Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing Heterogeneity and Scalability in Layered Multicast CongestionControl

In this paper, we design SIM, a protocol that integrates three distinct mechanisms - Selective participation, Intra-group transmission adjustment, and Menu adaptation - to solve the general multicast congestion control problem. We argue that only a solution that includes elements of each mechanism can scale and adapt to heterogeneity in network and receiver characteristics. In our protocol, these mechanisms operate at different time scales and distribute the responsibility of adaptation to different entities in the network. Per our knowledge, SIM is the first protocol for layered multicast that adjusts not only the subscription levels of the receivers but also the transmission rates of the layers. We show that SIM is efficient and stable in the presence of heterogeneous receivers and dynamic changes in the bottlenecks and session membership. SIM also outperforms RLM in terms of stability and efficiency.
Enhancement of viewer comfort in stereoscopic viewing: parallax adjustment

One of the major deficiencies of stereoscopic visualization, viewer discomfort, can be caused by the non-robustness of human perception or by excessive 3D cues in the viewed images. In order to minimize this discomfort, the amount of parallax within each stereo pair needs to be reduced. Similarly to the case of 'continuous look-around', parallax adjustment requires the knowledge of images from virtual cameras. In the case of parallel geometry, the virtual cameras are located on the line between the true cameras. Since in a general scenario no constraint should be posed on the complexity of the viewed scene, 3D modeling techniques cannot be used. We evaluate the usefulness of parallax adjustment using two view reconstruction methods based on disparity-compensated linear interpolation: a quadtree method with block splitting adapted to object boundaries and a pixel based method. For all, but most complex stereoscopic images tested both algorithms performed very well, especially the pixel-based approach. In terms of the overall usefulness of parallax adjustment, the initial test have shown a very favorable viewer response; the perceived depth was judged to vary smoothly from zero through natural 3D to exaggerated 3D. The adjustment was convincing although not completely free of distortions.
Exploring Self-Injury: An Art-Based Approach to Cultivating Empathy and Understanding in Mental Health Professionals

Self-injury greatly affects individuals, their families, and the mental health professionals who provide their care. This art-based research investigates the impact of clients’ self-injury on mental health professionals. It addresses four research questions and applies a methodology that integrates body art, photography, poetry, and dialogue. Six participants (including the researcher) participated in three meetings wherein they artistically responded with temporary body art to two questions, sorted through their photographs, reviewed poetry created about their work, and discussed the study experience. The researcher navigated being a witness-researcher by personally and creatively engaging in the study. The results present raw, distilled narratives, visual and verbal, as the process of discovery identified six prevailing characteristics: (1) Holding the space and relationship with the witness became a model and guide for participants to explore self-injury in the room and with their clients; (2) Sensory empathy allowed participants to increase appreciation of the body as a canvas. Experiencing art materials on the skin and in the mirror and photographic viewing of the art created a pseudo self-injury experience that deepened participants’ connection to the behavior; (3) Cultivating a compassionate response is important in healing the client and the mental health professional; (4) The research provided insights into the role of ambivalence and the impact of overflowing emotions in the development of self-injury; (5) I examined the significance of self-injury as expression and communication. Through body art, participants metaphorically experienced potential power and influence of this communication; (6) I considered the surfacing sense of hope an integral treatment-related experience that can replace self-
Cytoplasmic Translocation of Polypyrimidine Tract-Binding Protein and Its Binding to Viral RNA during Japanese Encephalitis Virus Infection Inhibits Virus Replication

Japanese encephalitis virus (JEV) has a single-stranded, positive-sense RNA genome containing a single open reading frame flanked by the 5′- and 3′-non-coding regions (NCRs). The virus genome replicates via a negative-sense RNA intermediate. The NCRs and their complementary sequences in the negative-sense RNA are the sites for assembly of the RNA replicase complex thereby regulating the RNA synthesis and virus replication. In this study, we show that the 55-kDa polypyrimidine tract-binding protein (PTB) interacts in vitro with both the 5′-NCR of the positive-sense genomic RNA - 5NCR(+), and its complementary sequence in the negative-sense replication intermediate RNA - 3NCR(-). The interaction of viral RNA with PTB was validated in infected cells by JEV RNA co-immunoprecipitation and JEV RNA-PTB colocalization experiments. Interestingly, we observed phosphorylation-coupled translocation of nuclear PTB to cytoplasmic foci that co-localized with JEV RNA early during JEV infection. Our studies employing the PTB silencing and over-expression in cultured cells established an inhibitory role of PTB in JEV replication. Using RNA-protein binding assay we show that PTB competitively inhibits association of JEV 3NCR(-) RNA with viral RNA-dependent RNA polymerase (NS5 protein), an event required for the synthesis of the plus-sense genomic RNA. cAMP is known to promote the Protein kinase A (PKA)-mediated PTB phosphorylation. We show that cells treated with a cAMP analogue had an enhanced level of phosphorylated PTB in the cytoplasm and a significantly suppressed JEV replication. Data presented here show a novel, cAMP-induced, PTB-mediated, innate host response that could effectively suppress JEV replication in mammalian cells.
Applicability of Immersive Analytics in Mixed Reality: Usability Study

Nowadays, visual analytics is mainly performed by programming approaches and viewing the results on a desktop monitor. However, due to the capabilities of smart glasses, new user interactions and representation possibilities become possible. This refers especially to 3D visualizations in the medical field, as well as, the industry domain, as valuable depth information can be related to the complex real-world structures and related data, which is also denoted as immersive analytics. However, the applicability of immersive analytics and its drawbacks, especially in the context of mixed reality, are quite unexplored. In order to validate the feasibility of immersive analytics for the aforementioned purposes, we designed and conducted a usability study with 60 participants. More specifically, we evaluated the effects of spatial sounds, performance changes from one analytics task to another, expert status, and compared an immersive analytics approach (i.e., a mixed-reality application) with a desktop-based solution. Participants had to solve several data analytics tasks (outlier’s detection and cluster recognition) with the developed mixed-reality application. Thereby, the performance measures regarding time, errors, and movement patterns were evaluated. The separation into groups (low performer vs. high performer) was performed using a mental rotation pretest. When solving analytic tasks in mixed reality, participants changed their movement patterns in the mixed reality setting significantly, while the use of spatial sounds reduced the handling time significantly, but did not affect the movement patterns. Furthermore, the usage of mixed reality for cluster recognition is significantly faster than the desktop-based solution (i.e., a 2D approach). Moreover, the results obtained with self-developed questionnaires indicate 1) that wearing smart glasses are perceived as a potential stressor and 2) that the utilization of sounds is perceived very differently by the participants. Altogether, industry and researchers should consider immersive analytics as a suitable alternative compared to the traditional approaches.
Induction of adipose tissue regeneration by chemically-modified hyaluronic acid

Hyaluronan was modified by amidation of the carboxyl groups with dodycyl amides to produce a viscous gel (HYADD3). Small aliquots were implanted intramuscularly in Wistar rats at a concentration of 30 mg/ml for up to 12 weeks. Standard histology and immunohistochemistry demonstrated low levels of inflammation at all time periods, absence of any fibrous capsule coupled with the presence of mature adipocytes within the degrading gel. It is concluded that the chemical architecture of HYADD3 gel causes the de novo regeneration of soft tissue by recruitment of appropriate progenitor cells.
Use of Radiofrequency Ablation in Benign Thyroid Nodules: A Literature Review and Updates

Successful thermal ablation using radiofrequency has been reported in various tumors including liver or kidney tumors. Nonsurgical minimally invasive ablative therapy such as radiofrequency ablation (RFA) has been reported to be a safe and efficient treatment option in managing symptomatic cold thyroid nodules or hyperfunctioning thyroid nodules. Pressure and cosmetic symptoms have been shown to be significantly improved both in the short and long terms after RFA. For hyperfunctioning thyroid nodules, RFA is indicated for whom surgery or radioiodine are not indicated or ineffective or for those who refuse surgery or radio-iodine. Improvement of thyroid function with decreased need for antithyroid medications has been reported. Complication rate is relatively low. By reviewing the current literature, we reported its efficacy and complications and compared the efficacy of RFA relative to other ablative options such as ethanol ablation and laser ablation.
Artificial Neural Network for Arabic Speech Recognition in Humanoid Robotic Systems

Speech recognition is projected to play an increasingly important role in the future of human/machine interfacing. The objective of this study is to engineer a speech recognition system capable of deployment onto a general humanoid robot. A MATLAB based program for speech extraction and identification is constructed. Although there are many different algorithms used in speech recognition, the utilization of artificial neural networks (ANNs) was found to be adequate for the Arabic language, with its multitude of complexities, accents and linguistic intentionality. Furthermore, ANN is powerful and can model complex functions while offering the opportunity for additional cognitive abilities. The software tool developed converts the incoming audio signal into a two dimensional spectrogram, which is subsequently supplied to the ANN through a mel frequency cepstral coefficients (mfcc) algorithm. A software product capable of converting Arabic speech to commands was developed for controlling the “NAO” humanoid robot with 90% hit rate.
Structure and Activation Mechanism of the Drosophila Initiator Caspase Dronc*

Activation of an initiator caspase is essential to the execution of apoptosis. The molecular mechanisms by which initiator caspases are activated remain poorly understood. Here we demonstrate that the autocatalytic cleavage of Dronc, an important initiator caspase in Drosophila, results in a drastic enhancement of its catalytic activity in vitro. The autocleaved Dronc forms a homodimer, whereas the uncleaved Dronc zymogen exists exclusively as a monomer. Thus the autocatalytic cleavage in Dronc induces its stable dimerization, which presumably allows the two adjacent monomers to mutually stabilize their active sites, leading to activation. Crystal structure of a prodomain-deleted Dronc zymogen, determined at 2.5 Å resolution, reveals an unproductive conformation at the active site, which is consistent with the observation that the zymogen remains catalytically inactive. This study revealed insights into mechanism of Dronc activation, and in conjunction with other observations, suggests diverse mechanisms for the activation of initiator caspases.
Data-aided algorithm based frequency synchronizer for DVB-S2

This paper presents an efficient frequency synchronizer for digital video broadcasting --- second generation (DVB-S2). Analyzing the robust algorithm among data-aided approaches, we find that the Luise & Reggiannini (L&R) algorithm is the most suitable one for coarse frequency estimation with respect to estimation performance and complexity. However, it requires many multipliers and adders to compute ouput values of correlators. We propose an efficient architecture identifying the serial correlator with the buffer and multiplexers. The proposed coarse frequency synchronizer can reduce the hardware complexity about 92% of the direct implementation. The proposed architecture has been implemented and verified on the Xilinx Virtex II FPGA.
Determining the Optimal Size of Handwriting Character Entry Boxes for Pen-Based Systems

This paper presents two experiments to determine the optimal size of a box for the input of Chinese characters (called "Kanji" in Japanese) mixed with Japanese Kana (phonetic) handwriting characters on a tablet computer. The experiments involve an evaluation of boxes of different sizes. The results are assessed in terms of high performance factors (e.g., high character recognition rates, minimal stroke protrusions outside the character box) and subjective ratings (e.g., ease of writing and a minimum level of fatigue). The results show that the optimal size of character boxes for Kanji & Kana characters is around 1.44 times 1.44 cm. The experimental results also show that the optimal size is the same for both PDAs and Tablet PCs. The results provide important guidelines to designers who are concerned about the interplay between user comfort, input efficiency and output display efficiency. The knowledge obtained from our experiments on Tablet PCs would be added to the literature on optimal size for handwriting character entry boxes
Long Range Dependence, Unbalanced Haar Wavelet Transformation and Changes in Local Mean Level

Long range dependent (LRD) stationary time series have historically served to model real time series with apparent changes in local mean level. A natural tool to study changes in local mean level is the unbalanced Haar wavelet transformation (UHT). In this work, UHT is used to study changes in local mean level in LRD models and several real and simulated time series exhibiting LRD. In particular, simulations for LRD models suggest that changes in local mean level occur at times essentially governed by a homogeneous Poisson arrival process, and only the local mean levels themselves inherit the LRD property of the original time series. These properties are compared with the analogous ones in several real and simulated time series. The results are mixed though generally in favor of LRD models. The approach based on UHT is also compared to several alternatives such as defining changes in local mean level through kernel smoothing. The interest throughout is mainly in very long time series such as those collected in the studies of data traffic over Internet.
Mindfulness and Group Cognitive Behavioural Therapy to Address Problematic Perfectionism

This paper presents the rationale and therapeutic components of a pilot group program which combined mindfulness skills and cognitive behavioural therapy to address problematic perfectionism (PP). PP is defined as the pursuit of extremely high standards despite negative outcomes and consequences in personal, academic and social areas of functioning. This pilot group used a psycho-educational approach, training participants in mindfulness skills and cognitive behavioural techniques to reduce procrastination, ruminative processes and general distress. Mindfulness is an ancient meditative practice that encourages acceptance and non-reactivity to different types of stimuli. Mindfulness in psychology has been used as a cognitive and behavioural tool to reduce anxiety and worry as well as to promote well-being. The paper discusses preliminary findings, initial participants’ impressions and acceptability of the program followed by an identification of limitations of pilot program. Finally the paper offers recommendations for future research.
Exceedance Probabilities for the Dirichlet Distribution

We derive an efficient method to calculate exceedance probabilities (EP) for the Dirichlet distribution when the number of event types is larger than two. Also, we present an intuitive application of Dirichlet EPs and compare our method to a sampling approach which is the current practice in neuroimaging model selection.
Efficient Reed-Muller Implementation for Fuzzy Extractor Schemes

Nowadays, physical tampering and counterfeiting of electronic devices are still an important security problem and have a great impact on large-scale and distributed applications, such as Internet-of-Things. Physical Unclonable Functions (PUFs) have the potential to be a fundamental means to guarantee intrinsic hardware security, since they promise immunity against most of known attack models. However, inner nature of PUF circuits hinders a wider adoption since responses turn out to be noisy and not stable during time. To overcome this issue, most of PUF implementations require a fuzzy extraction scheme, able to recover responses stability by exploiting error correction codes (ECCs). In this paper, we propose a Reed-Muller (RM) ECC design, meant to be embedded into a fuzzy extractor, that can be efficiently configured in terms of area/delay constraints in order to get reliable responses from PUFs. We provide implementation details and experimental evidences of area/delay efficiency through syntheses on medium-range FPGA device.
Feature Extraction Using Power-Law Adjusted Linear Prediction With Application to Speaker Recognition Under Severe Vocal Effort Mismatch

Linear prediction is one of the most established techniques in signal estimation, and it is widely utilized in speech signal processing. It has been long understood that the nerve firing rate of human auditory system can be approximated by power law non-linearity, and this has been the motivation behind using perceptual linear prediction in extracting acoustic features in a variety of speech processing applications. In this paper, we revisit the application of power law non-linearity in speech spectrum estimation by compressing/expanding power spectrum in autocorrelation-based linear prediction. The development of so-called LP- α is motivated by a desire to obtain spectral features that present less mismatch than conventionally used spectrum estimation methods when speech of normal loudness is compared to speech under vocal effort. The effectiveness of the proposed approach is demonstrated in a speaker recognition task conducted under severe vocal effort mismatch comparing shouted versus normal speech mode.
Measurement-based modelling of LTE performance in Dublin city

LTE/4G is the next generation of cellular networks which specifically aims to improve the network performance for data traffic and is currently being rolled out by many network operators. We present results from an extensive LTE measurement campaign in Dublin, Ireland using a custom performance measurement tool. Performance data was measured for two local operators at a variety of locations within the city (including cell edge locations, indoors, outdoors, etc.) as well as for mobile users on public transport within the city, too. Using this data we derive a model of the characteristics of link layer RTT and bandwidth vs link signal strength. This model is suited to use for performance evaluation of applications and services, and since it is based on real measurements it allows realistic evaluation of performance.
Using mathematical modelling to challenge accepted methods and paradigms of tuberculosis control and transmission

3 Declaration . 5 Preface . 6 Acknowledgements . 7 Publications in peer-reviewed journals during the thesis . 8 Articles submitted for publication . 10 Presentations at international conferences . 11 Table of contents . 12 List of tables . 16 List of figures . 17 Abbreviations . 20 1 Literature review . 21 1.1 Tuberculosis Today . 21 1.2 Drug-resistant tuberculosis and associated challenges . 24 1.3 Latent tuberculosis infection and its control . 27 1.4 The missing pieces of the tuberculosis epidemic picture . 29 1.5 Mathematical modelling of tuberculosis . 31 2 High rates of multidrug-resistant and rifampicin-resistant tuberculosis among re-treatment cases: where do they come from? 35 2.1 Abstract . 36 2.2 Background . 36 2.3 Methods . 37 2.3. 1 Study design . 37 2.3. 2 Model principle . 38 2.3. 3 Stochastic method for generating parameter values . 40 2.3. 4 Observed outputs . 41 2.3. 5 Sensitivity analyses . 41 2.4 Results . 41 2.5 Discussion . 45
Facilitating image exploratory search with relations

Traditional text-based image retrieval does not fully support queries on semantic relationships between two entities. To better help the exploratory search on image collections, this paper presents a system for automatically extracting the relations between entities by analyzing the sentence dependency on the descriptions of the images. Our results demonstrate that using the extracted relations is not only beneficial for understanding the data set but also an effective way to facilitate users' exploratory searches.
Probabilistic Modeling of Streaming RFID Data by Using Correlated Variable-duration HMMs

Radio Frequency Identification (RFID) has been widely deployed to track product flow in such fields as automated manufacture, retail and supply chain management. The special characteristics of streaming RFID data, combined with the specific scenarios of RFID applications, present numerous challenges in RFID stream processing, including noisy and incomplete data, temporal and spatial correlations and very huge volumes. In this paper, we present a probabilistic model, specifically Correlated Variable-Duration Hidden Markov Models (CVD-HMMs), to capture uncertainty and correlations of locations of tagged objects. Based on this model, we can infer object locations from raw RFID streams. And our model can be self-tuned by learning its key parameters from sample RFID readings. Experimental results show that our proposed model and the preliminary inference techniques are effective.