Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust modal regression with direct gradient approximation of modal regression risk

Modal regression is aimed at estimating the global mode (i.e., global maximum) of the conditional density function of the output variable given input variables, and has led to regression methods robust against a wide-range of noises. A typical approach for modal regression takes a two-step approach of firstly approximating the modal regression risk (MRR) and of secondly maximizing the approximated MRR with some gradient method. However, this two-step approach can be suboptimal in gradient-based maximization methods because a good MRR approximator does not necessarily give a good gradient approximator of MRR. In this paper, we take a novel approach of directly approximating the gradient of MRR in modal regression. Based on the direct approach, we first propose a modal regression method with reproducing kernels where a new update rule to estimate the conditional mode is derived based on a fixed-point method. Then, the derived update rule is theoretically investigated. Furthermore, since our direct approach is compatible with recent sophisticated stochastic gradient methods (e.g., Adam), another modal regression method is also proposed based on neural networks. Finally, the superior performance of the proposed methods is demonstrated on various artificial and benchmark datasets.
Paper presented at the 10th Nordic-Baltic Conference on Biomedical Imaging: Field: A Program for Simulating Ultrasound Systems

A program for the simulation of ultrasound systems is presented. It is based on the Tupholme-Stepanishen method, and is fast because of the use of a fareld approximation. Any kind of transducer geometry and excitation can be simulated, and both pulse-echo and continuous wave elds can be calculated for both transmit and pulse-echo. Dynamic apodization and focusing are handled through time lines, and different focusing schemes can be simulated. The versatility of the program is ensured by interfacing it to Matlab. All routines are called directly from Matlab, and all Matlab features can be used. This makes it possible to simulate all types of ultrasound imaging systems.
Differentially Private Survey Research ∗

Survey researchers have long sought to protect the privacy of their respondents via de-identiﬁcation (removing names and other directly identifying information) before sharing data. Although these procedures can help, recent research demonstrates that they fail to protect respondents from intentional re-identiﬁcation attacks, a problem that threatens to undermine vast survey enterprises in academia, government, and industry. This is especially a problem in political science because political beliefs are not merely the subject of our scholarship; they are key information respondents seek to keep private. We conﬁrm the problem in practice by easily re-identifying a sensitive survey about a recent abortion referendum. We build on the concept of “differen-tial privacy” to offer new survey research data sharing procedures with mathematical guarantees for protecting respondent privacy and statistical validity guarantees for social scientists analyzing differentially private data. The cost of these new procedures is larger standard errors, which can be overcome with somewhat larger sample sizes.
Two-Hole Bound States from an Effective Field Theory for Magnons and Holes in an Antiferromagnet on the Honeycomb Lattice

In this thesis a systematic low-energy effective field theory for magnons and holes in an antiferromagnet on the honeycomb lattice is constructed analogous to baryon chiral perturbation theory (BχPT). The Hubbard model serves as an underlying microscopic model. We show that the leading action of the pure magnon sector is the same as in the square lattice case. Using the effective theory, we derive the one-magnon exchange potentials. With these potentials, we investigate the Schrödinger equation describing the relative motion of two holes in an otherwise undoped antiferromagnet. We derive the radial solution of this Schrödinger equation for a special case analytically and find that the magnon-mediated forces lead to two-hole bound states under a condition on the low-energy constants. Also the weak coupling limit of the Hubbard model is investigated. In this context we derive the energy-momentum relation in the zero coupling limit, where massless Dirac fermions arise. In addition to deriving their velocity, we construct the leading terms of the corresponding effective Lagrangian.
Tax Audit, Investigation, and Tax Evasion

The issue of tax evasion continues to attract the attention of government, policymakers and researchers. Several studies have been conducted on how tax evasion can be curbed to increase government revenue. The broad objective of this paper was to determine the impact of tax audit and investigation on tax evasion. A survey research design was adopted with responses obtained through a well-structured questionnaire administered to staff of revenue generating agencies in Bayelsa State, Nigeria. The Ordered Logistic Regression technique was applied to test the hypotheses of the study. Findings from the study revealed that tax audit in the form of desk audit, field audit and back-duty audit exert a significant negative influence on tax evasion. It was also revealed that the explanatory power of tax investigation significantly and negatively impacts on tax evasion. The study, therefore, recommended among others that tax audit should be carried out on a routine basis to serve as a check and a preventive tool for tax evasion and taxpayers should be adequately profiled and selected for audit to avoid inefficiencies and audit delays.
Measuring the Money Demand Function Stability in Bahrain

The study aims at analyzing the behavior of the real narrow money demand function (M1) for Bahrain based on annual data for the period (1991-2013), by adopting Cointegration, Error Correction Model and Granger Causality techniques. The main findings show that the demand function for real money is stable in the long run; in addition, there is a cointegration between real money demand, real income and inflation rate during the study period. The study concludes that Central Bank of Bahrain could effectively use the supply of money as an instrument of monetary policy in order to achieve price stability and promote economic growth.
The Parent Populations of 6 groups identified from Chemical Tagging in the Solar neighborhood

We estimate the size and distribution of the parent populations for the 6 largest (at least 20 stars in the Solar neighborhood) chemical groups identified in the Chemical Tagging experiment by Mitschang et al. 2014. Stars in the abundance groups tend to lie near a boundary in angular momentum versus eccentricity space where the probability is highest for a star to be found in the Solar neighborhood and where orbits have apocenter approximately equal to the Sun’s galactocentric radius. Assuming that the parent populations are uniformly distributed at all azimuthal angles in the Galaxy, we estimate that the parent populations of these abundance groups contain at least 200,000 members. The spread in angular momentum of the groups implies that the assumption of an even azimuthal distribution only fails for the two youngest groups and only for the highest angular momentum stars in them. The parent populations of three thin disk groups have narrow angular momentum distributions, but tails in the eccentricity and angular momentum distributions suggest that only a small fraction of stars have migrated and increased in eccentricity. In contrast, the parent populations of the thick disk groups exhibit both wide angular momentum and eccentricity distributions implying that both heating and radial migration has taken place.
YCbCr Associated Digital Image Watermarking in the Discrete Cosine Transform Domain

The development of a digital watermarking scheme associated with YCbCr color space in the Discrete Cosine Transform (DCT) Domain is presented. The scheme embeds a rgb color watermark in a rgb host image. The Watermark is pre-processed such that it’s converted from rgb color space to index image and then to binary bits. The watermark embedding is done only in the Y component of the host in YCbCr color space. The Y component is sub-divided into 8-by-8 blocks of pixels which are transformed to frequency domain using Discrete Cosine Transform (DCT). Hosting Pixels in the DCT 8-by-8 block are classified into regions: Low-Frequency, Middle-Frequency, High-Frequency, Low and Middle-Frequency and the whole 8-by-8 block. The watermarking scheme was subjected to image compression attack and Gaussian noise attack and found to be robust to both attacks. A coding constant 5  a is found to produce satisfactory compromise between imperceptibility and robustness. Highest robustness is recorded with watermarking in the Low-Frequency region. This study confirmed that information bits embedded in the Low-Frequency region are more immune to noise and interference. For Compression Quality factor Q less than 50%, the robustness is found to be poor. For Gaussian noise variance greater than 0.002, the robustness is found to be poor. The lower the watermark size the better the robustness. At lower watermark size, watermark bits are encoded more than once to enhance immunity against noise and interference. The proposed YCbCr associated DCT watermarking scheme is found to have a higher robustness, same level of imperceptibility but lower capacity compared with the Spatial Domain watermarking scheme. KeywordsColor Spaces, Copyright Protection, Discrete Cosine Transform, Noise, Watermarking
EYE-HEIGHT/WIDTH PREDICTION USING ARTIFICIAL NEURAL NETWORKS FROM S-PARAMETERS WITH VECTOR FITTING

Artificial neural networks (ANNs) have been used to model microwave and RF devices over the years. Conventionally, S-parameters of microwave/RF designs are used as the inputs of neural network models to predict the electrical properties of the designs. However, using the S-parameters directly as inputs into the ANN results in a large number of inputs which slows down the training and configuration process. In this paper, a new method is proposed to first disassemble the S-parameters into poles and residues using vector fitting, and then the poles and residues are used as the input data during configuration and training of the neural networks. Test cases show that the ANN trained using the proposed method is able to predict the eye-heights and eye-widths of typical interconnect structures with minimal error, while showing significant speed improvement over the conventional method.
Integrated Natural Language Processing and Meta-Network Analysis for Social Sensing of Location-Event-Actor Nexus in Disasters

The objective of this study is to propose and test a methodological framework that integrates natural language processing and meta-network analysis to leverage social media data for situation assessment and action prioritization in disasters. Situation assessment based on evaluation of location-event-actor nexus (i.e., what event is occurring where and who is working on what response actions) is essential for residents, volunteers, and relief organizations to effectively manage response actions to cope with natural disasters. This study proposed an integrated framework to parse social media data and analyze location-event-actor meta-networks for examining location-specific disaster situations. The proposed framework consists of three components: named entity recognition, convolutional neural networks, and meta-network analysis. First, a named entity recognition model is utilized to identify locations and organizations that are reported in posts on social media. Then, using a taxonomy for humanitarian event categories, a Convolutional Neural Network model is developed and trained to classify the tweets and identify disruptive events that occurred in specific locations. Finally, the relationship among locations, disruptive events, and actors/organizations are captured and modeled as a meta-network. The meta-network analysis provides indicators to assess the urgency of the events and the needs in the locations for prioritization of relief and rescue efforts. The application of the proposed framework is implemented on a case study of Hurricane Harvey in Houston. The results illustrate the capabilities of the proposed framework for enhanced social sensing of disaster situations and prioritizing relief actions
Analyzing Racket Sports From Broadcast Videos

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. However, Sports videos have a inherent temporal structure, due to the nature of sports themselves. For instance, tennis, comprises of points, games and sets played between the two players/teams. Recent attempts in sports analytics are not fully automatic for finer details or have a human in the loop for high level understanding of the game and, therefore, have limited practical applications to large scale data analysis. Many of these applications depend on specialized camera setups and wearable devices which are costly and unwieldy for players and coaches, specially in a resource constrained environments like India. Utilizing very simple and non-intrusive sensor(s) (like a single camera) along with computer vision models is necessary to build indexing and analytics systems. Such systems can be used to sort through huge swathes of data, help coaches look at interesting video segments quickly, mine player data and even generate automatic reports and insights for a coach to monitor. Firstly, we demonstrate a score based indexing approach for broadcast video data. Given a broadcast sport video, we index all the video segments with their scores to create a navigable and searchable match. Even though our method is extensible to any sport with scores, we evaluate our approach on broadcast tennis videos. Our approach temporally segments the rallies in the video and then recognizes the scores from each of the segments, before refining the scores using the knowledge of the tennis scoring system. We finally build an interface to effortlessly retrieve and view the relevant video segments by also automatically tagging the segmented rallies with human accessible tags such as ‘fault’ and ‘deuce’. The efficiency of our approach is demonstrated on broadcast tennis videos from two major tennis tournaments. Secondly, we propose an end-to-end framework for automatic attributes tagging and analysis of broadcast sport videos. We use commonly available broadcast videos of badminton matches and, unlike previous approaches, we do not rely on special camera setups or additional sensors. We propose a method to analyze a large corpus of broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective strokes. We evaluate the performance on 10 Olympic badminton matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score (mAP@0.5), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable
Survival Analysis of HIV/AIDS Patients Under ART Follow up in Attat Referral Hospital

Background: The non-curable HIV/AIDS epidemic has become a serious health and development problem especially for developing country like Ethiopia despite using ART treatment. The antiretroviral treatment (ART) scale-up service has been a recent development in Ethiopia, but its impact on mortality has not been well investigated. This study aimed to analyze the survival time of HIV-positive patients under antiretroviral therapy (ART) and identifies associated risk factors in Attat Referral Hospital. Methods: A retrospective study was conducted to examine the survival of HIV/AIDS patients under ART at Attat Referral Hospitals. Total of 408 HIV/AIDS patients in ART from April 30, 2010 up to March 30, 2017 were included in the study. Cox proportional hazard model was employed to assess the survival of HIV/AIDS patients and the associated factors. Results: A total of 408 patients were followed for a survival mean time of 46 months. In the followed-up period, 121 (29.7%) patients dead and 287 (70.3%) patients were censored. Based on functional status of patients, higher mortality was significantly associated with bedridden (HR: 3.934) and Ambulatory (HR: 1.790). According to WHO stages the patients under ART follow up with stage- IV (HR: 7.277), stage-III (HR: 4.536) and stage-II (HR: 2.213) were high risk of dying than patients in stages-I and male patients are at high risk of death than females. Conclusions: This study found that the survival time of the HIV patient under ART follow-ups in Attat Referral Hospital of Guraghe zone was significantly associated with factors: functional status, Weight, Original Regimen, CD4 count and WHO stage.
A Multimodal Approach to Assessing Document Quality

The perceived quality of a document is aﬀected by various factors, including grammat-icality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text — such as images, font choices, and visual layout — we propose a joint model that combines the text content with a visual rendering of the document for document quality assessment. Our joint model achieves state-of-the-art results over ﬁve datasets in two domains (Wikipedia and academic papers), which demonstrates the complementarity of textual and visual features, and the general applicability of our model. To examine what kinds of features our model has learned, we further train our model in a multi-task learning setting, where document quality assessment is the primary task and feature learning is an auxiliary task. Experimental results show that visual embeddings are better at learning structural features while textual embeddings are better at learning readability scores, which further veriﬁes the complementarity of visual and textual features.
A Framework for Building Verified Partial Evaluators

Partial evaluation is a classic technique for generating lean, customized code from libraries that start with more bells and whistles. It is also an attractive approach to creation of formally verified systems, where theorems can be proved about libraries, yielding correctness of all specializations “for free. ” However, it can be challenging to make library specialization both performant and trustworthy. We present a new approach, prototyped in the Coq proof assistant, which supports specialization at the speed of native-code execution, without adding to the trusted code base. Our extensible engine, which combines the traditional concepts of tailored term reduction and automatic rewriting from hint databases, is also of interest to replace these ingredients in proof assistants’ proof checkers and tactic engines, at the same time as it supports extraction to standalone compilers from library parameters to specialized code.
Supplementary Material for “Personalized Screening Intervals for Biomarkers using Joint Models for Longitudinal and Survival Data”

In this section we present a general definition of the framework of joint models for longitudinal and survival data that will be used later on for planning the optimal visit schedule. Let Dn = {Ti, δi,yi; i = 1, . , n} denote a sample from the target population, where T ∗ i denotes the true event time for the i-th subject, Ci the censoring time, Ti = min(T ∗ i , Ci) the corresponding observed event time, and δi = I(T ∗ i ≤ Ci) the event indicator, with I(·) being the indicator function that takes the value 1 when T ∗ i ≤ Ci, and 0 otherwise. In addition, we let yi denote the ni × 1 longitudinal response vector for the i-th subject, with element yil denoting the value of the longitudinal outcome taken at time point til, l = 1, . , ni. To accommodate different types of longitudinal responses in a unified framework, we postulate a generalized linear mixed effects model. In particular, the conditional distribution ∗Correspondance at: Department of Biostatistics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands. E-mail address: d.rizopoulos@erasmusmc.nl.
A Successful Entrance Contract for the Youngs ? Revisiting the Italian Training on the Job Contract

The “Contratto Formazione e Lavoro” (CFL training on the job contract) is a fixed term contract aiming at reducing young unemployment and at training young Italian workers. CFL was introduced 20 years ago, in a period of severe unemployment, and provided incentive for employers to hire young people under the program through a drastic reduction of the payroll tax and firing costs. Using the introduction of the CFL contract as an exogenous innovation and exploiting the reform of the program over time the effect of CFL on the Italian labour market is discussed and evaluated. Difference in difference technique together with matching method is used in order to establish if CFL has improved Italian young workers hiring chances. Our estimates support a positive CFL effect on employment at the introduction of the program and provide new evidence against previous results leading in the opposite direction. At the beginning of the nineties, the program reform with the drastic reduction of the relative fiscal benefits, reduced non-artisan firms probability to enter the CFL program, so that the overall result appears less clear cut.
Depression following traumatic brain injury is characterised by dominant and recurring brain loops in self-referential areas

Depression is a major albeit neglected complication in patients with traumatic brain injury (TBI). Elucidating its neural correlates remains an important milestone with respect to understanding the disorder and helping with the rehabilitation process. Towards this direction, neuropsychological theories have proposed abnormal brain dynamics as the neural basis of depressive symptomatology. This observational study addressed the question of whether depression in TBI patients is related to abnormal brain dynamics using a sample of 81 TBI patients with depressive symptomatology. To explore brain dynamics we employed the Hidden Markov model that utilises resting-state fMRI data to identify the states that the brain visits sequentially during scanning. Spatial (highest activated regions) and temporal (occupancy, switching rate) characteristics of these states were used to analyse the networks involved and probe differences between depressed and non-depressed TBI patients. We found a significant positive association between depression score and the fractional occupancy and switching rate of two specific states that distinguished between depressed and non-depressed TBI patients. These states spanned default mode, subcortical and cerebellar regions while also forming a temporally coherent metastate that the depressed brain would recurrently visit. Depression in TBI patients is characterised by abnormal recruitment and repetitive sequencing between certain neural networks. These results point to the existence of a reinforced, self-referential circuitry that could provide the basis for targeted therapies during the recovery process.