Miracle-dz/UniFAR-FTUs huggingface dataset:
DETECTION OF MICROCALCIFICATION IN DIGITAL MAMMOGRAMS USING ONE DIMENSIONAL WAVELET TRANSFORM

Mammography is the most efficient method for breast cancer early detection. Clusters of microcalcifications are the early sign of breast cancer and their detection is the key to improve prognosis of breast cancer. Microcalcifications appear in mammogram image as tiny localized granular points, which is often difficult to detect by naked eye because of their small size. Automatic and accurately detection of microcalcifications has received much more attention from radiologists and physician. An efficient method for automatic detection of clustered microcalcifications in digitized mammograms is the use of Computer Aided Diagnosis (CAD) systems. This paper presents a one dimensional wavelet-based multiscale products scheme for microcalcification detection in mammogram images. The detection of microcalcifications were achieved by decomposing the each line of mammograms by 1D wavelet transform into different frequency sub-bands, suppressing the low-frequency subband, and finally reconstructing the mammogram from the subbands containing only significant high frequencies features. The significant features are obtained by multiscale products. Preliminary results indicate that the proposed scheme is better in suppressing the background and detecting the microcalcification clusters than any other wavelet decomposition methods.
Critical path selection under NBTI/PBTI aging for adaptive frequency tuning

Critical Paths Monitoring is an efficient technique to overcome transistor aging. A methodology to select critical paths to be monitored at design phase is proposed. A spatial correlation approach is used to perform Statistical Timing Analysis at design phase. Critical paths are selected for various aging workload profiles to avoid worst-case assumptions. The results show that up to 16x less paths are selected compared to path selection using worst-case aging. Moreover, the selected paths match well with those selected using spatial correlation extracted from final circuit layout.
Incrementally learned Mixture Models for GNSS Localization

GNSS localization is an important part of today's autonomous systems, although it suffers from non-Gaussian errors caused by non-line-of-sight effects. Recent methods are able to mitigate these effects by including the corresponding distributions in the sensor fusion algorithm. However, these approaches require prior knowledge about the sensor's distribution, which is often not available. We introduce a novel sensor fusion algorithm based on variational Bayesian inference, that is able to approximate the true distribution with a Gaussian mixture model and to learn its parametrization online. The proposed Incremental Variational Mixture algorithm automatically adapts the number of mixture components to the complexity of the measurement's error distribution. We compare the proposed algorithm against current state-of-the-art approaches using a collection of open access real world datasets and demonstrate its superior localization accuracy.
Resolving The Generation of Starburst Winds in Galaxy Mergers

We study galaxy superwinds driven in major mergers, using pc-scale resolution simulations with detailed models for stellar feedback that can self-consistently follow the generation of winds. The models include molecular cooling, star formation at high densities in giant molecular clouds, and gas recycling and feedback from supernovae (I and II), stellar winds and radiation pressure. We study mergers of systems from Small-Magellanic-Cloud-like dwarfs and Milky Way analogues to z ∼ 2 starburst discs. Multiphase superwinds are generated in all passages, with outflow rates up to ∼1000 M⊙ yr^(−1). However, the wind mass-loading efficiency (outflow rate divided by star formation rate, SFR) is similar to that in the isolated galaxy counterparts of each merger: it depends more on global galaxy properties (mass, size and escape velocity) than on the dynamical state or orbital parameters of the merger. Winds tend to be bi- or unipolar, but multiple ‘events’ build up complex morphologies with overlapping, differently oriented bubbles and shells at a range of radii. The winds have complex velocity and phase structure, with material at a range of speeds up to ∼1000 km s^(−1) (forming a Hubble-like flow), and a mix of molecular, ionized and hot gas that depends on galaxy properties. We examine how these different phases are connected to different feedback mechanisms. These simulations resolve a problem in some ‘subgrid’ models, where simple wind prescriptions can dramatically suppress merger-induced starbursts, often making it impossible to form Ultra Luminous Infrared Galaxies (ULIRGs). Despite large mass-loading factors (≳10–20) in the winds simulated here, the peak SFRs are comparable to those in ‘no wind’ simulations. Wind acceleration does not act equally, so cold dense gas can still lose angular momentum and form stars, while these stars blow out gas that would not have participated in the starburst in the first place. Considerable wind material is not unbound, and falls back on the disc at later times post-merger, leading to higher post-starburst SFRs in the presence of stellar feedback. We consider different simulation numerical methods and their effects on the wind phase structure; while most results are converged, we find that the existence of small clumps in the outflow at large distances from the galaxy is quite sensitive to the methodology.
fgCAPTCHA: Genetically Optimized Face Image CAPTCHA 5

The increasing use of smartphones, tablets, and other mobile devices poses a significant challenge in providing effective online security. CAPTCHAs, tests for distinguishing human and computer users, have traditionally been popular; however, they face particular difficulties in a modern mobile environment because most of them rely on keyboard input and have language dependencies. This paper proposes a novel image-based CAPTCHA that combines the touch-based input methods favored by mobile devices with genetically optimized face detection tests to provide a solution that is simple for humans to solve, ready for worldwide use, and provides a high level of security by being resilient to automated computer attacks. In extensive testing involving over 2600 users and 40000 CAPTCHA tests, fgCAPTCHA demonstrates a very high human success rate while ensuring a 0% attack rate using three well-known face detection algorithms.
Distinct phases of free α‐synuclein—A Monte Carlo study

The α‐synuclein protein (αS), implicated in Parkinson's disease, shows conformational versatility. It aggregates into β‐sheet‐rich fibrils, occurs in helical membrane‐bound forms, is disordered as a free monomer, and has recently been suggested to have a folded helical tetramer as its main physiological form. Here, we use implicit solvent all‐atom Monte Carlo methods to explore the conformational ensemble sampled by the free αS monomer. We analyze secondary structure propensities, size, and topological properties and compare with existing experimental data. Our study suggests that free αS has two distinct phases. One phase has the expected disordered character. The other phase also shows large conformational variability. However, in this phase, the β‐strand content is substantial, and the backbone fold shows statistical similarities with that in αS fibrils. Presence of this phase is consistent with data from low‐temperature experiments. Conversion of disordered αS to this fibril‐like form requires the crossing of a rather large apparent free‐energy barrier. Proteins © 2012 Wiley Periodicals, Inc.
Evaluation of Candidate Protocols for IP Flow Information Export (IPFIX)

This document contains an evaluation of the five candidate protocols for an IP Flow Information Export (IPFIX) protocol, based on the requirements document produced by the IPFIX Working Group. The protocols are characterized and grouped in broad categories, and evaluated against specific requirements. Finally, a recommendation is made to select the NetFlow v9 protocol as the basis for the IPFIX specification. This memo provides information for the Internet community.
Online cache modeling for commodity multicore processors

Modern chip-level multiprocessors (CMPs) contain multiple processor cores sharing a common last-level cache, memory interconnects, and other hardware resources. Workloads running on separate cores compete for these resources, often resulting in highly-variable performance. To improve fairness and performance, it is helpful to co-schedule workloads having minimal cache and other forms of resource contention. In this work, we develop several cache modeling techniques to help make informed resource management decisions. Using only commonly-available performance counters on existing processors, we introduce an efficient online technique for estimating the cache occupancies of software threads. We derive an analytical model that considers the impact of set-associativity, line replacement policy, and memory locality effects. We demonstrate the effectiveness of occupancy estimation with a series of CMP simulations using SPEC benchmarks. Our occupancy estimation technique is currently being used to develop online utility functions, such as miss-ratio curves (MRCs), which capture performance impacts as a function of resource usage. We are leveraging both online cache occupancy estimation and MRC construction in our ongoing studies of cache-aware scheduling.
Improving SCL Model for Sentiment-Transfer Learning

In recent years, Structural Correspondence Learning (SCL) is becoming one of the most promising techniques for sentiment-transfer learning. However, SCL model treats each feature as well as each instance by an equivalent-weight strategy. To address the two issues effectively, we proposed a weighted SCL model (W-SCL), which weights the features as well as the instances. More specifically, W-SCL assigns a smaller weight to high-frequency domain-specific (HFDS) features and assigns a larger weight to instances with the same label as the involved pivot feature. The experimental results indicate that proposed W-SCL model could overcome the adverse influence of HFDS features, and leverage knowledge from labels of instances and pivot features.
Real-Time Multiple Sound Source Localization and Counting Using a Circular Microphone Array

In this work, a multiple sound source localization and counting method is presented, that imposes relaxed sparsity constraints on the source signals. A uniform circular microphone array is used to overcome the ambiguities of linear arrays, however the underlying concepts (sparse component analysis and matching pursuit-based operation on the histogram of estimates) are applicable to any microphone array topology. Our method is based on detecting time-frequency (TF) zones where one source is dominant over the others. Using appropriately selected TF components in these “single-source” zones, the proposed method jointly estimates the number of active sources and their corresponding directions of arrival (DOAs) by applying a matching pursuit-based approach to the histogram of DOA estimates. The method is shown to have excellent performance for DOA estimation and source counting, and to be highly suitable for real-time applications due to its low complexity. Through simulations (in various signal-to-noise ratio conditions and reverberant environments) and real environment experiments, we indicate that our method outperforms other state-of-the-art DOA and source counting methods in terms of accuracy, while being significantly more efficient in terms of computational complexity.
Analysis of Chinese Tourists in Japan by Text Mining of a Hotel Portal Site

With an increasingly large number of Chinese tourists in Japan, the hotel industry is in need of an affordable market research tool that does not rely on expensive and time-consuming surveys or interviews. Because this problem is real and relevant to the hotel industry in Japan, and otherwise completely unexplored in other studies, we have extracted a list of potential keywords from Chinese reviews of Japanese hotels in the hotel portal site Ctrip1 using a mathematical model to then use them in a sentiment analysis with a machine learning classifier. While most studies that use information collected from the internet use pre-existing data analysis tools, in our study, we designed the mathematical model to have the highest possible performing results in classification, while also exploring on the potential business implications these may have.
Consumer Grade Brain Sensing for Emotion Recognition

For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets has been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. In addition, 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.
Monitoring Network Evolution using MDL

Given publication titles and authors, what can we say about the evolution of scientific topics and communities over time? Which communities shrunk, which emerged, and which split, over time? And, when in time were the turning points? We propose TimeFall, which can automatically answer these questions given a social network/graph that evolves over time. The main novelty of the proposed approach is that it needs no user-defined parameters, relying instead on the principle of minimum description length (MDL), to extract the communities, and to find good cut-points in time when communities change abruptly: a cut-point is good, if it leads to shorter data description. We illustrate our algorithm on synthetic and large real datasets, and we show that the results of the TimeFall agree with human intuition.
Payment Instrument Characteristics : A Repertory Grid Analysis Full Paper

Over the last decade, we have witnessed payment innovations that fundamentally have changed the ways we pay. Payment innovations, such as mobile payments and on-line banking, include characteristics or features that are essential to understand if we want to know how and why payers choose among payment innovations. Using the Repertory Grid technique to explore 15 payers’ perception of six payment instruments, including coins, banknotes, debit cards, credit cards, mobile payments, and on-line banking, we identify 16 payment characteristics. The characteristics aggregate seventy-six unique features. Many of the characteristics and one of the categories are completely novel and unaccounted for in previous works.
Multiple Parenting Phylogeny Relationships in Digital Images

Recently, several studies have been concerned with modeling the parenthood relationships between near duplicates in a set of images. Two images share a parenthood relationship if one is obtained by applying transformations to the other. However, this is not the only form of parenting that can exist among images. An image might be a composition created through the combination of the semantic information existent in two or more source images, establishing a relationship between the sources and the composite. The problem of identifying these relations in a set containing near-duplicate subsets of source and composition images is referred to as multiple parenting phylogeny. Thus far, researchers tackled this problem with a three-step solution: 1) separation of near-duplicate groups; 2) classification of the relations between the groups; and 3) identification of the images used to create the original composition. In this work, we extend upon this framework by introducing key improvements, such as better identification of when two images share content, and improved ways to compare this content. In addition, we also introduce a new realistic professionally created data set of compositions involving multiple parenting relationships. The method we present in this paper is properly evaluated through quantitative metrics, established for assessing the accuracy in finding multiple parenting relationships. Finally, we discuss some particularities of the framework, such as the importance of an accurate reconstruction of phylogenies and the method's behavior when dealing with more complex compositions.
Gene Expression Analysis of Energy Metabolism Mutants of Desulfovibrio vulgaris Hildenborough Indicates an Important Role for Alcohol Dehydrogenase

ABSTRACT Comparison of the proteomes of the wild-type and Fe-only hydrogenase mutant strains of Desulfovibrio vulgaris Hildenborough, grown in lactate-sulfate (LS) medium, indicated the near absence of open reading frame 2977 (ORF2977)-coded alcohol dehydrogenase in the hyd mutant. Hybridization of labeled cDNA to a macroarray of 145 PCR-amplified D. vulgaris genes encoding proteins active in energy metabolism indicated that the adh gene was among the most highly expressed in wild-type cells grown in LS medium. Relative to the wild type, expression of the adh gene was strongly downregulated in the hyd mutant, in agreement with the proteomic data. Expression was upregulated in ethanol-grown wild-type cells. An adh mutant was constructed and found to be incapable of growth in media in which ethanol was both the carbon source and electron donor for sulfate reduction or was only the carbon source, with hydrogen serving as electron donor. The hyd mutant also grew poorly on ethanol, in agreement with its low level of adh gene expression. The adh mutant grew to a lower final cell density on LS medium than the wild type. These results, as well as the high level of expression of adh in wild-type cells on media in which lactate, pyruvate, formate, or hydrogen served as the sole electron donor for sulfate reduction, indicate that ORF2977 Adh contributes to the energy metabolism of D. vulgaris under a wide variety of metabolic conditions. A hydrogen cycling mechanism is proposed in which protons and electrons originating from cytoplasmic ethanol oxidation by ORF2977 Adh are converted to hydrogen or hydrogen equivalents, possibly by a putative H2-heterodisulfide oxidoreductase complex, which is then oxidized by periplasmic Fe-only hydrogenase to generate a proton gradient.
Evaluating the design of inclusive interfaces by simulation

We have developed a simulator to help with the design and evaluation of assistive interfaces. The simulator can predict possible interaction patterns when undertaking a task using a variety of input devices, and estimate the time to complete the task in the presence of different dis-abilities. In this paper, we have presented a study to evaluate the simulator by considering a representative application being used by able-bodied, visually impaired and mobility impaired people. The simulator predicted task completion times for all three groups with statistically significant accuracy. The simulator also predicted the effects of different interface designs on task completion time accurately.
Current Correlations in a Quantum Dot Ring: A Role of Quantum Interference

We present studies of the electron transport and circular currents induced by the bias voltage and the magnetic flux threading a ring of three quantum dots coupled with two electrodes. Quantum interference of electron waves passing through the states with opposite chirality plays a relevant role in transport, where one can observe Fano resonance with destructive interference. The quantum interference effect is quantitatively described by local bond currents and their correlation functions. Fluctuations of the transport current are characterized by the Lesovik formula for the shot noise, which is a composition of the bond current correlation functions. In the presence of circular currents, the cross-correlation of the bond currents can be very large, but it is negative and compensates for the large positive auto-correlation functions.
Corona: System Implications of Emerging Nanophotonic Technology

We expect that many-core microprocessors will push performance per chip from the 10 gigaflop to the 10 teraflop range in the coming decade. To support this increased performance, memory and inter-core bandwidths will also have to scale by orders of magnitude. Pin limitations, the energy cost of electrical signaling, and the non-scalability of chip-length global wires are significant bandwidth impediments. Recent developments in silicon nanophotonic technology have the potential to meet these off- and on-stack bandwidth requirements at acceptable power levels. Corona is a 3 D many-core architecture that uses nanophotonic communication for both inter-core communication and off-stack communication to memory or I/O devices. Its peak floating-point performance is 10 teraflops. Dense wavelength division multiplexed optically connected memory modules provide 10 terabyte per second memory bandwidth. A photonic crossbar fully interconnects its 256 low-power multithreaded cores at 20 terabyte per second bandwidth. We have simulated a 1024 thread Corona system running synthetic benchmarks and scaled versions of the SPLASH-2 benchmark suite. We believe that in comparison with an electrically-connected many-core alternative that uses the same on-stack interconnect power, Corona can provide 2 to 6 times more performance on many memory intensive workloads, while simultaneously reducing power.