Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Demographic Transition, Technological Innovation on Environmental Degradation in the Congo Basin

Ensuring Environmental sustainability as a means of stimulating growth remains an issue of great concern. In the past decades the environment has been deteriorating and almost every part of the planet has been touched. The main cause of environmental degradation is linked to humans’ activities such as agricultural intensification, population growth and energy consumption. Demographic transition entails many challenges as the population decreases due to a fall in birth rate and high dependency ratio, the technological progress usually improve health care and standard of living thereby increasing the life expectancies. The objective of this paper is therefore to investigate whether excess demography and technological innovation can explain the environmental degradation in the Countries that make up the Congo Basin. By applying the panel ARDL model (PMG), we realized that the demographic variables (Life Expectancy and depending ratio) negatively influence the environmental degradation. We equally found that technological innovation significantly reduces environmental emissions. However, the results do not support the hypothesis Original Research Article Nguambi et al.; JEMT, 26(11): 38-53, 2020; Article no.JEMT. 63086 39 of the Environmental Kuznets Curve (EKC) for most of the countries except for Gabon and the Republic of Congo. Finally this region through policy implementation has to make greater efforts in controlling demographic change and new technologies that are environmentally friendly.
Application of epoxy putty external skeletal fixator for stabilization of tarsocrural arthrodesis in small dogs and cats

Two small-breed dogs and two cats, with an average body weight of 4.88 kg (range: 4.3–5.5 kg), suffered hindlimb lameness due to luxation with or without fractures of the tarsocrural joint. These patients underwent tarsocrural arthrodesis with epoxy putty external skeletal fixator. The animals’ skins were incised minimally, and the articular cartilage of the tarsocrural joint was removed, followed by autogenous cancellous bone grafting. Epoxy putty and positive Centerface®, pins with diameters 1.2 mm and 2.0 mm, were used for connecting bar and as a full pin fixation, respectively. All the patients regained the ability to bear weight on the affected limb within 3–7 days and resumed a normal gait within 9–15 weeks. The external skeletal fixator frame was removed within 13–17 weeks without major complications. Tarsocrural arthrodesis using epoxy putty external skeletal fixator resulted in excellent outcomes without severe postoperative complications in this study. Epoxy putty external skeletal fixator can be a valuable surgical option for tarsocrural arthrodesis in patients weighing less than 5.5 kg.
Association of Gestational Diabetes Mellitus with Adverse Pregnancy Outcomes and Its Interaction with Maternal Age in Chinese Urban Women

Background The prevalence of gestational diabetes mellitus (GDM) has been dramatically increasing worldwide. The aims of this study were to examine associations of GDM with pregnancy outcomes in Chinese urban women and to evaluate the interaction between GDM and other major risk factors for the risk of adverse pregnancy outcomes. Methods A retrospective analysis included 8844 women who delivered live singletons at ≥28 weeks of gestation between June 2012 and March 2013 among Chinese urban women. Structured questionnaires were used to collect information on demographic characteristics, lifestyle behavior, medical history, and pregnancy outcomes. The diagnosis of GDM was made between 24 and 28 gestational weeks according to the International Association of Diabetes and Pregnancy Study Groups criteria. Logistic regression models were used to assess the association of GDM with pregnancy outcomes and to examine the interaction between GDM and other major risk factors including maternal age, prepregnancy body mass index, and gestational weight gain for the risk of pregnancy outcomes. Results 13.9% of women were diagnosed with GDM. We found that GDM was associated with higher risk of cesarean delivery (odds ratio (OR) = 1.69, 95% CI (confidence interval): 1.48-1.92), preterm birth (OR = 1.32, 95% CI: 1.07-1.64), macrosomia (OR = 1.69, 95% CI: 1.34-2.13), and large for gestational age (LGA, OR = 1.43, 95% CI: 1.18-1.73) after adjustment for potential confounders. We also observed the interaction between GDM and maternal age for the risk of cesarean delivery (P for interaction = 0.025), and the OR of GDM for cesarean delivery was 1.71 (95% CI: 1.49-1.97) among women aged less than 35 years. Conclusions GDM was associated with an increased risk of cesarean delivery, preterm birth, macrosomia, and LGA in Chinese urban women, and there was an interaction between GDM and maternal age for the risk of cesarean delivery.
Socio-emotional Dimensions in Gifted Chilean High School Students with Interests in STEM: Influence of Gender and University Enrichment Program Participation

This article contributes to the study of the gender gap for women in STEM, by exploring the socioemotional dimensions of gifted girls with positive attitudes toward math and science, as measured by the TOSRA and TOMRA. The variables of self-concept, self-efficacy, and gender stereotype identification were examined, using the Piers-Harris Self-Concept Scale, Sources of Science and Mathematics Self-Efficacy Scale, and Masculine-Feminine Inventory, based on gender and extracurricular enrichment program participation. The sample consisted of 103 Chilean students in 10th grade, identified as gifted using the Raven’s Standard Progressive Matrices. Data were analyzed using Multivariate Analysis of Variance (MANOVA), Analysis of Variance (ANOVA), and t-tests to analyze the effects of gender and enrichment program participation. Results showed that self-concept for girls is lower than for boys, and lower for girls who attended enrichment programs, compared to girls who did not attend programs. Furthermore, an interaction effect was observed between gender and enrichment program attendance on the gender stereotype subscales of masculinity and submission. Implications for enrichment programs were discussed, suggesting the need to (a) focus on girls´ self-concept, (b) decrease the risk of stereotype threat, and (c) encourage gifted girls to identify how they experience and act upon gender stereotypes.
A discrete boundedness-by-entropy method for finite-volume approximations of cross-diffusion systems

An implicit Euler finite-volume scheme for general cross-diffusion systems with volume-filling constraints is proposed and analyzed. The diffusion matrix may be nonsymmetric and not positive semidefinite, but the diffusion system is assumed to possess a formal gradient-flow structure that yields $L^\infty$ bounds on the continuous level. Examples include the Maxwell-Stefan systems for gas mixtures, tumor-growth models, and systems for the fabrication of thin-film solar cells. The proposed numerical scheme preserves the structure of the continuous equations, namely the entropy dissipation inequality as well as the nonnegativity of the concentrations and the volume-filling constraints. The discrete entropy structure is a consequence of a new vector-valued discrete chain rule. The existence of discrete solutions, their positivity, and the convergence of the scheme is proved. The numerical scheme is implemented for a one-dimensional Maxwell-Stefan model and a two-dimensional thin-film solar cell system. It is illustrated that the convergence rate in space is of order two and the discrete relative entropy decays exponentially.
Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve

We consider the problem of dividing limited resources to individuals arriving over T rounds. Each round has a random number of individuals arrive, and individuals can be characterized by their type (i.e. preferences over the different resources). A standard notion of 'fairness' in this setting is that an allocation simultaneously satisfy envy-freeness and efficiency. For divisible resources, when the number of individuals of each type are known upfront, the above desiderata are simultaneously achievable for a large class of utility functions. However, in an online setting when the number of individuals of each type are only revealed round by round, no policy can guarantee these desiderata simultaneously. We show that in the online setting, the two desired properties (envy-freeness and efficiency) are in direct contention, in that any algorithm achieving additive counterfactual envy-freeness up to a factor of LT necessarily suffers a efficiency loss of at least 1 / LT. We complement this uncertainty principle with a simple algorithm, Guarded-Hope, which allocates resources based on an adaptive threshold policy and is able to achieve any fairness-efficiency point on this frontier.
DEEMD: Drug Efficacy Estimation Against SARS-CoV-2 Based on Cell Morphology With Deep Multiple Instance Learning

Drug repurposing can accelerate the identification of effective compounds for clinical use against SARS-CoV-2, with the advantage of pre-existing clinical safety data and an established supply chain. RNA viruses such as SARS-CoV-2 manipulate cellular pathways and induce reorganization of subcellular structures to support their life cycle. These morphological changes can be quantified using bioimaging techniques. In this work, we developed DEEMD: a computational pipeline using deep neural network models within a multiple instance learning framework, to identify putative treatments effective against SARS-CoV-2 based on morphological analysis of the publicly available RxRx19a dataset. This dataset consists of fluorescence microscopy images of SARS-CoV-2 non-infected cells and infected cells, with and without drug treatment. DEEMD first extracts discriminative morphological features to generate cell morphological profiles from the non-infected and infected cells. These morphological profiles are then used in a statistical model to estimate the applied treatment efficacy on infected cells based on similarities to non-infected cells. DEEMD is capable of localizing infected cells via weak supervision without any expensive pixel-level annotations. DEEMD identifies known SARS-CoV-2 inhibitors, such as Remdesivir and Aloxistatin, supporting the validity of our approach. DEEMD can be explored for use on other emerging viruses and datasets to rapidly identify candidate antiviral treatments in the future. Our implementation is available online at https://www.github. com/Sadegh-Saberian/DEEMD.
Hardness Test of GRB 950830 as a Gravitationally Lensed Echo

Cumulative hardness comparisons are a simple but statistically powerful test for the presence of gravitational lensing in gamma-ray bursts (GRBs). Since gravitational lensing does not change photon energies, all source images should have the same spectra—and hence hardness. Applied to the recent claim that the two pulses in GRB 950830 are lensed images of the same pulse, the measured flux ratio between the two main pulses should be the same at all energies. After summing up all the counts in both of GRB 950830's two pulses in all four BATSE energy bands, it was found that in energy channel 3, the second pulse appears somewhat weak. In comparison with the other BATSE energy channels, the difference was statistically significant at above 90%. This model-independent test indicates that the case for GRB 950830 involving a gravitational lens may be intriguing—but should not be considered proven.
Incremental Few-Shot Instance Segmentation

Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly1.
On Density of Modular Points in Pseudo-Deformation Rings

Given a continuous, odd, reducible, and semi-simple $2$-dimensional representation $\bar \rho _{0}$ of $G_{{\mathbb{Q}},Np}$ over a finite field of odd characteristic $p$, we study the relation between the universal deformation ring of the pseudo-representation corresponding to $\bar \rho _{0}$ (pseudo-deformation ring) and the big $p$-adic Hecke algebra to prove that the maximal reduced quotient of the pseudo-deformation ring is isomorphic to the local component of the big $p$-adic Hecke algebra corresponding to $\bar \rho _{0}$ if a certain global Galois cohomology group has dimension $1$. This partially extends the results of Böckle to the case of residually reducible representations. We give an application of our main theorem to the structure of Hecke algebras modulo $p$. As another application of our methods and results, we prove a result about non-optimal levels of newforms lifting $\bar \rho _{0}$ in the spirit of Diamond–Taylor. This also gives a partial answer to a conjecture of Billerey–Menares.
Edge coloring graphs with large minimum degree

Let G $G$ be a simple graph with maximum degree Δ ( G ) ${\rm{\Delta }}(G)$ . A subgraph H $H$ of G $G$ is overfull if ∣ E ( H ) ∣ > Δ ( G ) ⌊ ∣ V ( H ) ∣ ∕ 2 ⌋ . $| E(H)| \gt {\rm{\Delta }}(G)\lfloor | V(H)| \unicode{x02215}2\rfloor . $ Chetwynd and Hilton in 1986 conjectured that a graph G $G$ with Δ ( G ) > ∣ V ( G ) ∣ ∕ 3 ${\rm{\Delta }}(G)\gt | V(G)| \unicode{x02215}3$ has chromatic index Δ ( G ) ${\rm{\Delta }}(G)$ if and only if G $G$ contains no overfull subgraph. The best previous results supporting this conjecture have been obtained for regular graphs. For example, Perković and Reed verified the conjecture for large regular graphs G $G$ with degree arbitrarily close to ∣ V ( G ) ∣ ∕ 2 $| V(G)| \unicode{x02215}2$ . We provide a similar result for general graphs asymptotically, showing that for any given 0 < ϵ < 1 $0\lt \epsilon \lt 1$ , there exists a positive integer n 0 ${n}_{0}$ such that the following statement holds: if G $G$ is a graph on 2 n ≥ n 0 $2n\ge {n}_{0}$ vertices with minimum degree at least ( 1 + ϵ ) n $(1+\epsilon )n$ , then G $G$ has chromatic index Δ ( G ) ${\rm{\Delta }}(G)$ if and only if G $G$ contains no overfull subgraph.
Uniform Forward-modeling Analysis of Ultracool Dwarfs. II. Atmospheric Properties of 55 Late-T Dwarfs

We present a large uniform forward-modeling analysis for 55 late-T (T7–T9) dwarfs, using low-resolution (R ≈ 50–250) near-infrared (1.0–2.5 μm) spectra and cloudless Sonora–Bobcat model atmospheres. We derive the objects’ effective temperatures, surface gravities, metallicities, radii, masses, and bolometric luminosities using our newly developed Bayesian framework, and use the resulting population properties to test the model atmospheres. We find (1) our objects’ fitted metallicities are 0.3–0.4 dex lower than those of nearby stars; (2) their ages derived from spectroscopic parameters are implausibly young (10 Myr–0.4 Gyr); (3) their fitted effective temperatures show a similar spread to empirical temperature scales at a given spectral type but are ∼50–200 K hotter for ≥T8 dwarfs; and (4) their spectroscopically inferred masses are unphysically small (mostly 1–8 M Jup). These suggest the Sonora–Bobcat assumptions of cloudless and chemical-equilibrium atmospheres do not adequately reproduce late-T dwarf spectra. We also find a gravity and metallicity dependence of effective temperature as a function of spectral type. Combining the resulting parameter posteriors of our sample, we quantify the degeneracy between the fitted surface gravity and metallicity such that an increase in Z combined with a 3.4× increase in logg results in a spectrum that has similar fitted parameters. We note the systematic difference between the late-T dwarf spectra and Sonora–Bobcat models is on average ≈2%–4% of the objects’ peak J-band fluxes over the 1.0–2.5 μm range, implying modeling systematics will exceed measurement uncertainties when analyzing data with J-band S/N ≳ 50. Using our large, high-quality sample, we examine the spectral-fitting residuals as a function of wavelength and atmospheric properties to discern how to improve the model assumptions. Our work constitutes the largest analysis of brown dwarf spectra using multimetallicity models and the most systematic examination of ultracool model atmospheres to date.
Dissipative superfluid hydrodynamics for the unitary Fermi gas

In this work we establish constraints on the temperature dependence of the shear viscosity $\eta$ in the superfluid phase of a dilute Fermi gas in the unitary limit. Our results are based on analyzing experiments that measure the aspect ratio of a deformed cloud after release from an optical trap. We discuss how to apply the two-fluid formalism to the unitary gas, and provide a suitable parametrization of the equation of state. We show that in expansion experiments the difference between the normal and superfluid velocities remains small, and can be treated as a perturbation. We find that expansion experiments favor a shear viscosity that decreases significantly in the superfluid regime. Using an exponential parametrization we find $\eta(T_c/(2T_F))<0.37\eta (T_c/T_F)$, where $T_c$ is the critical temperature, $T_F$ is the local Fermi temperature of the gas.
Adjustable Regret for Continuous Control of Conservatism and Competitive Ratio Analysis

A major issue of the increasingly popular robust optimization is the tendency to produce overly conservative solutions. This paper proposes a new parameterized robust criterion to oﬀer smooth control of conservatism without tampering with the uncertainty set. Unlike many other intractable criteria, its tractability is attained for common types of linear problems by reformulating them into traditional linear robust optimization problems. Many properties of it are studied to help analyze multistage robust optimization problems for closed-form solutions and give rise to a new approach to competitive ratio analysis. Finally, the new criterion is applied to the well-studied robust one-way trading problem to demonstrate its potential. A closed-form solution is obtained, which not only facilitates a numerical study of its smooth control of conservatism, but leads to a much simpler competitive ratio analysis.
BertGCN: Transductive Text Classification by Combining GNN and BERT

In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification. BertGCN constructs a heterogeneous graph over the dataset and represents documents as nodes using BERT representations. By jointly training the BERT and GCN modules within BertGCN, the proposed model is able to leverage the advantages of both worlds: large-scale pretraining which takes the advantage of the massive amount of raw data and transductive learning which jointly learns representations for both training data and unlabeled test data by propagating label influence through graph convolution. Experiments show that BertGCN achieves SOTA performances on a wide range of text classification datasets. Code is available at https://github.com/ZeroRin/BertGCN.
WildGait: Learning Gait Representations from Raw Surveillance Streams

Simple Summary In this work, we explore self-supervised pretraining for gait recognition. We gather the largest dataset to date of real-world gait sequences automatically annotated through pose tracking (UWG), which offers realistic confounding factors as opposed to current datasets. Results highlight the great performance in scenarios with low amounts of training data, and state-of-the-art accuracy on skeleton-based gait recognition when utilizing all available training data. Abstract The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera. We address the challenges of real-world scenarios in which camera feeds capture multiple people, who in most cases pass in front of the camera only once. We address privacy concerns by using only motion information of walking individuals, with no identifiable appearance-based information. As such, we propose a self-supervised learning framework, WildGait, which consists of pre-training a Spatio-Temporal Graph Convolutional Network on a large number of automatically annotated skeleton sequences obtained from raw, real-world surveillance streams to learn useful gait signatures. We collected and compiled the largest pretraining dataset to date of anonymized walking skeletons called Uncooperative Wild Gait, containing over 38k tracklets of anonymized walking 2D skeletons. We make the dataset available to the research community. Our results surpass the current state-of-the-art pose-based gait recognition solutions. Our proposed method is reliable in training gait recognition methods in unconstrained environments, especially in settings with scarce amounts of annotated data.
Distributed Graph Coloring Made Easy

In this paper we present a deterministic CONGEST algorithm to compute an O(kΔ)-vertex coloring in O(Δ/k)+łog^* n rounds, where Δ is the maximum degree of the network graph and 1łeq kłeq O(Δ) can be freely chosen. The algorithm is extremely simple: Each node locally computes a sequence of colors and then it "tries colors" from the sequence in batches of size k. Our algorithm subsumes many important results in the history of distributed graph coloring as special cases, including Linial's color reduction [Linial, FOCS'87], the celebrated locally iterative algorithm from [Barenboim, Elkin, Goldenberg, PODC'18], and various algorithms to compute defective and arbdefective colorings. Our algorithm can smoothly scale between these and also simplifies the state of the art (Δ+1)-coloring algorithm. At the cost of losing the full algorithm's simplicity we also provide a O(kΔ)-coloring algorithm in O(√Δ/k )+łog^* n rounds. We also provide improved deterministic algorithms for ruling sets, and, additionally, we provide a tight characterization for one-round color reduction algorithms.
A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries

Releasing the result size of conjunctive queries and graph pattern queries under differential privacy (DP) has received considerable attention in the literature, but existing solutions do not offer any optimality guarantees. We provide the first DP mechanism for this problem with a fairly strong notion of optimality, which can be considered as a natural relaxation of instance-optimality to a constant.