Miracle-dz/UniFAR-FTUs huggingface dataset:
Image fusion based on wedgelet and wavelet

Wavelet transform has been widely used in image fusion; it can well represent smooth regions and regular textures. However, wavelet is unable to provide a good representation of 2D edges. Wedgelet transform has good performance in approximation to edges. In this paper, we propose a new fusion method; it makes good approximation to edges in original images by using wedgelet and retains texture features in residual images with wavelet. Through combining wedgelet with wavelet, we develop a detector of edges and texture features in residual images. So the respective superiorities of wedgelet and wavelet can be combined by adding the fusion results using wavelet on the fusion results using wedgelet respectively. The experiments show that this novel method provides improved qualitative results compared to previous wavelet and dual tree complex wavelet (DT-CWT) fusion methods.
A tool for adaptive lighting design

Filmmakers, theater directors, and designers take extreme care in aesthetically composing their scenes. This sense of aesthetics is important to promote presence, immersion, and involvement. Video games have adopted many design theories and techniques from linear media, particularly film. However, interactive environments are dynamic and unpredictable, and thus require the development of theories, techniques, and tools specific to their interactive nature. In this paper, we present a lighting design tool that algorithmically adapts lighting to user interaction within game environments at runtime. Previous work includes our work on the Expressive Lighting Engine (ELE), which uses intelligent algorithms to adapt lighting in real-time accounting for variation in context, narrative, and intended artistic effects defined through numerical constraints. We encoded cinematic lighting techniques within ELE as mathematical formulae that guide the system's choices in terms of lighting to achieve artistic constraints while maintaining visual continuity. The work presented in this paper expands this work in several directions. First, ELE did not take objects' or scenes' materials into consideration. This is important since the appearance of objects under lighting conditions depends on their materials. Second, using numerical constraints as a method by which designers encode their artistic intention for lighting proved to be unintuitive. Thus, we present a prototype that builds on ELE and focuses on exploring solutions to resolve these problems.
Non-Monte Carlo Analysis of Low-Frequency Noise: Exposition of Intricate Nonstationary Behavior and Comparison With Legacy Models

Modeling and analysis of low-frequency noise, such as 1/f and burst noise, with time-varying bias conditions is a long-standing open problem in circuit simulation. In this paper, we offer a solution for this problem and present a computational model for low-frequency noise. The merits of our model are twofold. First, it is fully nonstationary. It can represent noise processes with time-varying statistics that are tightly coupled to the circuit variables in a stochastically correct manner. Second, its mathematical structure allows the utilization of well-established, non-Monte Carlo noise analysis techniques which are orders of magnitude faster than their Monte Carlo counterparts. We first provide an overview of our noise model along with the legacy modeling techniques. We verify that, when used with fast non-Monte Carlo analysis methods, our noise model produces results with the same accuracy as computationally laborious Monte Carlo simulations. We then proceed to contrast our nonstationary noise model with the legacy modulated stationary model. As a typical instance, where the results produced by these two models differ significantly due to intricate nonstationary behavior, we analyze the switched biasing technique that was proposed in order to reduce low-frequency noise. As a case study, we examine the 1/f noise behavior in the previously proposed coupled sawtooth oscillator circuit and show that, whereas simulations conducted with the legacy modulated stationary model suggest that no noise reduction is obtainable with switched biasing, our nonstationary noise model predicts the correct amount of noise reduction observed in previously published experimental data.
Comparative Transcriptional Profiling of Bacillus cereus Sensu Lato Strains during Growth in CO2-Bicarbonate and Aerobic Atmospheres

Background Bacillus species are spore-forming bacteria that are ubiquitous in the environment and display a range of virulent and avirulent phenotypes. This range is particularly evident in the Bacillus cereus sensu lato group; where closely related strains cause anthrax, food-borne illnesses, and pneumonia, but can also be non-pathogenic. Although much of this phenotypic range can be attributed to the presence or absence of a few key virulence factors, there are other virulence-associated loci that are conserved throughout the B. cereus group, and we hypothesized that these genes may be regulated differently in pathogenic and non-pathogenic strains. Methodology/Principal Findings Here we report transcriptional profiles of three closely related but phenotypically unique members of the Bacillus cereus group—a pneumonia-causing B. cereus strain (G9241), an attenuated strain of B. anthracis (Sterne 34F2), and an avirulent B. cereus strain (10987)—during exponential growth in two distinct atmospheric environments: 14% CO2/bicarbonate and ambient air. We show that the disease-causing Bacillus strains undergo more distinctive transcriptional changes between the two environments, and that the expression of plasmid-encoded virulence genes was increased exclusively in the CO2 environment. We observed a core of conserved metabolic genes that were differentially expressed in all three strains in both conditions. Additionally, the expression profiles of putative virulence genes in G9241 suggest that this strain, unlike Bacillus anthracis, may regulate gene expression with both PlcR and AtxA transcriptional regulators, each acting in a different environment. Conclusions/Significance We have shown that homologous and even identical genes within the genomes of three closely related members of the B. cereus sensu lato group are in some instances regulated very differently, and that these differences can have important implications for virulence. This study provides insights into the evolution of the B. cereus group, and highlights the importance of looking beyond differences in gene content in comparative genomics studies.
College Students with ADHD and LD: Effects of Support Services on Academic Performance

Two relatively common disabilities reported by college students are attention–deficit/hyperactivity disorder (ADHD) and learning disabilities (LD). Many questions remain regarding how best to support these students and whether services such as advising, coaching, and tutoring lead to significant academic gains. The current study examined the effect of support services on the GPA of students with LD and/or ADHD at a campus that specifically serves this population by tracking students’ support service usage and GPAs over a 5–year period. Results indicate that although most students who choose to receive coaching support experience academic gains, it is students with ADHD who obtain the greatest gains in GPA as a result of this support. Additionally, although tutoring can be of benefit to all students, significant GPA gains were obtained only for students with an LD diagnosis. Thus, there is a need to provide targeted supports aimed at addressing students’ specific areas of challenge.
An Efficient Framework for Extracting Parallel Sentences from Non-Parallel Corpora

Automatically building a large bilingual corpus that contains millions of words is always a challenging task. In particular in case of low-resource languages, it is difficult to find an existing parallel corpus which is large enough for building a real statistical machine translation. However, comparable non-parallel corpora are richly available in the Internet environment, such as in Wikipedia, and from which we can extract valuable parallel texts. This work presents a framework for effectively extracting parallel sentences from that resource, which results in significantly improving the performance of statistical machine translation systems. Our framework is a bootstrapping-based method that is strengthened by using a new measurement for estimating the similarity between two bilingual sentences. We conduct experiment for the language pair of English and Vietnamese and obtain promising results on both constructing parallel corpora and improving the accuracy of machine translation from English to Vietnamese.
Econometric Analysis of Price Index for Home Video Cassette Recorders in the U.S., 1978-87

This paper conducts an empirical analysis on the U.S. VCR market using a hedonic approach. The paper finds that quality-adjusted price indices decline at 11-12% per year, with a large annual drop of about 18% from 1982-85. Estimation and data analysis reveal interesting aspects of the evolution of quality and price in the VCR market. As a result of the exclusion of the VCR category until 1986, the paper estimates a bias of 2.4% per year in the CPI electronics subindex.
RF-Based device-free recognition of simultaneously conducted activities

We investigate the use of received RF-signals for activity recognition in scenarios with multiple receive nodes and multiple simultaneously active individuals. Our system features a short 0.5 second window over which features are calculated and we report on experiences in the choice of the neighbourhood size of the k-nearest neighbour (k-NN) classifier utilised. In a case study with software defined radio nodes utilised in an active, device-free activity recognition (DFAR) system, we observe a good recognition accuracy for the recognition of multiple simultaneously conducted activities with two and more receive devices. This is the first study to distinguish this particular set of activities from users conducting them simultaneously. For a single individual, we repeat the experiment and report the recognition accuracy in scenarios where the recognition area per receive node is larger than 8sqm
Delusion in Attribution: Caveats in Using Attribution for Multimedia Budget Allocation

Media attribution is the assignment of a percentage weight to each media touchpoint a consumer is exposed to prior to purchasing. Many firms consider using attribution to allocate media budgets, particularly for digital media, but an important question is whether this is appropriate. An initial hurdle when answering this question is that, despite the surge in interest for media attribution in marketing academia and practice, attribution does not have an agreed-on formal definition. Therefore, this article proposes an attribution formulation based on the relative incremental contribution that each medium makes to a purchase, taking into account advertising carryover and interaction effects. The formulation shows that attribution is proportional to the marginal effectiveness of a medium times its number of exposures. This means that often-used media will have high attribution weights. However, the profit-maximizing allocation for a fixed budget is a function of advertising effectiveness, but not a function of past exposure levels. By offering analytical derivations and studying simulated and empirical data, the paper shows how attribution can offer misleading insights on how to allocate resources across media. Moreover, the empirical example demonstrates that substantial gains in purchase probability can be made using profit-maximizing allocation compared with attribution-based allocation.
Experiential learning as preparation for leadership: An exploration of the cognitive and physiological processes

Purpose The purpose of this paper is to explore whether challenging experiences on development programmes would simulate leadership challenges and therefore stimulate the body’s autonomic nervous system response. The authors also aimed to determine whether increase in autonomic arousal would be related to learning, and/or moderated by personality variables. Design/methodology/approach The research used heart rate (HR) monitors to measure HR continuously over a two-day simulated learning experience. This was used to calculate autonomic arousal which was taken to be the difference between resting HR measured during sleep and HR during critical incidents (CIs) (HR). The authors correlated this with self-reports of learning immediately after, and one month after, the programme to assess the impact of autonomic arousal on perceived learning, as well as with variety of psychometric measures. Findings The research found significant correlations between (HR) during CIs and perceived learning which were not related to personality type. The research also found a significant correlation between (HR) and learning during a control event for individuals with “approach” personalities. Research limitations/implications Whilst a significant result was found, the sample size of 28 was small. The research also did not empirically assess the valence or intensity of the emotions experienced, and used only a self-report measure of learning. Future research should replicate the findings with a larger sample size, attempt to measure these emotional dimensions, as well as obtain perceptions of learning from direct reports and line managers. Practical implications The findings from the research help clarify the mechanisms involved in the effectiveness of experiential learning, and contribute to the understanding of the influence of personality type on perceived learning from experiential methodologies. Such understanding has implications for business schools and learning and development professionals, suggesting that development experiences that challenge leaders are likely to result in learning that is longer lasting. Originality/value The research extends the literature regarding the value of learning through experience, the role of autonomic arousal on learning, and the impact of negative emotions on cognition. The research makes a unique contribution by exploring the impact of experience on arousal and learning in a simulated learning experience and over time, by demonstrating that simulated experiences induce emotional and physiological responses, and that these experiences are associated with increased learning.
Combined Throughput and Energy Optimization for Synthesis of Streaming Applications on Multi-Core Architectures

We present a methodology for synthesizing streaming applications, modeled as task graphs, on multi-core architectures. We develop a task graph extraction and characterization framework that accurately determines the structure, computation and communication characteristics of application task graph using its specification in C. Furthermore, we develop a provably effective algorithm that jointly balances the workload assigned to each core, and minimizes intercore communication traffic. Consequently, both throughput and energy dissipation of implemented applications, that are of prime significance in target application domain, are simultaneously optimized. Experiment results show that our method improves both throughput and energy efficiency of streaming applications significantly.
Analysis and Recent Advances in Gamma Heating Measurements in MINERVE Facility by Using TLD and OSLD Techniques

The objective of this study is to develop nuclear heating measurement methods in Zero Power experimental reactors. This paper presents the analysis of Thermo-Luminescent Detector (TLD) and Optically Stimulated Luminescent Detectors (OSLD) experiments in the <formula formulatype="inline"><tex Notation="TeX">$\hbox{UO}_{2}$</tex> </formula> core of the MINERVE research reactor at the CEA Cadarache.
FPGA-based fuzzy PWM control implementation for hybrid system

This paper proposes a new method based on fuzzy logic theory to adjust the pulse width of PWM (Pulse Width Modulation) control in digital redesign and to implement it by FPGA (Field Programmable Gate Array) chip. The GA (Genetic Algorithm) is utilized to adjust the parameters of fuzzy logical system such that output response of controlled hybrid system closely matches with that of analog system.
On the stability of the Ethernet

We consider the stochastic behavior of binary exponential backoff, a probabilistic algorithm for regulating transmissions on a multiple access channel. Ethernet, a local area network, is built upon this algorithm. The fundamental theoretical issue is stability: does the backlog of packets awaiting transmission remain bounded in time, provided the rates of new packet arrivals are small enough? We present a realistic model of n ≥ 2 stations communicating over the channel. Our main result is to establish that the algorithm is stable if the sum of the arrival rates is sufficiently small. We report detailed results on which rates lead to stability when n = 2 stations share the channel. In passing we derive several other results bearing on the efficiency of the conflict resolution process. Lastly, we report results from a simulation study, which, in particular, indicate alternative retransmission strategies can significantly improve performance.
A closing scheme for finding almost-invariant sets in open dynamical systems

We explore the concept of metastability or almost-invariance in open dynamical systems. In such systems, the loss of mass through a ``hole'' occurs in the presence of metastability. We extend existing techniques for finding almost-invariant sets in closed systems to open systems by introducing a closing operation that has a small impact on the system's metastability.
Adopting Software Engineering Trends in AI

Designing and developing reliable, robust, well-architected, and easy-to-extend software applications or tools in any field requires conformance to sound principles and rules of software engineering. Intelligent systems, especially AI development tools, are no exception. Although AI has always been a wellspring of ideas that software engineering has later adopted, most of its gems remain buried in laboratories, available only to a few AI practitioners. This paper believes that AI tools should be integrated with mainstream SE tools and thus become more widely known and used. To that end, this paper presents the development of Air, an integrated AI development environment based on model-driven-architecture concepts. Using the philosophy of MDA in Air, familiar and mainstream software technologies are used and expanded with new functionalities
Towards Optimal Task Distribution on Computer Clusters with Intel MIC Coprocessors

Computer clusters with coprocessors/accelerators are typically leveraged to parallelize applications for reducing computation time. Given N parallel tasks and M processing cores, the typical strategy is to statically distribute those N tasks among M cores so that each core receives N/M tasks. However, for many sophisticated applications, the processing times of N tasks may vary. In other words, some tasks will take longer time than others. The static distribution will cause the cores with light tasks to wait for the cores with the heavy tasks, resulting in an imbalance in task distribution and the nonminimal overall processing time for the application. In this work we apply dynamic task distribution. All the unfinished tasks form a task pool. Once a core finishes a task, it will request a new task from the task pool. Through this manner, all cores will be kept busy in the whole computation process. We apply two additional optimization techniques to further improve the performance of applications on clusters with Intel MIC coprocessors. First, we design hybrid implementations to distribute tasks to both the CPUs and MICs. Second, we apply multiple-level parallelism technique to realize the concurrency among the N tasks as well as the concurrency in each task. We use sparse coding as a case study to demonstrate the advantages of our approach. Sparse coding is a class of unsupervised methods for learning sets of over-complete bases to represent data efficiently. The aim of sparse coding is to find a set of basis vectors such that an input vector can be represented as a linear combination of these basis vectors. The results show that the dynamic task distribution can improve the performance by 25% compared with the static one. Further, the hybrid mode implementation involving both the host CPUs and the MICs can outperform the basic offload mode implementation by 40%.
Radiative tau decays with one pseudoscalar meson.

We have calculated the decay [tau][r arrow][nu][pi]([ital K])[gamma]. We present the photon energy spectrum, the meson-photon invariant mass spectrum, and the integrated rate as a function of a photon energy cut or an invariant mass cut. Both the internal bremsstrahlung and the structure-dependent radiation have been taken into account. To this aim we have parametrized the form factors [ital F][sub [ital V]] and [ital F][sub [ital A]], which determine the structure-dependent radiation. Observables especially suited for the measurement of the structure-dependent form factors are found and implications on the width of the [ital a][sub 1] discussed. We compare our results with previously published ones.
EVALUATION OF FORMANT-LIKE FEATURES FOR ASR IDIAP-RR 02-04 EVALUATION OF FORMANT-LIKE FEATURES FOR ASR

This paper investigates possibilities to automatically find a low-dimensional, formant-related physical representation of the speech signal, which is suitable for automatic speech recognition (ASR). This aim is motivated by the fact that formants have been shown to be discriminant features for ASR. Combinations of automatically extracted formant-like features and ‘conventional’, noiserobust, state-of-the-art features (such as MFCCs including spectral subtraction and cepstral mean subtraction) have previously been shown to be more robust in adverse conditions than state-of-the-art features alone. However, it is not clear how these automatically extracted formant-like features behave in comparison with true formants. The purpose of this paper is to investigate two methods to automatically extract formant-like features, and to compare these features to hand-labeled formant tracks as well as to standard MFCCs in terms of their performance on a vowel classification task. Acknowledgements: We would like to thank Prof. James Hillenbrand for making the AEV database available to us and for his swift reply to all our enquiries. The development of the database was supported by a research grant from the American National Institutes of Health (NIDCD 1-R01-DC01661). Febe de Wet's visit to IDIAP was made possible by the I.B. M. Frye grant. Katrin Weber was funded through the Swiss National Science Foundation, project FN 2000-059169.99/1.
Variational Blue Noise Sampling

Blue noise point sampling is one of the core algorithms in computer graphics. In this paper, we present a new and versatile variational framework for generating point distributions with high-quality blue noise characteristics while precisely adapting to given density functions. Different from previous approaches based on discrete settings of capacity-constrained Voronoi tessellation, we cast the blue noise sampling generation as a variational problem with continuous settings. Based on an accurate evaluation of the gradient of an energy function, an efficient optimization is developed which delivers significantly faster performance than the previous optimization-based methods. Our framework can easily be extended to generating blue noise point samples on manifold surfaces and for multi-class sampling. The optimization formulation also allows us to naturally deal with dynamic domains, such as deformable surfaces, and to yield blue noise samplings with temporal coherence. We present experimental results to validate the efficacy of our variational framework. Finally, we show a variety of applications of the proposed methods, including nonphotorealistic image stippling, color stippling, and blue noise sampling on deformable surfaces.