Miracle-dz/UniFAR-FTUs huggingface dataset:
Rheology of Entangled Active Polymer-Like T. Tubifex Worms.

We experimentally study the rheology of long, slender, and entangled living worms (Tubifex Tubifex). Their level of activity can be controlled by changing the temperature or by adding small amounts of alcohol to make the worms temporarily inactive. Performing classical rheology experiments on this entangled polymer-like system, we find that the rheology is qualitatively similar to that of usual polymers, but, quantitatively, (i) shear thinning is reduced by activity, (ii) the characteristic shear rate for the onset of shear-thinning is given by the time scale of the activity, and (iii) the low shear viscosity as a function of concentration shows a very different scaling from that of regular polymers. Our study paves the way towards a new experimental research field of active "polymer-like worms. "
Hipertrigliceridemia: influencia sobre parámetros que estiman el transporte reverso del colesterol

El transporte reverso del colesterol puede evaluarse mediante dos parametros que cuantifican indirectamente a las HDL: a traves de la Apo AI, su proteina mayoritaria, o por su contenido de colesterol (C-HDL). El objetivo del presente trabajo fue analizar como el aumento de trigliceridos (TG) modifica los niveles de Apo AI, C-HDL y la relacion C-HDL/Apo AI. Para ello se analizaron 200 muestras con colesterol inferior a 240 mg/dL, provenientes de pacientes de ambos sexos y con edades comprendidas entre 20 y 60 anos; de ellas la mitad presentaban TG 150 mg/dL y el resto correspondian a TG entre 150-400 mg/dL. Los TG se dosaron por colorimetria de punto final, C-HDL por la tecnica enzimatica directa y Apo AI por inmunoturbidimetria, utilizando reactivos Roche Diagnostics en un autoanalizador Hitachi 912. Al analizar los valores medios de Apo AI, C-HDL y C-HDL/Apo AI de las dos poblaciones se encontraron valores inferiores para el rango de TG entre 150-400 mg/dL en un 5,15%, 20,34% y 12,28% respectivamente, siendo estas diferencias significativas para un nivel de 0,05. La correlacion entre C-HDL y Apo AI fue buena: r = 0,869 (P 0,0001) con una recta de regresion C-HDL = 0,55 Apo AI 15,8 para TG 150 mg/dL; y r = 0,862 (P 0,0001) con una recta C-HDL = 0,48 Apo AI 15,1 para las muestras de TG en el rango 150-400 mg/dL, resultando una disminucion en el C-HDL en las muestras hipertrigliceridemicas de 0,07 Apo AI 0,7. La grafica C-HDL/Apo AI vs. TG mostro una caida de la relacion C-HDL/Apo AI por encima de 150 mg/dL de TG, indicando la presencia de HDL empobrecidas en colesterol, de manera que el cociente C-HDL/Apo AI constituye un buen indice de riesgo aterogenico asociado a la eficiencia del transporte reverso del colesterol.
The Security Reference Architecture for Blockchains: Toward a Standardized Model for Studying Vulnerabilities, Threats, and Defenses

Blockchains are distributed systems, in which security is a critical factor for their success. However, despite their increasing popularity and adoption, there is a lack of standardized models that study blockchain-related security threats. To fill this gap, the main focus of our work is to systematize and extend the knowledge about the security and privacy aspects of blockchains and contribute to the standardization of this domain.We propose the security reference architecture (SRA) for blockchains, which adopts a stacked model (similar to the ISO/OSI) describing the nature and hierarchy of various security and privacy aspects. The SRA contains four layers: (1) the network layer, (2) the consensus layer, (3) the replicated state machine layer, and (4) the application layer. At each of these layers, we identify known security threats, their origin, and countermeasures, while we also analyze several cross-layer dependencies. Next, to enable better reasoning about security aspects of blockchains by the practitioners, we propose a blockchain-specific version of the threat-risk assessment standard ISO/IEC 15408 by embedding the stacked model into this standard. Finally, we provide designers of blockchain platforms and applications with a design methodology following the model of SRA and its hierarchy.
Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch

Person re-identification (re-ID), which aims to re-identify people across different camera views, has been significantly advanced by deep learning in recent years, particularly with convolutional neural networks (CNNs). In this paper, we present Torchreid, a software library built on PyTorch that allows fast development and end-to-end training and evaluation of deep re-ID models. As a general-purpose framework for person re-ID research, Torchreid provides (1) unified data loaders that support 15 commonly used re-ID benchmark datasets covering both image and video domains, (2) streamlined pipelines for quick development and benchmarking of deep re-ID models, and (3) implementations of the latest re-ID CNN architectures along with their pre-trained models to facilitate reproducibility as well as future research. With a high-level modularity in its design, Torchreid offers a great flexibility to allow easy extension to new datasets, CNN models and loss functions.
Question Answering over Knowledge Graphs via Structural Query Patterns

Natural language question answering over knowledge graphs is an important and interesting task as it enables common users to gain accurate answers in an easy and intuitive manner. However, it remains a challenge to bridge the gap between unstructured questions and structured knowledge graphs. To address the problem, a natural discipline is building a structured query to represent the input question. Searching the structured query over the knowledge graph can produce answers to the question. Distinct from the existing methods that are based on semantic parsing or templates, we propose an effective approach powered by a novel notion, structural query pattern, in this paper. Given an input question, we first generate its query sketch that is compatible with the underlying structure of the knowledge graph. Then, we complete the query graph by labeling the nodes and edges under the guidance of the structural query pattern. Finally, answers can be retrieved by executing the constructed query graph over the knowledge graph. Evaluations on three question answering benchmarks show that our proposed approach outperforms state-of-the-art methods significantly.
Nonminimal dark sector physics and cosmological tensions

We explore whether nonstandard dark sector physics might be required to solve the existing cosmological tensions. The properties we consider in combination are (a) an interaction between the dark matter and dark energy components and (b) a dark energy equation of state $w$ different from that of the canonical cosmological constant $w=\ensuremath{-}1$. In principle, these two parameters are independent. In practice, to avoid early-time, superhorizon instabilities, their allowed parameter spaces are correlated. Moreover, a clear degeneracy exists between these two parameters in the case of cosmic microwave background (CMB) data. We analyze three classes of extended interacting dark energy models in light of the 2019 Planck CMB results and Cepheid-calibrated local distance ladder ${H}_{0}$ measurements of Riess et al. (R19), as well as recent baryon acoustic oscillation (BAO) and type Ia supernovae (SNeIa) distance data. We find that in quintessence coupled dark energy models, where $wg\ensuremath{-}1$, the evidence for a nonzero coupling between the two dark sectors can surpass the $5\ensuremath{\sigma}$ significance. Moreover, for both $\mathrm{Planck}+\mathrm{BAO}$ or $\mathrm{Planck}+\mathrm{SNeIa}$, we find a preference for $wg\ensuremath{-}1$ at about three standard deviations. Quintessence models are, therefore, in excellent agreement with current data when an interaction is considered. On the other hand, in phantom coupled dark energy models, there is no such preference for a nonzero dark sector coupling. All the models we consider significantly raise the value of the Hubble constant, easing the ${H}_{0}$ tension. In the interacting scenario, the disagreement between $\mathrm{Planck}+\mathrm{BAO}$ and R19 is considerably reduced from $4.3\ensuremath{\sigma}$ in the case of the $\mathrm{\ensuremath{\Lambda}}$ cold dark matter ($\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$) model to about $2.5\ensuremath{\sigma}$. The addition of low-redshift BAO and SNeIa measurements leaves, therefore, some residual tension with R19 but at a level that could be justified by a statistical fluctuation. Bayesian evidence considerations mildly disfavor both the coupled quintessence and phantom models, while mildly favoring a coupled vacuum scenario, even when late-time datasets are considered. We conclude that nonminimal dark energy cosmologies, such as coupled quintessence, phantom, or vacuum models, are still an interesting route toward softening existing cosmological tensions, even when low-redshift datasets and Bayesian evidence considerations are taken into account.
Quark beam function at next-to-next-to-next-to-leading order in perturbative QCD in the generalized large-
Nc
 approximation

We present the matching coefficient for the quark beam function at next-to-next-to-next-to-leading order in perturbative QCD in the generalized large Nc-approximation, Nc ∼ Nf ≫ 1. Although several refinements are still needed to make this result interesting for phenomenological applications, our computation shows that a fully-differential description of simple color singlet production processes at a hadron collider at N3LO in perturbative QCD is within reach.
Mexiletine for Diabetic Peripheral Neuropathy

Objective: To evaluate the efficacy and safety of mexiletine for the treatment of painful diabetic peripheral neuropathy (DPN). Data Sources: English-language clinical studies and review articles involving human subjects were identified using MEDLINE (January 1966-January 1999). Data Synthesis: Peripheral neuropathy is a common complication of diabetes. Tricyclic antidepressants are the agents of choice for treatment of symptomatic peripheral neuropathy. However, other therapies are necessary in patients who do not respond to tricyclic antidepressants, do not tolerate their adverse effects, or have a contraindication to their use. The use of mexiletine for DPN has been evaluated in four studies. Two studies concluded that mexiletine was significantly better than placebo as treatment for DPN; another showed no difference. In the fourth study, improvement with use of mexiletine was not demonstrated, but posthoc analysis showed benefits in certain subgroups of patients, including patients with stabbing or burning pain, heat sensation, or formication. All studies excluded patients with cardiac, renal, or hepatic disease; in addition, only one study assessed quality of life, which raises questions about the safety and clinical significance of the reported benefits of mexiletine therapy. Conclusions: Four studies have evaluated the role of mexiletine in treating DPN. Although the results are not uniform, they suggest that mexiletine may be considered a potential alternative for patients unable to take tricyclic antidepressants. The long-term safety of mexiletine and its use in patients with renal, hepatic, or cardiac disease has not been established.
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking

How to leverage cross-document interactions to improve ranking performance is an important topic in information retrieval (IR) research. However, this topic has not been well-studied in the learning-to-rank setting and most of the existing work still treats each document independently while scoring. The recent development of deep learning shows strength in modeling complex relationships across sequences and sets. It thus motivates us to study how to leverage cross-document interactions for learning-to-rank in the deep learning framework. In this paper, we formally define the permutation-equivariance requirement for a scoring function that captures cross-document interactions. We then propose a self-attention based document interaction network and show that it satisfies the permutation-equivariant requirement, and can generate scores for document sets of varying sizes. Our proposed methods can automatically learn to capture document interactions without any auxiliary information, and can scale across large document sets. We conduct experiments on three ranking datasets: the benchmark Web30k, a Gmail search, and a Google Drive Quick Access dataset. Experimental results show that our proposed methods are both more effective and efficient than baselines.
Integrated Quantile RAnk Test (iQRAT) for gene-level associations

Testing gene-based associations is the fundamental approach to identify genetic associations in sequencing studies. The best-known approaches include Burden and Sequence Kernel Association Tests (SKAT). The gene-traits associations are often complex due to population heterogeneity, gene-environmental interactions, and various other reasons. The mean-based tests, including Burden and SKAT, may miss or underestimate some high-order associations that could be scientifically interesting. In this paper, we propose a new family of gene-level association tests, which integrate quantile rank score processes while combining multiple weighting schemes to accommodate complex associations. The resulting test statistics have multiple advantages. They are as efficient as the mean-based SKAT and Burden test when the associations are homogeneous across quantile levels and have improved efficiency for complex and heterogeneous associations. The test statistics are distribution-free, and could hence accommodate a wide range of distributions. They are also computationally feasible. We established the asymptotic properties of the proposed tests under the null and alternative hypothesis and conducted large scale simulation studies to investigate its finite sample performance. We applied the proposed tests to Metabochip data to identify genetic associations with lipid traits and compared the results with those of the the Burden and SKAT tests.
J Regularization Improves Imbalanced Multiclass Segmentation

We propose a new loss formulation to further advance the multiclass segmentation of cluttered cells under weakly supervised conditions. When adding a Youden's $J$ statistic regularization term to the cross entropy loss we improve the separation of touching and immediate cells, obtaining sharp segmentation boundaries with high adequacy. This regularization intrinsically supports class imbalance thus eliminating the necessity of explicitly using weights to balance training. Simulations demonstrate this capability and show how the regularization leads to correct results by helping advancing the optimization when cross entropy stagnates. We build upon our previous work on multiclass segmentation by adding yet another training class representing gaps between adjacent cells. This addition helps the classifier identify narrow gaps as background and no longer as touching regions. We present results of our methods for 2D and 3D images, from bright field images to confocal stacks containing different types of cells, and we show that they accurately segment individual cells after training with a limited number of images, some of which are poorly annotated.
The LOFAR Tied-Array All-Sky Survey (LOTAAS): Characterization of 20 pulsar discoveries and their single-pulse behavior

We are using the LOw-Frequency ARray (LOFAR) to perform the LOFAR Tied-Array All-Sky (LOTAAS) survey for pulsars and fast transients. Here we present the astrometric and rotational parameters of 20 pulsars discovered as part of LOTAAS. These pulsars have regularly been observed with LOFAR at 149 MHz and the Lovell telescope at 1532 MHz, supplemented by some observations with the Lovell telescope at 334 MHz and the Nançay Radio Telescope at 1484 MHz. Timing models are calculated for the 20 pulsars, some of which are among the slowest-spinning pulsars known. PSR J1236−0159 rotates with a period P ∼ 3.6 s, while 5 additional pulsars show P > 2 s. Also, the spin-down rates $\dot{P}$ are, on average, low, with PSR J0815+4611 showing $\dot{P} \sim 4\times 10^{-18}$. Some of the pulse profiles, generically single-peaked, present complex shapes evolving with frequency. Multi-frequency flux measurements show that these pulsars have generically relatively steep spectra but exceptions are present, with values ranging between ∼−4 and −1. Among the pulsar sample, a large fraction shows large single-pulse variability, with 4 pulsars being undetectable more than $15\%$ of the time and one tentatively classified as a Rotating Radio Transient. Two single-peaked pulsars show drifting sub-pulses.
A Review of Visual Trackers and Analysis of its Application to Mobile Robot

Computer vision has received a significant attention in recent year, which is one of the important parts for robots to obtain information about the external environment. Visual trackers can provide the necessary physical and environmental parameters for the mobile robot, and their performance is related to the actual application of the robot. This study provides a comprehensive survey on visual trackers. Following a brief introduction, we first analyzed the basic framework and difficulties of visual trackers. Then the structure of generative and discriminative methods is introduced, and summarized the feature descriptors, modeling methods, and learning methods which be used in tracker. Later we reviewed and evaluated the state-of-the-art progress on discriminative trackers from three directions: correlation filter, deep learning and convolutional features. Finally, we analyzed the research direction of visual tracker used in mobile robot, as well as outlined the future trends for visual tracker on mobile robot.
Learning to Communicate in a Noisy Environment

In this work we examine the problem of learning to cooperate in the context of wireless communication. We consider the two agent setting where agents must learn modulation and demodulation schemes that enable them to communicate with each other in the presence of a power-constrained additive white Gaussian noise (AWGN) channel. We investigate whether learning is possible under different levels of information sharing between distributed agents that are not necessarily co-designed. We make use of the "Echo" protocol, a learning protocol where an agent hears, understands, and repeats (echoes) back the message received from another agent, simultaneously training itself to communicate. To capture the idea of cooperation between agents that are "not necessarily co-designed," we use two different populations of function approximators - neural networks and polynomials. In addition to diverse learning agents, we include non-learning agents that use fixed modulation protocols such as QPSK and 16QAM. We verify that the Echo learning approach succeeds independent of the inner workings of the agents, and that learning agents can not only learn to match the communication expectations of others, but can also collaboratively invent a successful communication approach from independent random initializations. We complement our simulations with an implementation of the Echo protocol in software-defined radios. To explore the continuum between tight co-design of learning agents and independently designed agents, we study how learning is impacted by different levels of information sharing - including sharing training symbols, sharing intermediate loss information, and sharing full gradient information. We find that, in general, co-design (increased information sharing) accelerates learning and that this effect becomes more pronounced as the communication task becomes harder.
Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates

Accurate segmentation of punctate white matter lesions (PWML) in preterm neonates by an automatic algorithm can better assist doctors in diagnosis. However, the existing algorithms have many limitations, such as low detection accuracy and large resource consumption. In this paper, a novel spatiotemporal transformation deep learning method called Trident Segmentation CNN (TS-CNN) is proposed to segment PWML in MR images. It can convert spatial information into temporal information, which reduces the consumption of computing resources. Furthermore, a new improved training loss called Self-balancing Focal Loss (SBFL) is proposed to balance the loss during the training process. The whole model is evaluated on a dataset of 704 MR images. Overall the method achieves median DSC, sensitivity, specificity, and Hausdorff distance of 0.6355, 0.7126, 0.9998, and 24.5836 mm which outperforms the state-of-the-art algorithm. (The code is now available on this https URL)
Robust Training with Ensemble Consensus

Since deep neural networks are over-parametrized, they may memorize noisy examples. We address such memorizing issue under the existence of annotation noise. From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we find that noisy examples do not consistently incur small losses on the network in the presence of perturbation. Based on this, we propose a novel training method called Learning with Ensemble Consensus (LEC) whose goal is to prevent overfitting noisy examples by eliminating them identified via consensus of an ensemble of perturbed networks. One of the proposed LECs, LTEC outperforms the current state-of-the-art methods on MNIST, CIFAR-10, and CIFAR-100 despite its efficient memory.
Automatic Extraction of Personality from Text: Challenges and Opportunities

In this study we examined the possibility to extract personality traits from a text. We created an extensive dataset by having experts annotate personality traits in a large number of texts from multiple online sources. From these annotated texts we selected a sample and made further annotations ending up with a large low-reliability dataset and a small high-reliability dataset. We then used the two datasets to train and test several machine learning models to extract personality from text, including a language model. Finally, we evaluated our best models in the wild, on datasets from different domains. Our results show that the models based on the small high-reliability dataset performed better (in terms of R2) than models based on large low-reliability dataset. Also, the language model based on the small high-reliability dataset performed better than the random baseline. Finally, and more importantly, the results showed our best model did not perform better than the random baseline when tested in the wild. Taken together, our results show that determining personality traits from a text remains a challenge and that no firm conclusions can be made on model performance before testing in the wild.
Kernel Computations from Large-Scale Random Features Obtained by Optical Processing Units

Approximating kernel functions with random features (RFs) has been a successful application of random projections for nonparametric estimation. However, performing random projections presents computational challenges for large-scale problems. Recently, a new optical hardware called Optical Processing Unit (OPU) has been developed for fast and energy-efficient computation of large-scale RFs in the analog domain. More specifically, the OPU performs the multiplication of input vectors by a large random matrix with complexvalued i.i. d. Gaussian entries, followed by the application of an element-wise squared absolute value operation – this last nonlinearity being intrinsic to the sensing process. In this paper, we show that this operation results in a dot-product kernel that has connections to the polynomial kernel, and we extend this computation to arbitrary powers of the feature map. Experiments demonstrate that the OPU kernel and its RF approximation achieve competitive performance in applications using kernel ridge regression and transfer learning for image classification. Crucially, thanks to the use of the OPU, these results are obtained with time and energy savings.