed Knowledge

Large repositories of knowledge can enable more powerful AI systems. Information Extraction (IE) is one approach to building knowledge repositories by extracting knowledge from text. Open IE systems like TextRunner [Banko et al., 2007] are able to extract hundreds of millions of assertions from Web text. However, because of imperfections in extraction technology and the noisy nature of Web text, IE systems return a mix of both useful, informative facts (e.g., "the FDA banned ephedra") and less informative statements (e.g., "the FDA banned products"). This paper investigates using user-contributed knowledge from Wikipedia and from TextRunner website visitors to train classifiers that automatically filter extracted assertions. In a study of human ratings of the interestingness of TextRunner assertions, we show that our approach substantially enhances the quality of results. Our relevance feedback filter raises the fraction of interesting results in the top thirty from 41.6% to 64.1%.
TWIX: twig structure and content matching of selective queries using binary labeling

XML queries specify predicates on the content and the structure of the elements of tree-structured XML documents. Hence, discovering the occurrences of twig (tree structure) query patterns is a core operation for XML query processing. In this paper, we propose a novel technique for matching XML twig query patterns, named TWIX, which results in a substantial reduction of the search space, response time, size and structure invariance through a distributed binary labeling and tree traversal algorithm. Furthermore, TWIX benefits from an interactive graphical user interface for twig query matching.
BM3D image denoising using heterogeneous computing platforms

Noise reduction is often performed at an early stage of the image processing path. In order to keep the processing delays small in different computing platforms, it is important that the noise reduction is performed swiftly. In this paper, the block-matching and three-dimensional filtering (BM3D) denoising algorithm is implemented on heterogeneous computing platforms using OpenCL and CUDA frameworks. To our knowledge, these implementations are the first successful open source attempts to use GPU computation for BM3D denoising. The presented GPU implementations are up to 7.5 times faster than their respective CPU implementations. At the same time, the experiments illustrate general design challenges in using massively parallel processing platforms for the calculation of complex imaging algorithms.
Building Successful Software Development Teams Using TSP and Effective Communication Networks

Social network models can help explain how and why some organizational structures and practices work. Moreover, network analysis is accessible to engineering practitioners and is particularly effective in helping us understand the value of Team Software Process SM (TSP SM ). Networks not only offer an explanation of how TSP works with respect to communication, but also suggest that as we scale beyond a team of teams, new organizational structures will be required. The role manager structure sets TSP apart. Teams that use role managers take advantage of a proven communication pattern that scales as teams grow. Successful work is facilitated by effective communication, which can be improved with specific network structures. These structures can take shape through the self-organization of teams around TSP role managers. Unlike the traditional tree hierarchy that you see on most organizational charts, the more flexible, self-organizing network can respond quickly to the demands of a fast-paced workplace.
A Gaussian Processes Reinforcement Learning Method in Large Discrete State Spaces

In order to solve the problem of ”curse of dimensionality”, which means that the state spaces will grow exponentially in the number of features, in large discrete state spaces in reinforcement learning, a reinforcement learning method based on Gaussian processes is proposed. The Gaussian processes model can represent the distributions of functions, and it can be used to get a distribution of the expectation instead of its value. The experiment result shows that the performance such as speed of convergence and final effect can be improved obviously. The ”curse of dimensionality” in large discrete state spaces could be solved to ascertain extent with the GP regression model.
Relationship-aware sequential pattern mining : results on medical practise on antibiotic treatment and resistance development

Relationship-aware sequential pattern mining is the problem of mining frequent patterns in sequences in which the events of a sequence are mutually related by one or more concepts from some respective hierarchical taxonomies, based on the type of the events. Additionally events themselves are also described with a certain number of taxonomical concepts. We present RaSP an algorithm that is able to mine relationshipaware patterns over such sequences; RaSP follows a two stage approach. In the first stage it mines for frequent type patterns and all their occurrences within the different sequences. In the second stage it performs hierarchical mining where for each frequent type pattern and its occurrences it mines for more specific frequent patterns in the lower levels of the taxonomies. We test RaSP on a real world medical application, that provided the inspiration for its development, in which we mine for frequent patterns of medical behavior in the antibiotic treatment of microbes and show that it has a very good computational performance given the complexity of the relationship-aware sequential pattern mining problem.
Energy comparison and optimization of wireless body-area network technologies

Wireless body-area networks (WBANs) have revolutionized the way mobile and wearable computers communicate with their users and I/O devices. We investigate an energy-efficient wireless device driver for low-duty peripherals, sensors and other I/O devices employed in a WBAN to communicate with a more powerful central device. We present an extensive comparative study of two popular WBAN technologies, 802.15. 1 (Bluetooth) and 802.15. 4 (ZigBee), in terms of design cost, performance, and energy efficiency. We discuss the impact of tunable parameters of the wireless device driver on connection latency and energy consumption for both Bluetooth and ZigBee. We address dynamic resource management in higher-level protocols by investigating the trade-off between connection latency and energy consumption. We propose an energy-efficient power-down policy that utilizes the interval between consecutive connection requests for energy reduction; we study an adaptive connection latency management technique that adjusts various tunable parameters dynamically to achieve minimum connection latency without changing the energy consumption level. Our measurements and experimental results show that these techniques are very effective in reducing energy consumption while meeting connection latency requirements.
Exploiting cross-modal rhythm for robot perception of objects

Why: Objects that move rhythmically are common and important in the kinds of workplaces where we might employ a humanoid robot. The humanoid form is often argued for so that the robot can interact well with tools designed for humans, and such tools are typically used in a repetitive manner, whether the sound is generated by physical abrasion or collision: hammers, chisels, saws etc. We also work with the perception of toys designed for infants – rattles, bells etc. – which could have utility for entertainment/pet robotics. The advantage of combining rhythmic information across acoustic and visual senses is that these senses have complementary properties. Since sound waves disperse more readily than light, vision retains more spatial structure – but for the same reason it is sensitive to occlusion and the relative angle of the robot’s sensors, while auditory perception is quite robust to these factors. The spatial trajectory of a moving object can be recovered quite straightforwardly from visual analysis, but not from sound. However, the trajectory in itself is not very revealing about the nature of the object. We use the trajectory to extract visual and acoustic features – patches of pixels, and sound frequency bands – that are likely to be associated with the object. Both can be used for recognition. Sound features are easier to use since they are relatively insensitive to spatial parameters such as the relative position and pose of the object and the robot. How: For each object moving visually, fragments of the concurrent sound input are taken for periods of that object, aligned, and compared. If the fragments are consistent, with sound and vision in phase with each other, then the visual trajectory and the sound are considered bound. The relationship betweenobject motion andthe sound generatedvaries inanobject-specific way. The hammer causes sound when changing direction after striking an object. The bell typically causes sound at either extreme of motion. A toy truck causes sound while moving rapidly with wheels spinning; it is quiet when changing direction.
Book review: Current Trends in SNePS - Semantic Network Processing System Edited by D. Kumar (Springer Verlag, 1990)

SNePS-the Semantic Network Processing System - has been under development by Stuart Shapiro and his colleagues at the State University of New York at Buffalo since the late 1970s (Shapiro 1978, Shapiro and Rapaport 1990, Shapiro and the SNePS Impl. Group 1991, SNePS Research Group 1991). These proceedings contain twelve papers presenting various SNePS-oriented research and application projects. Quite a bit of the material is system-specific. Since the book consists of a collection of reports related only by their connection to SNePS, this review is also organized on a paper-by-paper basis, rather than as a single narrative.
Retirement Trajectories and Income Redistribution Through the Pension System in Finland

Abstract:In this article, we investigate the redistributive outcomes of the Finnish pension system. We hypothesize that a pension system does not straightforwardly diminish, maintain, or increase income differences after retirement, but it can have different outcomes for different groups. Our focus is on differences in changes in income between groups that vary in their timing and type of retirement. We make use of longitudinal register-based data from the Finnish Centre for Pensions and analyze income and retirement trajectories of Finnish employees born in 1948 from the age of 57 to 66 (N = 44,449). Our aim is to find out in what way trajectories of income from earnings and pensions are related to different types of retirement trajectories, while controlling for gender, sector of employment, and length of working life. Eight distinct retirement trajectories are identified using sequence analysis. The results of our multilevel regression models indicate that the pension system sustains inequalities related to gender and employment sector. Early old-age retirement and part-time retirement are associated with higher earnings and more generous pension entitlements, indicating cumulative advantage. Lower earnings are associated with higher risk of early exit through unemployment and disability pensions, while the pension system guarantees a minimum income level in retirement, resulting in status leveling. Those who retire later are relatively well off in work, but worse off in retirement, suggesting a status-leveling outcome. By disentangling these outcomes of the pension system, it is possible to learn social policy lessons for other national institutional contexts as well.