Miracle-dz/UniFAR-FTUs huggingface dataset:
Contributions to hemostasis

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading contributions to hemostasis as one of the reading material to finish quickly.
On the Convergence of ADMM with Task Adaption and Beyond

Along with the development of learning and vision, Alternating Direction Method of Multiplier (ADMM) has become a popular algorithm for separable optimization model with linear constraint. However, the ADMM and its numerical variants (e.g., inexact, proximal or linearized) are awkward to obtain state-of-the-art performance when dealing with complex learning and vision tasks due to their weak task-adaption ability. Recently, there has been an increasing interest in incorporating task-specific computational modules (e.g., designed filters or learned architectures) into ADMM iterations. Unfortunately, these task-related modules introduce uncontrolled and unstable iterative flows, they also break the structures of the original optimization model. Therefore, existing theoretical investigations are invalid for these resulted task-specific iterations. In this paper, we develop a simple and generic proximal ADMM framework to incorporate flexible task-specific module for learning and vision problems. We rigorously prove the convergence both in objective function values and the constraint violation and provide the worst-case convergence rate measured by the iteration complexity. Our investigations not only develop new perspectives for analyzing task-adaptive ADMM but also supply meaningful guidelines on designing practical optimization methods for real-world applications. Numerical experiments are conducted to verify the theoretical results and demonstrate the efficiency of our algorithmic framework.
Random walks and quasi‐convexity in acylindrically hyperbolic groups

Arzhantseva proved that every infinite‐index quasi‐convex subgroup H of a torsion‐free hyperbolic group G is a free factor in a larger quasi‐convex subgroup of G . We give a probabilistic generalization of this result. That is, we show that when R is a subgroup generated by independent random walks in G , then ⟨H,R⟩≅H*R with probability going to one as the lengths of the random walks go to infinity and this subgroup is quasi‐convex in G . Moreover, our results hold for a large class of groups acting on hyperbolic metric spaces and subgroups with quasi‐convex orbits. In particular, when G is the mapping class group of a surface and H is a convex cocompact subgroup we show that ⟨H,R⟩ is convex cocompact and isomorphic to H*R .
Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems

In this paper, we present Eco, a hardware-software co-design enabling generic energy management on IoT nodes. Eco is tailored to devices with limited resources and thus targets most of the upcoming IoT scenarios. The proposed measurement module combines commodity components with common system interfaces to achieve easy, flexible integration with various hardware platforms and the RIOT IoT operating system. We thoroughly evaluate and compare accuracy and overhead. Our findings indicate that our commodity design competes well with highly optimized solutions, while being significantly more versatile. We employ Eco for energy management on RIOT and validate its readiness for deployment in a five-week field trial integrated with energy harvesting.
Automatic techniques for cochlear implant CT image analysis

The goals of this dissertation are to fully automate the image processing techniques needed in the post-operative stage of IGCIP and to perform a thorough analysis of (a) the robustness of the automatic image processing techniques used in IGCIP and (b) assess the sensitivity of the IGCIP process as a whole to individual components. The automatic methods that have been developed include the automatic localization of both closely- and distantly-spaced CI electrode arrays in post-implantation CTs and the automatic selection of electrode configurations based on the stimulation patterns. Together with the existing automatic techniques developed for IGCIP, the proposed automatic methods enable an end-to-end IGCIP process that takes pre- and post-implantation CT images as input and produces a patient-customized electrode configuration as output.
Successive Embedding and Classification Loss for Aerial Image Classification

Deep neural networks can be effective means to automatically classify aerial images but is easy to overfit to the training data. It is critical for trained neural networks to be robust to variations that exist between training and test environments. To address the overfitting problem in aerial image classification, we consider the neural network as successive transformations of an input image into embedded feature representations and ultimately into a semantic class label, and train neural networks to optimize image representations in the embedded space in addition to optimizing the final classification score. We demonstrate that networks trained with this dual embedding and classification loss outperform networks with classification loss only. %We also study placing the embedding loss on different network layers. We also find that moving the embedding loss from commonly-used feature space to the classifier space, which is the space just before softmax nonlinearization, leads to the best classification performance for aerial images. Visualizations of the network's embedded representations reveal that the embedding loss encourages greater separation between target class clusters for both training and testing partitions of two aerial image classification benchmark datasets, MSTAR and AID. Our code is publicly available on GitHub.
Portuguese Named Entity Recognition using BERT-CRF

Recent advances in language representation using neural networks have made it viable to transfer the learned internal states of a trained model to downstream natural language processing tasks, such as named entity recognition (NER) and question answering. It has been shown that the leverage of pre-trained language models improves the overall performance on many tasks and is highly beneficial when labeled data is scarce. In this work, we employ a pre-trained BERT with Conditional Random Fields (CRF) architecture to the NER task on the Portuguese language, combining the transfer capabilities of BERT with the structured predictions of CRF. We explore feature-based and fine-tuning training strategies for the BERT model. Our fine-tuning approach obtains new state-of-the-art results on the HAREM I dataset, improving the F1-score by 3.2 points on the selective scenario (5 NE classes) and by 3.8 points on the total scenario (10 NE classes).
ABC: Asynchronous Blockchain without Consensus

There is a preconception that a blockchain needs consensus. But consensus is a powerful distributed property with a remarkably high price tag. So one may wonder whether consensus is at all needed. We introduce a new blockchain architecture called ABC that functions despite not establishing consensus, and comes with an array of advantages: ABC is permissionless, deterministic, and resilient to complete asynchrony. ABC features finality and does not rely on costly proof-of-work. Without establishing consensus, ABC cannot support certain applications, in particular smart contracts that are open for interaction with unknown agents. However, our system is an advantageous solution for many important use cases, such as cryptocurrencies like Bitcoin.
Dynamic Complexity of Document Spanners

The present paper investigates the dynamic complexity of document spanners, a formal framework for information extraction introduced by Fagin, Kimelfeld, Reiss, and Vansummeren (JACM 2015). We first look at the class of regular spanners and prove that any regular spanner can be maintained in the dynamic complexity class DynPROP. This result follows from work done previously on the dynamic complexity of formal languages by Gelade, Marquardt, and Schwentick (TOCL 2012). To investigate core spanners we use SpLog, a concatenation logic that exactly captures core spanners. We show that the dynamic complexity class DynCQ, is more expressive than SpLog and therefore can maintain any core spanner. This result is then extended to show that DynFO can maintain any generalized core spanner and that DynFO is at least as powerful as SpLog with negation.
Technical report on Conversational Question Answering

Conversational Question Answering is a challenging task since it requires understanding of conversational history. In this project, we propose a new system RoBERTa + AT +KD, which involves rationale tagging multi-task, adversarial training, knowledge distillation and a linguistic post-process strategy. Our single model achieves 90.4(F1) on the CoQA test set without data augmentation, outperforming the current state-of-the-art single model by 2.6% F1.
A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series

Conventional financial models fail to explain the economic and monetary properties of cryptocurrencies due to the latter's dual nature: their usage as financial assets on the one side and their tight connection to the underlying blockchain structure on the other. In an effort to examine both components via a unified approach, we apply a recently developed Non-Homogeneous Hidden Markov (NHHM) model with an extended set of financial and blockchain specific covariates on the Bitcoin (BTC) and Ether (ETH) price data. Based on the observable series, the NHHM model offers a novel perspective on the underlying microstructure of the cryptocurrency market and provides insight on unobservable parameters such as the behavior of investors, traders and miners. The algorithm identifies two alternating periods (hidden states) of inherently different activity -- fundamental versus uninformed or noise traders -- in the Bitcoin ecosystem and unveils differences in both the short/long run dynamics and in the financial characteristics of the two states, such as significant explanatory variables, extreme events and varying series autocorrelation. In a somewhat unexpected result, the Bitcoin and Ether markets are found to be influenced by markedly distinct indicators despite their perceived correlation. The current approach backs earlier findings that cryptocurrencies are unlike any conventional financial asset and makes a first step towards understanding cryptocurrency markets via a more comprehensive lens.
On the computation of overorders

The computation of a maximal order of an order in a semisimple algebra over a global field is a classical well-studied problem in algorithmic number theory. In this paper, we consider the related problems of computing all minimal overorders as well as all overorders of a given order. We use techniques from algorithmic representation theory and the theory of minimal integral ring extensions to obtain efficient and practical algorithms, whose implementation is publicly available.
Fluctuations of the arctic curve in the tilings of the Aztec diamond on restricted domains

We consider uniform random domino tilings of the restricted Aztec diamond which is obtained by cutting off an upper triangular part of the Aztec diamond by a horizontal line. The restriction line asymptotically touches the arctic circle that is the limit shape of the north polar region in the unrestricted model. We prove that the rescaled boundary of the north polar region in the restricted domain converges to the Airy$_2$ process conditioned to stay below a parabola with explicit continuous statistics and the finite dimensional distribution kernels. The limit is the hard-edge tacnode process which was first discovered in the framework of non-intersecting Brownian bridges. The proof relies on a random walk representation of the correlation kernel of the non-intersecting line ensemble which corresponds to a random tiling.
Spin inertia and polarization recovery in quantum dots: Role of pumping strength and resonant spin amplification

Spin inertia measurements are a novel experimental tool to study long-time spin relaxation processes in semiconductor nanostructures. We develop a theory of the spin inertia effect for resident electrons and holes localized in quantum dots. We consider the spin orientation by short optical pulses with arbitrary pulse area and detuning from the trion resonance. The interaction with an external longitudinal magnetic field and the hyperfine interaction with the nuclear spin bath is considered in both the ground and excited (trion) states of the quantum dots. We analyze how the spin inertia signal depends on the magnetic field (polarization recovery) and on the modulation frequency of the helicity of the pump pulses as well as on their power and detuning. In particular, we elaborate how approaching the saturation limit of the spin polarization influences the measurements. The quantitative description of spin inertia measurements will enable the determination of the parameters of spin dynamics such as the spin relaxation times in the ground and excited states and the parameters of the hyperfine interaction. Finally, we predict the emergence of resonant spin amplification due to the transverse components of the nuclear spin fluctuations, which manifests itself as oscillations of the spin polarization as a function of the longitudinal magnetic field.
Évaluation biomécanique de la locomotion à la suite d'une arthroplastie de la hanche

Objective: To compare center of mass (COM) compensation in the frontal and sagittal plane during gait in patients with large diameter head total hip arthroplasty (LDH-THA) and hip resurfacing (HR). Design: Observational study. Setting: Outpatient biomechanical laboratory. Participants: Two groups of 12 patients with LDH-THA and HR recruited from a larger randomized study and 11 healthy controls. Interventions: Not applicable. Main outcomes measures: To compare the distance between the hip prosthetic joint center (HPJC) and the COM. The ratio (RHPJC-COM) and the variability (CVHPJCCOM) were compared between groups. Hip flexor, abductor and adductor muscle strength were also correlated between groups while radiographic measurements were correlated with the outcome measures.
MAINTAINING CONTRAST IN REDUCED SPEECH IN STANDARD SOUTHERN BRITISH ENGLISH

The aim of this paper is to investigate how contrast is maintained in highly reduced speech in Standard Southern British English. To this end, a carefully controlled elicitation task was designed to collect reduced forms of function words – which are known to be prone to high degrees of reduction – in anacrusis (initial unstressed position). Specifically, the small paradigmatic system of pronoun and contracted auxiliary sequences was recorded and analysed. This paper reports on the acoustic analysis of a few contrasting paradigms (we’d vs we’ll and you’d vs he’d) focussing on the phonetic features that help maintain the distinction between them. The results indicate that the contrast is maintained by ‘apparently missing’ phonetic features being temporally redistributed rather than segmentally realised.
Can Structure-Strategy Nexus Impact on Corporate Performance ? An Investigation of Selected Banking Organizations in Nigeria

Can a mismatch between strategy and the structure lead to inefficiency in all cases meaning a less than optimal input/output ratio and hence affecting performance? This has been a discourse of debate in the recent times. This work investigated the impact of structure-strategy nexus on the performances of selected banks in Nigeria. The work among other things ascertains if banking organization with well-designed structure but ineffective corporate strategy enjoys stellar performance; evaluates the impact of well-articulated strategy but poor structure on the corporate growth of banking firm and as well determines the effect of organist rather than mechanistic structure on the employee morale in banking institutions. The study utilized descriptive survey design; and data was collected through a self-administered questionnaire from a sample of 205 respondents who were staff of five selected banking organizations in Ikeja, Lagos State Nigeria. The findings revealed that firms with welldesigned structure but ineffective corporate strategy do not enjoy stellar performance. The study also found out that well-articulated strategy but poor structure do not impact positively on the corporate growth of banking firm. It was also discovered that organist rather than mechanistic structure affect positively on the employee morale of Nigerian banking institutions. The study therefore concluded that the synergistic interface between structure and strategy are needed for enhanced performance in the Nigerian banking sector. It was recommended that management of today’s organizations particularly the banking firms needs to intensify their efforts in order to determine the synergistic relationship between strategy and structure and as well as enhance the implementation of these activities so as to increase their position through better applications of reliable structural dimensions and strategic elements.
THE ROLE OF ALTERNATIVE SPLICING IN THE REGULATION OF JASMONATE SIGNALING By

THE ROLE OF ALTERNATIVE SPLICING IN THE REGULATION OF JASMONATE SIGNALING By Lalita Chimanlal Patel The plant hormone jasmonate (JA) is studied primarily for its role in plant defense. In response to attack by pathogens and herbivores, the JA-amino acid conjugate JA-isoleucine (JAIle) accumulates and promotes the association of the E3 ubiquitin ligase SCF COI1 with its substrates, the Jasmonate-ZIM domain (JAZ) repressor proteins. JAZ proteins bind the F-box protein COI1 in a hormone-dependent manner and are targeted for ubiquitination and degradation by the 26S proteasome pathway. JAZ degradation releases transcription factors from repression, thereby promoting transcription of JA-responsive genes. Among the earliest response genes to be transcribed are the JAZ genes themselves. Many JAZ genes are subject to alternative splicing events that reduce the ability of JAZ proteins to interact with COI1 in a hormonedependent manner. These truncated JAZ proteins are hypothesized to attenuate JA responses. In this study, JAZ transcripts were quantified in response to JA elicitation and in various tissues. The results show that the relative abundance of JAZ splice variants is not significantly affected by JA elicitation or tissue type, thus suggesting that alternative splicing of JAZ pre-mRNA is controlled by the efficiency with which cis-acting splice sites are selected by the spliceosome. Additionally, transgenic plants ectopically expressing cDNAs encoding truncated splice variants of JAZ2 and JAZ4 have reduced sensitivity to JA, indicating that these proteins act as dominant repressors of JA signaling. This research highlights the importance of alternative splicing as an adaptive response of plants to environmental stress.
Slow Growth for Gauss Legendre Sparse Grids

A sparse grid for multidimensional quadrature can be constructed from products of 1D rules. For multidimensional quadrature, it is highly desirable to construct families of sparse grids whose point growth is controlled. The properties of the sparse grid are determined by the indexed family of 1D rules that are selected. In this regard, both the precision and nestedness of the 1D rules can be crucial. Thus, two frequent bases for sparse grids are the nested Clenshaw Curtis family, and the high precision Gauss Legendre family. In many cases, sparse grids of comparable precision and comparable point count may be formed from either family. However, it is possible to slightly modify the usual Gauss Legendre family in such a way that a significant reduction in point count occurs, resulting in sparse grids that are noticeably more efficient.
SPECTROSCOPIC INVESTIGATIONS OF THE FORMATION OF THE Zn-SnO NANOSTRUCTURED FILMS

The aim of this work is to obtain nanostructured Zn-Sn-O films by sol-gel method. Stable solutions with Sn/Zn atomic ratio of 1:1 and 1:2 of zinc and tin, respectively, were obtained starting from Sn(II) 2ethylhexanoate and zinc acetate dehydrate in ethylic absolute alcohol and triethanolamine. Solutions which contain both tin and zinc cations exhibit fluorescence emission even after four days storage. The obtained powders are amorphous. Monoand multi-layer films were deposited on glass and silicon substrates. A beginning of crystallization of Zn2SnO4 can be seen by XRD in the case of films with Sn/Zn atomic ratio of 1:2. The surface topography of the films was estimated by AFM. The refractive index values in the 1.6-2.3 range were calculated from Spectroellipsometry data using Cauchy Model. Fluorescence emission was observed in the case of studied films excepting tin dioxide. The structure, morphology and optical properties of the obtained films depend on the composition, number of deposition and the thermal treatment temperature.
Listener Preference on the Local Criterion for Ideal Binary-Masked Speech

Ideal binary mask (IBM) is a signal-processing technique that retains the time-frequency regions in a mixture of target speech and background noise when the local signal-to-noise ratio (SNR) is higher than a local criterion (LC) and removes the regions otherwise. The intelligibility of IBM-processed speech is typically high and does not depend on the choice of LC for a wide range of LC values. The current study investigates the listeners’ preferences on the LC value for IBM processed speech. Concatenated everyday sentences were mixed with three types of background noises (airplane noise, train noise, and multitalker babble) and were presented continuously to the listeners following the IBM processing. The IBM algorithm was implemented so that the listeners were able to adjust the LC value in real-time using a programmable knob. The listeners were instructed to adjust the LC value until the IBM-processed stimuli reached the most preferable quality. Across 20 listeners, large individual differences were observed for the preferred LC values. A cluster analysis identified that 11 of the 20 listeners exhibited consistent patterns of results. For this main cluster of listeners, the preferred LC value depended on the noise type, overall SNR, and the difficulty of the target sentences.
Balancing Resource Pooling and Equipoise in Multipath Transport

By simultaneously using multiple paths through the Internet, multipath transport protocols have the potential to greatly improve performance, resilience and flexibility. Further, by linking the congestion behavior of the subflows of a connection, it is possible to move traffic away from congested paths, allowing network capacity to be pooled and better handling surges in traffic. In this paper we show that existing algorithms that achieve such resource pooling have a few problems, which stem from the differences between fluid flow models and packet-level behavior. Further, these algorithms are poor at detecting and using capacity when congestion levels vary rapidly, as they do in the Internet. We propose the principle of equipoise as a balance to resource pooling, and present a class of algorithms that achieve different degrees of resource pooling and equipoise. We show how to dynamically adapt the aggressiveness of multipath flows so they compete fairly in the current Internet. We use a combination of real deployment and packet-level simulation to show that the emergent behavior of these algorithms is robust, the algorithms are fair to existing TCP, and achieve both equipoise and resource pooling.