Miracle-dz/UniFAR-FTUs huggingface dataset:
Separating Background Texture, Image Structure in Mammograms

There have been several approaches to the classification of texture in images. Most approaches will take certain local attributes or features into account and base the classification on these measures. In here we demonstrate the use of a statistical approach to separate the structure and texture background present in images. Modelling is based on normal images which only contain a texture background. The resulting model is applied to images which contain abnormal image structures as well as a normal texture background. Especially for mammographic (and other medical application) this can provide useful information which can be used as a pre-processing tool to obtain the structures present in the image and at the same time get a robust classification of the background texture.
Optimal Multi-Antenna Transmission With Multiple Power Constraints

We determine the capacity-optimal transmission strategy for a multiple-input-multiple-output (MIMO) Gaussian channel under multiple power constraints, namely joint sum power constraint (SPC), per group power constraints (PGPC), and per antenna power constraints (PAPC). First, we focus on cases where we can analytically determine the optimal transmit strategy under joint SPC-PGPC-PAPC. We obtain results for the following cases: 1) <inline-formula> <tex-math notation="LaTeX">$n_{t} \times 1$ </tex-math></inline-formula> multiple-input-single-output (MISO); 2) MIMO channel with full column rank and full rank optimal covariance matrix; and 3) <inline-formula> <tex-math notation="LaTeX">$2 \times n_{r}$ </tex-math></inline-formula> MIMO channel. These results generalize some recent results for the special cases of PAPC only and joint SPC-PAPC. Then, we propose a projected factored gradient descent (PFGD) algorithm for the general MIMO Gaussian channel under joint SPC-PGPC-PAPC including the possibility of additional rank constraints. This algorithm matches the solution of standard convex optimization tools with lower complexity. The algorithm also overcomes the limitations of existing algorithms, in terms of accuracy and applicability to low rank channels.
Aberrant Cx26 hemichannels and keratitis-ichthyosis-deafness syndrome: insights into syndromic hearing loss

Mutation of the GJB2 gene, which encodes the connexin 26 (Cx26) gap junction (GJ) protein, is the most common cause of hereditary, sensorineural hearing loss. Cx26 is not expressed in hair cells, but is widely expressed throughout the non-sensory epithelial cells of the cochlea. Most GJB2 mutations produce non-syndromic deafness, but a subset produces syndromic deafness in which profound hearing loss is accompanied by a diverse array of infectious and neoplastic cutaneous disorders that can be fatal. Although GJ channels, which are assembled by the docking of two, so-called hemichannels (HCs), have been the main focus of deafness-associated disease models, it is now evident that the HCs themselves can function in the absence of docking and contribute to signaling across the cell membrane as a novel class of ion channel. A notable feature of syndromic deafness mutants is that the HCs exhibit aberrant behaviors providing a plausible basis for disease that is associated with excessive or altered contributions of Cx26 HCs that, in turn, lead to compromised cell integrity. Here we discuss some of the aberrant Cx26 HC properties that have been described for mutants associated with keratitis-ichthyosis-deafness (KID) syndrome, a particularly severe Cx26-associated syndrome, which shed light on genotype-phenotype relationships and causes underlying cochlear dysfunction.
Class and the Classroom: The Role of Individual- and School-Level Socioeconomic Factors in Predicting College Students’ Academic Behaviors

This study examines how, for emerging adults attending residential colleges, family incomes and the socioeconomic status (SES) composition of high schools are jointly associated with academic behaviors in college. Using a one-time survey, daily surveys, and additional data collection on high school SES composition, this study measured 221 college students’ (17–25 years old) SES backgrounds and academic behaviors. Findings indicated that three academic behaviors (study time, in-class engagement, and help-seeking) were predicted by an interaction between family income and high school context. Among students who attended high schools that serve many low-income students, higher family income was significantly associated with more beneficial academic behaviors in college; among students who attended high schools that serve few low-income students, there was no association between family income and academic behaviors. Results indicate that colleges may need to be especially prepared to support students from lower income families who matriculated from lower SES high schools.
Training Support Vector Machines using Frank-Wolfe Optimization Methods

Training a support vector machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale datasets. Traditional optimization methods cannot be directly applied in these cases, mainly due to memory restrictions. By adopting a slightly different objective function and under mild conditions on the kernel used within the model, efficient algorithms to train SVMs have been devised under the name of core vector machines (CVMs). This framework exploits the equivalence of the resulting learning problem with the task of building a minimal enclosing ball (MEB) problem in a feature space, where data is implicitly embedded by a kernel function. In this paper, we improve on the CVM approach by proposing two novel methods to build SVMs based on the Frank–Wolfe algorithm, recently revisited as a fast method to approximate the solution of a MEB problem. In contrast to CVMs, our algorithms do not require to compute the solutions of a sequence of increasingly complex QPs and are defined by using only analytic optimization steps. Experiments on a large collection of datasets show that our methods scale better than CVMs in most cases, sometimes at the price of a slightly lower accuracy. As CVMs, the proposed methods can be easily extended to machine learning problems other than binary classification. However, effective classifiers are also obtained using kernels which do not satisfy the condition required by CVMs, and thus our methods can be used for a wider set of problems.
FPGA implementation of the DCD algorithm

This paper describes an experimental implementation of the Dichotomous Coordinate Descent (DCD) algorithm on a Xilinx Virtex-II Pro FPGA. The DCD algorithm is a computationally-efficient method for solving linear systems of equations – a common task in the field of digital signal processing. Our implementation is described, and our implementation results and resource requirement figures are stated. It is shown that the DCD algorithm forms a highly efficient method for solving linear systems of equations on FPGA, requiring 650 logic slices for a core to solve systems of equations of size N=50.
Epileptic Seizure Detection Using Empirical Mode Decomposition

In this paper, we attempt to analyze the performance of the Empirical Mode Decomposition (EMD) for discriminating epileptic seizure data from the normal data. The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and nonstationary time series. The main idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode functions (IMFs). EMD is an adaptive decomposition since the extracted information is obtained directly from the original signal. By utilizing this method to obtain the features of normal and epileptic seizure signals, we compare them with traditional features such as wavelet coefficients through two classifiers. Our results confirmed that our proposed features could potentially be used to distinguish normal from seizure data with success rate up to 95.42%.
Mothers’ Perceptions of the Phenomenon of Bullying among Young Children in South Korea

This study aimed to investigate mothers’ different perspectives on bullying in early childhood. Twelve mothers having children under eight years old were interviewed in South Korea. All the interviews were transcribed in Korean and analyzed using Nvivo. The constant comparison method was used to analyze the data. The results showed six themes consisting of categories: (1) concept of bullying (2) difficulty in defining bullying in early childhood, (3) difficulty in telling other mothers about bullying, (4) children who do not reveal their experiences, (5) ways to be aware of bullying, and (6) mothers’ concern. Categories were sometimes divided into subcategories. Findings showed that mothers seemed to view bullying differently, and that relationships among them contributed to differences in their perspectives on bullying. Mothers’ relationships also interacted with children’s relationships. Children were unlikely to tell their victimization experiences, due to certain reasons. These findings can contribute to understanding the nature of bullying in early childhood, increasing the social awareness of bullying among young children, and emphasizing the need for intervention/prevention programs.
A dependence graph-based representation for test coverage analysis of object-oriented programs

Test coverage analysis is used to determine the extent to which different features of interest are exercised by a given test suite. For object-oriented programs, it involves determining the extent to which features such as inheritance, polymorphism, dynamic binding, etc. are exercised. As object-oriented coverage analysis using only source code or object code is difficult and inefficient, the source code is converted to a suitable intermediate representation for subsequent analysis. We propose a dependence graph-based representation for object-oriented programs, named Call-based Object-Oriented System Dependence Graph (COSDG). COSDG captures the important object-oriented features, provides details of method visibility in a derived class, and differentiates various calling contexts with different type of method call edges: simple, inherited, and polymorphic. We also propose an algorithm to construct COSDG incrementally. Though COSDG has been developed primarily to aid test coverage analysis, it can be used in a variety of other software engineering applications also.
Optimal Disinflation Under Learning

We model transitional dynamics that emerge after the adoption of a new monetary-policy rule. We assume that private agents learn about the new policy via Bayesian updating, and we study how learning affects the nature of the transition and choice of a new rule. The model endogenously generates time-varying volatility during the transition. Managing this volatility is the central bank's main challenge. The optimal policy depends on subtle features of the private sector's prior. Nevertheless, two robust conclusions emerge from our examples. First, the central bank can adjust target inflation freely without triggering high volatility. Second, none of our examples rationalizes a gradual reduction in inflation. On the contrary, inflation falls sharply at impact, overshoots the new target, and converges from below.
On a decentralized approach to tree construction in hybrid P2P networks

The client-server architecture widely adopted on the Internet is not adequate to meet the ever-increasing user loads and bandwidth demands in live streaming systems especially for multimedia content delivery. Peer-to-peer (P2P) overlay networks provide excellent system scalability and high resource utilization, which make it an attractive solution to this problem. We consider a hybrid hierarchical P2P overlay network that consists of both super and normal peers to support live streaming applications. This architecture is built upon a tree-structured network of super peers, which organize normal peers into clusters. The tree construction process has a significant impact on the overall system performance. We formulate a specific type of problem, max-minTC, to maximize the minimum node throughput in tree construction, where the system's stream rate is optimized by constructing an efficient spanning tree among super peers. We present a decentralized approach where super peers run the same algorithm in parallel to derive a tree from an identical database describing the topology of the streaming system. This approach is able to quickly converge to a new tree upon the detection of any topological changes in super peers. The performance superiority of the proposed solution is illustrated by extensive simulations on a large set of simulated networks of various sizes from small to large scales in comparison with other methods.
Set-Expression Based Method for Effective Privacy Preservation

Anonymization is proposed to alleviate the problem of privacy disclosure in recent years. Previous approaches typically generalize the specific values in the original data table to achieve anonymization. However, these solutions suffer from information loss in different degrees. In this paper, we propose the concept of set-expression, which causes less data distortion and equipments the anonymized table with better aggregate query answerability. Furthermore we propose a greedy algorithm and conduct a set of extensive experiments to show the advantages of our approach. We also introduce a novel Information Loss Metric to measure the quality of our proposed method.
Single Event Upset Detection and Correction

This paper proposes a low cost solution to detect and correct a transient faults in registers of a design. The proposed method realizes a single-event upset detection and correction (SEU-DC) technique. The detection and correction of SEU in registers of a design is difficult and requires some efficient approaches without significant area overhead and timing penalty. Furthermore, the proposed method is based on the traditional parity codes to detect and correct a single-bit error without significant increase in area overhead. We conducted experiments on the MCNC benchmark circuits which show that present approach has very low area overhead and timing penalty as compared to hardware redundancy approach.
Fatty liver and subcutaneous edema in a free-living bottlenose dolphin (Tursiops truncatus, Montagu 1821) from the Adriatic Sea; light- and electron-microscopical study

Research was carried out on a 21-year-old, 288 cm-long free-living grand multigravida female bottlenose dolphin (Tursiops truncatus, Montagu 1821) with mass of 214 kg. The animal died on 8 October 1997 in the Adriatic Sea, near Sibenik, Croatia. Prior to death she showed some neurological symptoms. For control and comparison we used the carcasses and tissues of two presumably clinically healthy dolphins. Autopsy showed massive edema in the subcutaneous tissues (between blubber and musculature) on the dorsal side of the body of the diseased subject. The fluid in intercellular tissue spaces was clear and light-yellowish. Larger amounts of the same fluid were found in serous sacs of the animal, as well as slight brain edema. The most prominent structural change was found in the liver, which was
Qualitative evaluation: A critical and interpretative complementary approach to improve health programs and services

This short essay aims at commenting on the origin, development, rationale, and main characteristics of qualitative evaluation (QE), emphasizing the value of this methodological tool to evaluate health programs and services. During the past decades, different approaches have come to light proposing complementary alternatives to appraise the performance of public health programs, mainly focusing on the implementation process involved rather than on measuring the impact of such actions. QE is an alternative tool that can be used to illustrate and understand the process faced when executing health programs. It can also lead to useful suggestions to modify its implementation from the stakeholders’ perspectives, as it uses a qualitative approach that considers participants as reflective subjects, generators of meanings. This implies that beneficiaries become involved in an active manner in the evaluated phenomena with the aim of improving the health programs or services that they receive. With this work we want to encourage evaluators in the field of public health to consider the use of QE as a complementary tool for program evaluation to be able to identify areas of opportunity to improve programs’ implementation processes from the perspective of intended beneficiaries.
Dynamic thread and data mapping for NoC based CMPs

Thread mapping and data mapping are two important problems in the context of NoC (network-on-chip) based CMPs (chip multiprocessors). While a compiler can determine suitable mappings for data and threads, such static mappings may not work well for multithreaded applications that go through different execution phases during their execution, each phase with potentially different data access patterns than others. Instead, a dynamic mapping strategy, if its overheads can be kept low, may be a more promising option. In this work, we present dynamic (runtime) thread and data mappings for NoC based CMPs. The goal of these mappings is to reduce the distance between the location of the core that requests data and the core whose local memory contains that requested data. In our experiments, we evaluate our proposed thread mapping and data mapping in isolation as well as in an integrated manner.
Effective Dispatching for Simultaneous Multi-Threading (SMT) Processors by Capping Per-Thread Resource Utilization

Simultaneous multithreading (SMT) provides a technique to improve resource utilization by sharing key datapath components among multiple independent threads. When critical resources are shared by multiple threads, effective use of these resources proves to be the most important factor in fully exploiting the system potential. Allowing any of the threads to overwhelm these shared resources not only leads to unfair thread processing but may also result in severely degraded overall performance. How to prevent idling threads from clogging the critical resources in the pipeline becomes a must in sustaining system performance. In this paper, we show that, by simply setting a cap on the number of the critical Issue Queue (IQ) entries each thread is allowed to occupy, the system performance is easily enhanced by a significant margin. An even more pronounced advantage of the proposed technique over other advanced dispatching algorithms is that the performance gain is obtained with very minimal additional hardware required.
Style-aware robust color transfer

Transferring features, such as light and colors, between input and reference images is the main objective of color transfer methods. Current state-of-the-art methods focus mainly on the complete transfer of the light and color distributions. However, they do not successfully grasp specific light and color variations in image styles. In this paper, we propose a local method for carrying out a transfer of style between two images. Our method partitions both images to Gaussian distributed clusters by considering their main style features. These features are automatically determined by the classification step of our algorithm. Moreover, we present several novel policies for input/reference cluster mapping, which have not been tackled so far by previous methods. To complete the style transfer, for each pair of corresponding clusters, we apply a parametric color transfer method and a local chromatic adaptation transform. Results, subjective user evaluation as well as objective evaluation show that the proposed method obtains visually pleasing and artifact-free images, respecting the reference style.
Long-Term Adherence to Antiretroviral Treatment and Program Drop-Out in a High-Risk Urban Setting in Sub-Saharan Africa: A Prospective Cohort Study

Background Seventy percent of urban populations in sub-Saharan Africa live in slums. Sustaining HIV patients in these high-risk and highly mobile settings is a major future challenge. This study seeks to assess program retention and to find determinants for low adherence to antiretroviral treatment (ART) and drop-out from an established HIV/ART program in Kibera, Nairobi, one of Africa's largest informal urban settlements. Methods and Findings A prospective open cohort study of 800 patients was performed at the African Medical Research Foundation (AMREF) clinic in the Kibera slum. Adherence to ART and drop-out from the ART program were independent outcomes. Two different adherence measures were used: (1) “dose adherence” (the proportion of a prescribed dose taken over the past 4 days) and (2) “adherence index” (based on three adherence questions covering dosing, timing and special instructions). Drop-out from the program was calculated based on clinic appointment dates and number of prescribed doses, and a patient was defined as being lost to follow-up if over 90 days had expired since the last prescribed dose. More than one third of patients were non-adherent when all three aspects of adherence – dosing, timing and special instructions – were taken into account. Multivariate logistic regression revealed that not disclosing HIV status, having a low level of education, living below the poverty limit (US$ 2/day) and not having a treatment buddy were significant predictors for non-adherence. Additionally, one quarter of patients dropped out for more than 90 days after the last prescribed ART dose. Not having a treatment buddy was associated with increased risk for drop-out (hazard ratio 1.4, 95% CI = 1.0–1.9). Conclusion These findings point to the dilemma of trying to sustain a growing number of people on life-long ART in conditions where prevailing stigma, poverty and food shortages threatens the long-term success of HIV treatment.
Exploring enterprise mobility: Lessons from the field

The mobile phone has received global attention primarily as a personal consumer technology. However, we believe that mobile information technology in general will play a significant role in organisational efforts to innovate current practices and have significant economic impact. Enterprise mobility signals new ways of managing how people work together using mobile information technology and will form an integral part of the efforts to improve the efficiency and effectiveness of information work. This belief is, however, not reflected in the current selection of books and collections exploring the issue of enterprise mobility. The aim of this paper is to highlight some of the key challenges in the application of mobile information technology to improve organisational efficiency. This is accomplished through comparing and contrasting findings from a selection of 11 empirical studies of enterprise mobility with information technology conducted between 2001 and 2007. The paper argues that the debate so far has largely failed to embed glowing accounts for technological potential in a sound discussion of organisational realities. In particular, there has been a lack of balanced accounts of the implicit and explicit trade-offs involved in mobilising the interaction between members of the workforce.