Miracle-dz/UniFAR-FTUs huggingface dataset:
Stylized black and white images from photographs

Halftoning algorithms attempt to match the tone of an input image despite lower color resolution in the output. However, in some artistic media and styles, tone matching is not at all the goal; rather, details are either portrayed sharply or omitted entirely. In this paper, we present an algorithm for abstracting arbitrary input images into black and white images. Our goal is to preserve details while as much as possible producing large regions of solid color in the output. We present two methods based on energy minimization, using loopy belief propagation and graph cuts, but it is difficult to devise a single energy term that both sufficiently promotes coherence and adequately preserves details. We next propose a third algorithm separating these two concerns. Our third algorithm involves composing a base layer, consisting of large flat-colored regions, with a detail layer, containing the small high-contrast details. The base layer is computed with energy minimization, while local adaptive thresholding gives the detail layer. The final labeling is tidied by removing small components, vectorizing, and smoothing the region boundaries. The output images satisfy our goal of high spatial coherence with detail preservation.
Measurement and prediction of residual stresses in thick-section steel welds

Significant levels of residual stress are developed in the production of thick-section steel welds. Residual stress can be measured by a variety of methods, although few offer the ability to determine the spatial distribution completely through the thickness. This paper summarizes recent developments in the deep-hole method for measuring residual stresses in thick-section welds. Experimental results obtained from a variety of welded steel components are described. The measurement method has also been used to provide validation for finite element simulations of residual stresses in a welded cylinder-to-nozzle stainless steel component. The paper describes the finite element weld analysis and compares the results with measurements obtained from several locations in the complex geometry. Overall, there is good agreement between the predicted and measured distributions of residual stress, but the magnitudes of predicted stress tend to be greater.
Computing approximate blocking probabilities for large loss networks with state-dependent routing

A reduced load approximation (also referred to as an Erlang fixed point approximation) for estimating point-to-point blocking probabilities in loss networks (e.g., circuit switched networks) with state-dependent routing is considered. In this approximation scheme, the idle capacity distribution for each link in the network is approximated, assuming that these distributions are independent from link to link. This leads to a set of nonlinear fixed-point equations which can be solved by repeated substitutions. The accuracy and the computational requirements of the approximation procedure for a particular routing scheme, namely least loaded routing, is examined. Numerical results for six-node and 36-node asymmetric networks are given. A novel reduced load approximation for multirate networks with state-dependent routing is also presented. >
Towards mixed language speech recognition systems

Multilingual speech recognition obviously involves numerous research challenges, including common phoneme sets, adaptation on limited amount of training data, as well as mixed language recognition (common in many countries, like Switzerland). In this latter case, it is not even possible to assume that one knows in advance the language being spoken. This is the context and motivation of the present work. We indeed investigate how current state-of-the-art speech recognition systems can be exploited in multilingual environments, where the language (from an assumed set of five possible languages, in our case) is not a priori known during recognition. We combine monolingual systems and extensively develop and compare different features and acoustic models. On SpeechDat(II) datasets, and in the context of isolated words, we show that it is actually possible to approach the performances of monolingual systems even if the identity of the spoken language is not a priori known.
Relevance feedback based on parameter estimation of target distribution

Relevance feedback formulations have been proposed to refine query result in content-based image retrieval in the past few years. Many of them focus on a learning approach to solve the feedback problem. In this paper, we present an expectation maximization approach to estimate the user's target distribution through user's feedback. Furthermore, we describe how to use the maximum entropy display to fully utilize user's feedback information. We detail the process and also demonstrate the result through experiments.
CS-2012-03 A Study on Justifications for Choices : Explanation Patterns and Guidelines

Many di erent forms of explanation have been proposed for justifying decisions made by automated systems. However, there is no consensus on what constitutes a good explanation, or what information these explanations should include. In this paper, we present the results of a study into how people justify their decisions. Analysis of our results allowed us to extract the forms of explanation adopted by users to justify choices, and the situations in which these forms are used. The analysis led to the development of guidelines and patterns for explanations to be generated by automated decision systems. This paper presents the study, its results, and the guidelines and patterns we derived.
MODELING THE EFFECT OF ORDER TIMING REDUCTION ON COORDINATING SUPPLY CHAIN INVENTORIES USING A SELECTIVE DISCOUNT STRATEGY

Prior studies show that coordinating supply chain inventories with common replenishment epochs (CRE) provides benefit for vendor and buyers. Coordination using selective discount (SD) strategy allows some buyers to participate in the coordination scheme while other buyers continue to order as earlier. This can reduce the vendor's total cost more than that of all buyers participating and no buyer coordination in many circumstances. This paper considers the situation in which SD buyers place orders before CRE due to demand timing uncertainty. According to vendor flexibility, each SD buyer with early ordering incurs various levels of penalty cost to compensate for possible capacity loss for the vendor. The proposed models also allow for a more practical situation by considering the cases of equal and unequal probabilities of early ordering for SD buyers. The results indicate that the vendor's cost savings can be improved substantially based on the vendor flexibility, probability of early ordering, and the number of SD buyers involved in early ordering.
Does Wage Rank Affect Employees Well-Being?

What makes workers happy? Here we argue that pure ‘rank’ matters. It is currently believed that wellbeing is determined partly by an individual’s absolute wage (say, 30,000 dollars a year) and partly by the individual’s relative wage (say, 30,000 dollars compared to an average in the company or neighborhood of 25,000 dollars). Our evidence shows that this is inadequate. The paper demonstrates that range-frequency theory – a model developed independently within psychology and unknown to most economists – predicts that wellbeing is gained partly from the individual’s ranked position of a wage within a comparison set (say, whether the individual is number 4 or 14 in the wage hierarchy of the company). We report an experimental study and an analysis of a survey of 16,000 employees’ wage satisfaction ratings. We find evidence of rank-dependence in workers’ pay satisfaction.
A probabilistic approach to emission-line galaxy classification

We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$ (WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically define classes of galaxies in a three-dimensional space spanned by the $\log$ [OIII]/H$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), optical parameters. The best-fit GMM based on several statistical criteria suggests a solution around four Gaussian components (GCs), which are capable to explain up to 97 per cent of the data variance. Using elements of information theory, we compare each GC to their respective astronomical counterpart. GC1 and GC4 are associated with star-forming galaxies, suggesting the need to define a new starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN) class and WHAN's weak AGN class. GC3 is associated with BPT's composite class and WHAN's strong AGN class. Conversely, there is no statistical evidence -- based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN diagrams respectively. Subtleties aside, we demonstrate the potential of our methodology to recover/unravel different objects inside the wilderness of astronomical datasets, without lacking the ability to convey physically interpretable results. The probabilistic classifications from the GMM analysis are publicly available within the COINtoolbox (this https URL\_Catalogue/).
MILLIMETER WAVE MATERIAL CHARACTERIZATION OF STRONTIUM FERRITES USING MAGNETO-OPTICAL APPROACH

Complex dielectric permittivity and magnetic permeability measurements of solid strontium ferrites have been performed in the broad band millimeter wave frequency range employing free space technique based on transverse magneto-optical approach. The experimental set-up consisting of a quasi-optical millimeter wave spectrometer with a backward wave oscillator as a high power tunable source of coherent radiation provides the transmittance spectra, in a magnetic field up to 7.5 kOe. Dielectric and magnetic parameters of strontium-ferrites have been calculated by matching theoretical curves to the experimental transmittance spectra. Shift of the ferromagnetic resonance to higher frequencies, as well as broadening of the zone of strong absorption with the increase of magnetic field, have been observed. I. INTRODUCTION Complex magnetic permeability and dielectric permittivity are two fundamental parameters that describe the response of matter to the external electric and magnetic fields. The development of new methods that provide accurate determination of both parameters from a single set of measurements is important for practical application of ferrite materials. In the millimeter wave range, a technique based on the concepts of quasi-optical Gaussian beams and free-space propagation is an alternative to the resonance techniques. High accuracy of this method was proven by numerous dielectric measurements carried out using the channel, as well as the Fourier transform technique [1] – [4]. The conventional approach applied in the millimeter-wave range uses well known Fresnel equations for calculation of dielectric and magnetic properties from a set of free-space measurements. At least four independent free-space measurements are necessary for the complete characterization of the demagnetized isotropic ferrites. This method turns out to be indirect and less convenient for the characterization of magnetic materials. The technique based on magneto-optical approach overcomes the shortcomings of the conventional method and its significant advantage being that the millimeter-wave dielectric and magnetic parameters are determined from a single set of broad-band transmission measurements thus enabling the complete and accurate characterization of ferrites in the entire frequency range.
High-performance bankruptcy prediction model using Graphics Processing Units

In recent years the the potential and programmability of Graphics Processing Units (GPU) has raised a note-worthy interest in the research community for applications that demand high-computational power. In particular, in financial applications containing thousands of high-dimensional samples, machine learning techniques such as neural networks are often used. One of their main limitations is that the learning phase can be extremely consuming due to the long training times required which constitute a hard bottleneck for their use in practice. Thus their implementation in graphics hardware is highly desirable as a way to speed up the training process. In this paper we present a bankruptcy prediction model based on the parallel implementation of the Multiple BackPropagation (MBP) algorithm which is tested on a real data set of French companies (healthy and bankrupt). Results by running the MBP algorithm in a sequential processing CPU version and in a parallel GPU implementation show reduced computational costs with respect to the latter while yielding very competitive performance.
Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture.
Reproductive Biology and Endocrinology Open Access the Proximal Promoter Region of Mtert Is Sufficient to Regulate Telomerase Activity in Es Cells and Transgenic Animals

Background: The reverse transcriptase of telomerase (Tert) controls telomerase activity maintaining the end of linear chromosomes in eukaryotic cells. Telomerase function is highly active in undifferentiated multipotent stem cells, decreases with cell differentiation and is generally absent from most somatic cells in the adult. Its absence is responsible of telomeres shortening in such somatic cells. Using an in vivo transgenic model and an in vitro culture differentiation of adult stem cells, we examined the elements of the mouse Tert (mTert) promoter that control telomerase activity.
Fabricating Nanogaps in YBa2Cu3O7-delta for Hybrid Proximity-Based Josephson Junctions

The advances of nanotechnologies applied to high-critical-temperature superconductors (HTSs) have recently given a huge boost to the field, opening new prospectives for their integration in hybrid devices. The feasibility of this research goes through the realization of HTS nanogaps with superconductive properties close to the as-grown bulk material at the nanoscale. Here we present a fabrication approach allowing the realization of YBa2Cu3O7-delta (YBCO) nanogaps with dimensions as small as 35 nm. To assess the quality of the nanogaps, we measure, before and after an ozone treatment, the current-voltage characteristics and the resistance versus temperature of YBCO nanowires with various widths and lengths, fabricated by using different lithographic processes. The analysis of the superconducting transition with a thermally activated vortex-entry model allows us to determine the maximum damage the nanowires undergo during the patterning which relates to the upper bound for the dimension of the nanogap. We find that the effective width of the nanogap is of the order of 100 nm at the superconducting transition temperature while retaining the geometrical value of about 35 nm at lower temperatures. The feasibility of the nanogaps for hybrid Josephson devices is demonstrated by bridging them with thin Au films. We detect a Josephson coupling up to 85 K with an almost ideal magnetic-field response of the Josephson current. These results pave the way for the realization of complex hybrid devices, where tiny HTS nanogaps can be instrumental to study the Josephson effect through barriers such as topological insulators or graphene.
Elissa: A Dialectal to Standard Arabic Machine Translation System

Modern Standard Arabic (MSA) has a wealth of natural language processing (NLP) tools and resources. In comparison, resources for dialectal Arabic (DA), the unstandardized spoken varieties of Arabic, are still lacking. We present Elissa , a machine translation (MT) system from DA to MSA. Elissa (version 1.0) employs a rule-based approach that relies on morphological analysis, morphological transfer rules and dictionaries in addition to language models to produce MSA paraphrases of dialectal sentences. Elissa can be employed as a general preprocessor for dialectal Arabic when using MSA NLP tools. u j ®E @ e J K. QaE@ u I@ e J K. QaE@ H A J OA aE @ aO e J E B@ e Og. Q E E u G. nA g ÐA ¢ : A  E @ eE KA U O XP@ nOð H@ ðX @ Q n J K B A O J K. A J K. nA g u j ®E @ e J K. QaE@ e a EE @ e m.I'A aOI eQ J» XP@ n Oð H@ ð X @ Yg. n K @ Ye A J Jm '. u ÐY ® J . e J K. QaE@ e a EE @ aO e J A J ®E @ Q « e J ojOI @ q E @ u eð , e J K. QaE@ H A J OA aE @ em.I'A a UI A  E @ YO Ja K . u j ®E @ e J K. QaE@ u I@ e J K. QaE@ H A J OA aE @ aO e J E B@ e Og. Q E AK. Ðn ®K u G. nA g ÐA ¢ u eð , A  E @ Nk. A aOð e Og. Q E @ Y «@ n  aO e «nOm. ×ð e OE3⁄4E E u Q E@ EJ Ej JE @ eJ ÐY j J  , Y «@ n ®E @ u I« A J J.O C k u j ®E @ e E Om.I'@ P A J J kB e K n a E h. X A U s u I@ e A @ , e J OA aE @ H A OE3⁄4E E H A Og. Q Kð H A X @ QO Z A  B e J OA « Ð @ Y j J@ EJ.  e J K. QaE@ H A J OA aE @ em.I'A a UI A  E @ Ð@ Y j J@ aoOs . e JoO UI @ E O m.I'@ © J Og. a K. eC £ E  B@ . A iD E« u j ®E @ e J K. QaE@ e a EE e Y a O H @ ð X @
Learning a Partial Behavior for a Competitive Robotic Soccer Agent

Robotic soccer is a highly competitive domain. Accordingly, the use of learnt behaviors in this application field presumes not only learning algorithms that are known to converge and produce stable results, but also imposes the wish for obtaining optimal or at least near-optimal behaviors, even when working within high-dimensional and continuous state/action spaces. This paper deals with the continuous amelioration of adaptive soccer playing skills in robotic soccer simulation, documenting and presenting results of our hunt for optimal policies. We show that not too much effort is necessary to realize straightforward Reinforcement Learning algorithms in this domain, but that a heavy load of work is required when tweaking them towards competitiveness.
DDoS Attack Detection and Wavelets

Abstract This paper presents a systematic method for DDoS attack detection. DDoS attack can be considered a system anomaly or misuse from which abnormal behavior is imposed on network traffic. Attack detection can be performed via abnormal behavior identification. Network traffic characterization with behavior modeling could be a good indication of attack detection. Aggregated traffic has been found to be strong bursty across a wide range of time scales. Wavelet analysis is able to capture complex temporal correlation across multiple time scales with very low computational complexity. We utilize energy distribution based on wavelet analysis to detect DDoS attack traffic. Energy distribution over time will have limited variation if the traffic keeps its behavior over time (i.e. attack-free situation) while an introduction of attack traffic in the network will elicit significant energy distribution deviation in a short time period. Our experimental results with typical Internet traffic trace show that energy distribution variance markedly changes, causing a “spike” when traffic behaviors are affected by DDoS attack. In contrast, normal traffic exhibits a remarkably stationary energy distribution. In addition, this spike in energy distribution variance can be captured in the early stages of an attack, far ahead of congestion build-up, making it an effective detection of the attack.
Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection

While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish it from healthy tissue. Recently, the notion of discovery radiomics was introduced, where a large amount of custom, quantitative radiomic features are directly discovered from the wealth of readily available medical imaging data. In this study, we present a novel discovery radiomics framework for skin cancer detection, where we leverage novel deep multi-column radiomic sequencers for high-throughput discovery and extraction of a large amount of custom radiomic features tailored for characterizing unique skin cancer tissue phenotype. The discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical images comprising of different skin cancers such as melanoma and basal cell carcinoma, and demonstrated sensitivity and specificity of 91% and 75%, respectively, thus achieving dermatologist-level performance and \break hence can be a powerful tool for assisting general practitioners and dermatologists alike in improving the efficiency, consistency, and accuracy of skin cancer diagnosis.
Block Markov Superposition Transmission of RUN Codes

In this paper, we propose a simple procedure to construct (decodable) good codes with any given alphabet (of moderate size) for any given (rational) code rate to achieve any given target error performance (of interest) over additive white Gaussian noise channels. We start with constructing codes over groups for any given code rates. This can be done in an extremely simple way if we ignore the error performance requirement for the time being. Actually, this can be satisfied by repetition (R) codes and uncoded (UN) transmission along with time-sharing technique. The resulting codes are simply referred to as RUN codes for convenience. The encoding/decoding algorithms for RUN codes are almost trivial. In addition, the performance can be easily analyzed. It is not difficult to imagine that the RUN code usually performs far away from the corresponding Shannon limit. Fortunately, the performance can be improved as required by spatially coupling the RUN codes via block Markov superposition transmission (BMST), resulting in the BMST-RUN codes. Simulation results show that the BMST-RUN codes perform well (within around 1 dB away from Shannon limits) for a wide range of code rates and outperform the BMST with bit-interleaved coded modulation scheme.