Miracle-dz/UniFAR-FTUs huggingface dataset:
QUINT: Node Embedding Using Network Hashing

Representation learning using network embedding has received tremendous attention due to its efficacy to solve downstream tasks. Popular embedding methods (such as <monospace>deepwalk</monospace>, <monospace>node2vec</monospace>, <monospace>LINE</monospace>) are based on a neural architecture, thus unable to scale on large networks both in terms of time and space usage. Recently, we proposed <inline-formula><tex-math notation="LaTeX">$\mathrm{BinSketch}$</tex-math><alternatives><mml:math><mml:mi> BinSketch </mml:mi></mml:math><inline-graphic xlink:href="bera-ieq1-3111997.gif"/></alternatives></inline-formula>, a sketching technique for compressing binary vectors to binary vectors. In this paper, we show how to extend <inline-formula><tex-math notation="LaTeX">$\mathrm{BinSketch}$</tex-math><alternatives><mml:math><mml:mi> BinSketch </mml:mi></mml:math><inline-graphic xlink:href="bera-ieq2-3111997.gif"/></alternatives></inline-formula> and use it for network hashing. Our proposal named <monospace>QUINT</monospace> is built upon <inline-formula><tex-math notation="LaTeX">$\mathrm{BinSketch}$</tex-math><alternatives><mml:math><mml:mi> BinSketch </mml:mi></mml:math><inline-graphic xlink:href="bera-ieq3-3111997.gif"/></alternatives></inline-formula>, and it embeds nodes of a sparse network onto a low-dimensional space using simple bit-wise operations. <monospace>QUINT</monospace> is the first of its kind that provides tremendous gain in terms of speed and space usage without compromising much on the accuracy of the downstream tasks. Extensive experiments are conducted to compare <monospace>QUINT</monospace> with seven state-of-the-art network embedding methods for two end tasks – link prediction and node classification. We observe huge performance gain for <monospace>QUINT</monospace> in terms of speedup (up to <inline-formula><tex-math notation="LaTeX">$7000\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>7000</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="bera-ieq4-3111997.gif"/></alternatives></inline-formula>) and space saving (up to <inline-formula><tex-math notation="LaTeX">$800\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>800</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="bera-ieq5-3111997.gif"/></alternatives></inline-formula>) due to its bit-wise nature to obtain node embedding. Moreover, <monospace>QUINT</monospace> is a consistent top-performer for both the tasks among the baselines across all the datasets. Our empirical observations are backed by rigorous theoretical analysis to justify the effectiveness of <monospace>QUINT</monospace>. In particular, we prove that <monospace>QUINT</monospace> retains enough structural information which can be used further to approximate many topological properties of networks with high confidence.
Animal Model Transgenic Mouse Model of AA Amyloidosis

AA amyloidosis can be induced in mice experimen-tally through injection of certain chemical or biolog-ical compounds. However, the usefulness of this ap-proach is limited by its dependence on exogenousinflammatory agents that stimulate cytokines to in-crease the synthesis of precursor serum amyloid A(SAA) protein and the transitory nature of the patho-logical fibrillar deposits. We now report that trans-genic mice carrying the human interleukin 6 geneunder the control of the metallothionein-I promoterhad markedly increased concentrations of SAA anddeveloped amyloid in the spleen, liver, and kidneysby 3 months of age. At the time of death about 6months later, organs obtained from these animalshad extensive amyloid deposits. This disease processwas apparent radiographically using small-animalcomputer axial tomography and magnetic resonanceimaging equipment. The AA nature of the amyloid wasevidenced immunohistochemically and was unequivo-cally established by sequence analysis of protein ex-tracted from the fibrils. The availability of this unique
Horizon effect on optimal retirement decision

We study an optimal consumption, investment, life insurance, and retirement decision of an economic agent who has an option to retire early any time before the mandatory retirement date. We conduct a thorough theoretical analysis for the optimal retirement problem with general utility function in the presence of a mandatory retirement date, which leads to the optimal stopping problem in finite horizon. Furthermore, the different marginal utility of consumption before and after retirement is considered, which can provide an explanation for the retirement-consumption puzzle, while it makes the problem technically more challenging. Based on the theory of partial differential equation, we analyze the variational inequality arising from the dual problem and establish the duality theorem. We show that the optimal retirement decision is determined by the time-varying optimal retirement wealth boundary, and we provide an integral equation representation for the optimal retirement wealth boundary, which can be solved accurately and efficiently by using recursive integration method. As an extension, the case with stochastic labor income is also considered. The properties of the optimal strategies are provided with emphasis on the role of the mandatory retirement date and the impact of having a retirement option on the optimal financial decisions.
Measuring uncertainty and uncertainty dispersion from a large set of model predictions

We construct measures of uncertainty and its dispersion exploiting the heterogeneity of a large set of model predictions. The approach is forward-looking, can be computed in real-time, and can be applied at any frequency. We illustrate the methodology with expected shortfall predictions of worldwide equity indices generated from 71 risk models. We use the new measures in asset pricing, risk forecasting, and for explaining the aggregate trading volume of S&P 500 firms.
Exploring the growth process of successors in long-lived small and medium-sized manufacturing companies: a qualitative study

Background: Japan is the world's largest country in terms of the number of long-lived companies, the majority of which are family-owned small and medium-sized enterprises. On the other hand, many business owners will be retiring without successors, and the closure of these small and medium enterprises may have a significant impact on the future economy. The purpose of this study is to explore the growth process of successors in long-lived small and medium-sized manufacturing companies so that potential successors can know what they will experience as managers and be prepared for the future, and professionals who support successors can be provided with information on the growth process of successors to provide appropriate support. Methods: Semi-structured interviews were conducted with six successors of small and medium-sized manufacturing companies that are more than half a century old. Their answers were analyzed using the modified grounded theory approach to construct a hypothetical model. Results: In total, 46 concepts, four categories, 17 subcategories, and one core category with an analysis result diagram were formed. The diagram shows the successors gained confidence in management through the dilemma between autonomy and constraint in the early stage of succession, which was found in previous research. Following the initial stage, the successors responded to the crisis caused by market constraints such as being stuck in a new market, unreasonable treatment from customers, and created autonomous strategies in their businesses. Conclusions: By experiencing repetitive crises, the successors tend to acquire new perspectives toward the naturally occurring crises. This change of premise by the successors is considered as the process of double-loop learning. Relationships inside and outside the company influence the generation of this viewpoint. From a long-term perspective, a sense of unity with employees, stable employment, and the pursuit of enjoyment constitute the successors' values in this model.
Epidemiology and Management of 144 Eclampsia Patients at Sikasso Hospital

Plummer Vinson syndrome is a rare condition characterized by cervical dysphagia associated with iron deficiency anemia and a ring on the upper esophagus. Sometimes its unusual presentation can lead to misdiagnosis. We report the observation of a patient with Plummer-Vinson syndrome, revealed by upper dysphagia.
Multiple case studies: schoolchildren with cleft lip and palate in context of phoniatric evaluation

Introduction: Phoniatrics is interested in cleft lip and palate (CLP) due to the complexity in aspects of speech, language and hearing. Objective: In context of phoniatric evaluation in schoolchildren with CLP, the objective of multiple cases study was to describe the interference of clinical factors in the performance in tests of auditory perceptual function of language, as well as the association between them. Method: Thirty children, between 6 years and 2 months and 9 years and 11 months, were evaluated by Phoniatric doctor, in interdisciplinary treatment service for CLP: 13 (43%) cleft palate and 17 (57%) cleft lip and palate. Clinical factors (including type of cleft, age at evaluation, sex, socioeconomic status, school performance, speech therapy, audiological examination and others) and performance in tests of auditory discrimination, auditory memory and phonological awareness were described, including the association between them. Results: There was no significant difference between the cleft groups and the mean age (p = 0.618), sex (p = 0.431), socioeconomic level (p = 0.580) and school performance (p = 0.785). The percentage of children who had attended therapy is higher in cleft lip and palate group (94.1%). Changes in tests: 4 (13.3%) auditory discrimination, 6 (20%) auditory memory and 8 (26.7%) * Pontifícia Universidade Católica de São Paulo – São Paulo, SP, Brazil. Authors’ contributions: MESG Conception and design of the work. VMF Critical review of the content. BCACN Approval of the final version and guidance of the master. Correspondence e-mail: Mônica Elisabeth Simons Guerra simons.guerra@gmail.com Received: 13/03/2020 Accepted: 28/5/2020 Distúrb Comun, São Paulo, 32(3): 383-395, setembro, 2020 Mônica Elisabeth Simons Guerra, Vanessa Magosso Franchi, Beatriz Cavalcanti de Albuquerque Caiuby Novaes https://doi.org/10.23925/2176-2724.2020v32i3p383-395
Binary Fractions of G and K Dwarf Stars Based on Gaia EDR3 and LAMOST DR5: Impacts of the Chemical Abundances

Based on the large volume Gaia Early Data Release 3 and LAMOST Data Release 5 data, we estimate the bias-corrected binary fractions of the field late G and early K dwarfs. A stellar locus outlier method is used in this work, which works well for binaries of various periods and inclination angles with single-epoch data. With a well-selected, distance-limited sample of about 90,000 GK dwarfs covering wide stellar chemical abundances, it enables us to explore the binary fraction variations with different stellar populations. The average binary fraction is 0.42 ± 0.01 for the whole sample. Thin-disk stars are found to have a binary fraction of 0.39 ± 0.02, thick-disk stars have a higher one of 0.49 ± 0.02, while inner halo stars possibly have the highest binary fraction. For both the thin- and thick-disk stars, the binary fractions decrease toward higher [Fe/H], [α/H], and [M/H] abundances. However, the suppressing impacts of [Fe/H], [α/H], and [M/H] are more significant for the thin-disk stars than those for the thick-disk stars. For a given [Fe/H], a positive correlation between [α/Fe] and the binary fraction is found for the thin-disk stars. However, this tendency disappears for the thick-disk stars. We suspect that it is likely related to the different formation histories of the thin and thick disks. Our results provide new clues for theoretical works on binary formation.
Memory semantization through perturbed and adversarial dreaming

Classical theories of memory consolidation emphasize the importance of replay in extracting semantic information from episodic memories. However, the characteristic creative nature of dreams suggests that memory semantization may go beyond merely replaying previous experiences. We propose that rapid-eye-movement (REM) dreaming is essential for eﬃcient memory semantization by randomly combining episodic memories to create new, virtual sensory experiences. We support this hypothesis by implementing a cortical architecture with hierarchically organized feedforward and feedback pathways, inspired by generative adversarial networks (GANs). Learning in our model is organized across three diﬀerent global brain states mimicking wakefulness, non-REM (NREM) and REM sleep, optimizing diﬀerent, but complementary objective functions. We train the model in an unsupervised fashion on standard datasets of natural images and evaluate the quality of the learned representations. Our results suggest that adversarial dreaming during REM sleep is essential for extracting memory contents, while perturbed dreaming during NREM sleep improves robustness of the latent representation to noisy sensory inputs. The model provides a new computational perspective on sleep states, memory replay and dreams and suggests a cortical implementation of GANs.
Sharp regret bounds for empirical Bayes and compound decision problems

We consider the classical problems of estimating the mean of an $n$-dimensional normally (with identity covariance matrix) or Poisson distributed vector under the squared loss. In a Bayesian setting the optimal estimator is given by the prior-dependent conditional mean. In a frequentist setting various shrinkage methods were developed over the last century. The framework of empirical Bayes, put forth by Robbins (1956), combines Bayesian and frequentist mindsets by postulating that the parameters are independent but with an unknown prior and aims to use a fully data-driven estimator to compete with the Bayesian oracle that knows the true prior. The central figure of merit is the regret, namely, the total excess risk over the Bayes risk in the worst case (over the priors). Although this paradigm was introduced more than 60 years ago, little is known about the asymptotic scaling of the optimal regret in the nonparametric setting. We show that for the Poisson model with compactly supported and subexponential priors, the optimal regret scales as $\Theta((\frac{\log n}{\log\log n})^2)$ and $\Theta(\log^3 n)$, respectively, both attained by the original estimator of Robbins. For the normal mean model, the regret is shown to be at least $\Omega((\frac{\log n}{\log\log n})^2)$ and $\Omega(\log^2 n)$ for compactly supported and subgaussian priors, respectively, the former of which resolves the conjecture of Singh (1979) on the impossibility of achieving bounded regret; before this work, the best regret lower bound was $\Omega(1)$. In addition to the empirical Bayes setting, these results are shown to hold in the compound setting where the parameters are deterministic. As a side application, the construction in this paper also leads to improved or new lower bounds for density estimation of Gaussian and Poisson mixtures.
Multilingual Speech Recognition for Low-Resource Indian Languages using Multi-Task conformer

Transformers have recently become very popular for sequence-to-sequence applications such as machine translation and speech recognition. In this work, we propose a multi-task learning-based transformer model for low-resource multilingual speech recognition for Indian languages. Our proposed model consists of a conformer [1] encoder and two parallel transformer decoders. We use a phoneme decoder (PHN-DEC) for the phoneme recognition task and a grapheme decoder (GRP-DEC) to predict grapheme sequence. We consider the phoneme recognition task as an auxiliary task for our multi-task learning framework. We jointly optimize the network for both phoneme and grapheme recognition tasks using Joint CTC-Attention [2] training. We use a conditional decoding scheme to inject the language information into the model before predicting the grapheme sequence. Our experiments show that our proposed approach can obtain significant improvement over previous approaches [4]. We also show that our conformer-based dual-decoder approach outperforms both the transformer-based dual-decoder approach and single decoder approach. Finally, We compare monolingual ASR models with our proposed multilingual ASR approach.
System Optimization in Synchronous Federated Training: A Survey

—The unprecedented demand for collaborative machine learning in a privacy-preserving manner gives rise to a novel machine learning paradigm called federated learning (FL). Given a sufﬁcient level of privacy guarantees, the practicality of an FL system mainly depends on its time-to-accuracy performance during the training process. Despite bearing some resemblance with traditional distributed training, FL has four distinct challenges that complicate the optimization towards shorter time-to-accuracy: information deﬁciency, coupling for contrasting factors, client heterogeneity, and huge conﬁguration space. Motivated by the need for inspiring related research, in this paper we survey highly relevant attempts in the FL literature and organize them by the related training phases in the standard workﬂow: selection, conﬁguration, and reporting. We also review exploratory work including measurement studies and benchmarking tools to friendly support FL developers. Although a few survey articles on FL already exist, our work differs from them in terms of the focus, classiﬁcation, and implications.
Risk-Averse Decision Making Under Uncertainty

A large class of decision making under uncertainty problems can be described via Markov decision processes (MDPs) or partially observable MDPs (POMDPs), with application to artificial intelligence and operations research, among others. In this article, we consider the problem of designing policies for MDPs and POMDPs with objectives and constraints in terms of dynamic coherent risk measures rather than the traditional total expectation, which we refer to as the constrained risk-averse problem. Our contributions can be described as follows: first, for MDPs, under some mild assumptions, we propose an optimization-based method to synthesize Markovian policies. We then demonstrate that such policies can be found by solving difference convex programs (DCPs). We show that our formulation generalize linear programs for constrained MDPs with total discounted expected costs and constraints; second, for POMDPs, we show that, if the coherent risk measures can be defined as a Markov risk transition mapping, an infinite-dimensional optimization can be used to design Markovian belief-based policies. For POMDPs with stochastic finite-state controllers (FSCs), we show that the latter optimization simplifies to a (finite dimensional) DCP. We incorporate these DCPs in a policy iteration algorithm to design risk-averse FSCs for POMDPs. We demonstrate the efficacy of the proposed method with numerical experiments involving conditional-value-at-risk and entropic-value-at-risk risk measures.
Improving Building Segmentation for Off-Nadir Satellite Imagery

Automatic building segmentation is an important task for satellite imagery analysis and scene understanding. Most existing segmentation methods focus on the case where the images are taken from directly overhead (i.e., low off-nadir/viewing angle). These methods often fail to provide accurate results on satellite images with larger off-nadir angles due to the higher noise level and lower spatial resolution. In this paper, we propose a method that is able to provide accurate building segmentation for satellite imagery captured from a large range of off-nadir angles. Based on Bayesian deep learning, we explicitly design our method to learn the data noise via aleatoric and epistemic uncertainty modeling. Satellite image metadata (e.g., off-nadir angle and ground sample distance) is also used in our model to further improve the result. We show that with uncertainty modeling and metadata injection, our method achieves better performance than the baseline method, especially for noisy images taken from large off-nadir angles.
An Accelerated Proximal Gradient-based Robust Model Predictive Control Algorithm

In this letter, an accelerated robust model predictive control (RMPC) algorithm for linear systems with additive disturbance is proposed based on the tube technique and the proximal gradient method. Our study consists of two parts. First, the tube cross sections are minimized in the RMPC objective, which make the description of the additive disturbance less conservative. Then, such RMPC problem is solved iteratively by a novel proximal gradient-based algorithm, which can accelerate the iteration convergence rate. A case study and several numerical experiments are provided to show the effectiveness of the proposed approach.
Concurrent Elections and Voting Behaviour: Evidence from an Italian Referendum

In September 2020 Italy held a constitutional referendum. On the same election days, many municipalities and some regions held municipal and regional elections. We exploit this unique occasion, caused by the unexpected Covid-19 crisis, to obtain a causal estimate of the effects of the overlap of concurrent elections on the referendum results. When the referendum overlaps with either municipal or regional elections, we find a positive effect on turnout and on the proportion of blank and null votes. We also find a quantitatively small but statistically significant effect on the referendum preferences. We interpret the results through the use of the calculus of voting model, exploiting a slightly modified version of the most widespread one in the literature. Our findings are relevant from a policy-making standpoint, with respect to both fostering turnout and reducing election organizational costs.
Applied Ethnobotany: People, Medicinal Plants Use and Conservation Practices in Benishangul Gumuz Regional State of Ethiopia: The future cursed natural resource in the Region

An ethnobotanical study of medicinal plants used by the communities in Wombera District, Benishangual Gumuz Regional State, and Western Ethiopia was carried out from 0ctober, 2019 to October, 2020. The purpose of the study was to document information of medicinal plants and indigenous knowledge on use and conservation of medicinal plants by the communities of Wombera District. A purposive sampling was designed and employed for selection of the study areas (6 sampling sites) and 200 informants (52 males and 18 females) aged between 18-85 years were randomly selected from 6 kebeles. Ethnobotanical data were collected using semi-structured questionnaires, interview and, group discussion s. A total of 91 medicinal plants were documented from the study area. Of these 60 were human, 7 veterinary and 24 both human and veterinary medicines. Data were analyzed quantitativelly. The highest number of medicinal plants was collected from wild habitat (64 %), while 33% was collected from home garden, 3% occurred both in wild habitat and home garden. The most plant parts used in treatment of human disease were leaves (34.6%) followed by roots (20.9%). The most frequently mentioned mode of administration was oral (54%) followed by dermal (27.5%) and the least was found to be application through eyes and ears (3%) each. The most common form of medicine preparation was crushing, pounding and homogenizing in water (43.07%) followed by boiling and Fumigating (16%), squeezing(15.45%), chewing(10.7) and the leasts were burning and cooking (5%) each. Deforestation for agriculture, over exploitation, firewood collection, and overgrazing were the main threats of medicinal plants in the study area. The biggest problem of traditional medicinal remedies is the accurate dosage, which sometimes may even kill. Moreover lack of awareness of cultivation in home garden resulted as threats of medicinal patient.