Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Men’s Labour Migration on Non-migrating Spouses’ Health: A Systematic Review

International migration is in an increasing trend globally; internal migration is also very common, particularly in low-and middle-income countries (LMICs). Little attention has been paid to the impact of men’s migration on non-migrating women’s health. Therefore, we undertook a systematic review to examine the impact of men's migration on the health of women who remain behind in LMICs. We searched five databases, CINAHL, Google Scholar, PsychINFO, PubMed and Scopus, for publications from 2005 to 2022 using key search terms 'left-behind', 'women' and 'migration'. Thirty-three peer-reviewed publications were included in the review. Findings suggest that left-behind women had increased access to healthcare due to better financial positions (via remittances) and experienced more empowerment/autonomy in the absence of their husbands. This resulted in increased (a) decision-making regarding their health and (b) freedom of mobility to seek healthcare. Remittances led to improved food and housing security, a critical wider determinant of health. However, some studies reported that in the longer term, the physical health of women who remain behind was negatively impacted. Almost all studies on mental health reported higher depressive symptoms among migrant wives compared to women co-habiting with their spouses. Left-behind women feared contracting sexually transmitted infections from their migrant partners. National and local policies should include support groups and counselling services at the local health centre for women who remain behind. We recommend further studies on the areas presented above as well as unexplored areas such as vulnerability to violence and impact of remittance on health and nutrition.
Effect of sesame + pulses intercropping systems on growth, yield and quality parameters of sesame

A field experiment was conducted to study the intercropping of sesame with black gram & green gram during pre-kharif season of 2015 at agricultural farm, Palli Siksha Bhavana, Visva-Bharati, Sriniketan, Birbhum, West Bengal in red and lateritic soil. The experiment was conducted by following Randomized Block Design (RBD) and 12 treatments were replicated thrice. The treatments were T1-sole sesame (30 cm spaced uniform row), T2-sole sesame (40 cm spaced uniform row), T3 - sole sesame (60 cm spaced paired row), T4 -sole sesame (100 cm spaced four row), T5 - sole black gram (15 cm spaced row), T6 - sole green gram (40 cm spaced row), T7-sesame + black gram (1:1 in 40 cm spaced row), T8 - sesame + green gram (1:1 in 40 cm spaced row), T9-sesame + blackgram (2:2 in 20/60 cm spaced row), T10-sesame + green gram (2:2 in 20/60 cm spaced row), T11-sesame + blackgram (4:4 in 20/100 cm spaced row), T12 -sesame + green gram (4:4 in 20/100 cm spaced row). Different growth parameters such as plant height and no. of branches plant -1 recorded higher than different intercropping systems. Whereas sesame + green gram (4:4) recorded highest dry matter accumulation (45.97 g m -2 ). Intercropping of sesame with green gram and black gram also showed higher yield attributes such as no. of capsules plant - 1 , no. of seeds capsule -1 and test weight. Highest grain yield, oil yield and protein content was obtained from sole sesame (30 cm spaced uniform row) followed by sesame + blackgram (2:2 in 20/60 cm spaced row) and also remained at par.
Evidence of Charge-Phonon coupling in Van der Waals materials Ni1-xZnxPS3

NiPS3 is a Van der Waals antiferromagnet that has been found to display spin-charge and spin-phonon coupling in its antiferromagnetically ordered state below TN = 155 K. Here, we study high-quality crystals of site-diluted Ni1-xZnxPS3 (0<x<0.2) using temperature-dependent specific heat and Raman spectroscopy probes. The site dilution suppresses the antiferromagnetic ordering in accordance with the mean-field prediction. In NiPS3, we show that the phonon mode P2 (176 cm-1) associated with Ni vibrations show a distinct asymmetry due to the Fano resonance, which persists only in the paramagnetic phase, disappearing below T_N = 155 K. This was further supported by temperature-dependent Raman data on an 8% Zn-doped crystal (T_N = 135 K) where Fano resonance similarly van in the magnetically ordered phase. This is contrary to the behaviour of the Raman mode P9 (570 cm-1), which shows a Fano resonance at low temperatures below T_N due to its coupling with the two-magnon continuum. We show that the Fano resonance of P2 arises from its coupling with an electronic continuum that weakens considerably upon cooling to low temperatures. In the doped crystals, the Fano coupling is found to enhance with Zn-doping. These observations suggest the presence of strong electron-phonon coupling in the paramagnetic phase of NiPS3 due to charge density fluctuations associated with the negative charge transfer state of Ni.
Background Pycnocline Depth Constrains Future Ocean Heat Uptake Efficiency

The Ocean Heat Uptake Efficiency (OHUE) quantifies the ocean's ability to mitigate surface warming through deep heat sequestration. Despite its importance, the main controls on OHUE, and on its two‐fold spread across contemporary climate models, remain unclear. We argue that OHUE is primarily controlled by mid‐latitude ventilation strength in the background climate, itself related to pycnocline depth and stratification. This hypothesis is supported by a strong correlation between mid‐latitude (30–60°) OHUE and the near‐global average (60°S–60°N) pycnocline depth in CMIP5 and CMIP6 AOGCMs under RCP85/SSP585, and in a parameter perturbation ensemble of ocean GCM (MITgcm) experiments. This correlation explains about 70% of the CMIP5‐6 spread in global OHUE. The relationship provides a pathway toward observationally constraining OHUE, and thus reducing uncertainty in projections of future global climate change and sea level rise.
Image and Attribute Based Convolutional Neural Network Inference Attacks in Social Networks

In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. At the same time, neural networks have been swiftly developed in recent years, and are proven to be very effective in inference attacks. This article proposes a new framework for inference attacks in social networks, which smartly integrates and modifies the existing state-of-the-art convolutional neural network (CNN) models. As a result, the framework can fit wider applicable scenarios for inference attacks no matter whether a user has a legit profile image or not. Moreover, the framework is able to boost the existing high-accuracy CNN for sensitive information prediction. In addition to the framework, the article also shows the detailed configuration of fully connected neural networks (FCNNs) for inference attacks. This part is usually missing in the existing studies. Furthermore, traditional machine learning algorithms are implemented to compare the results from the constructed FCNN. Last but not least, this article also discusses that applying differential privacy (DP) can effectively undermine the accuracy of inference attacks in social networks.
Edge Guided GANs With Multi-Scale Contrastive Learning for Semantic Image Synthesis

We propose a novel edge guided generative adversarial network with contrastive learning (ECGAN) for the challenging semantic image synthesis task. Although considerable improvements have been achieved by the community in the recent period, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural information, making it challenging to synthesize local details and structures; 2) The widely adopted CNN operations such as convolution, down-sampling, and normalization usually cause spatial resolution loss and thus cannot fully preserve the original semantic information, leading to semantically inconsistent results (e.g., missing small objects); 3) Existing semantic image synthesis methods focus on modeling “local” semantic information from a single input semantic layout. However, they ignore “global” semantic information of multiple input semantic layouts, i.e., semantic cross-relations between pixels across different input layouts. To tackle 1), we propose to use the edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module. Edge information is produced by a convolutional generator and introduces detailed structure information. To tackle 2), we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout to preserve the semantic information. To tackle 3), inspired by current methods in contrastive learning, we propose a novel contrastive learning method, which aims to enforce pixel embeddings belonging to the same semantic class to generate more similar image content than those from different classes. We further propose a novel multi-scale contrastive learning method that aims to push same-class features from different scales closer together being able to capture more semantic relations by explicitly exploring the structures of labeled pixels from multiple input semantic layouts from different scales. Experiments on three challenging datasets show that our methods achieve significantly better results than state-of-the-art approaches.
QoE-Driven Video Transmission: Energy-Efficient Multi-UAV Network Optimization

This article is concerned with the issue of improving video subscribers' quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers' QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizing the multi-UAV network in terms of serving UAV selection, UAV trajectory, and UAV transmit power. The dynamic multi-UAV network optimization problem is formulated as a challenging sequential-decision problem with the goal of maximizing subscribers' QoE while minimizing the total network power consumption, subject to some physical resource constraints. We propose a novel network optimization algorithm to solve this challenging problem, in which a Lyapunov technique is first explored to decompose the sequential-decision problem into several repeatedly optimized sub-problems to avoid the curse of dimensionality. To solve the sub-problems, iterative and approximate optimization mechanisms with provable performance guarantees are then developed. Finally, we design extensive simulations to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 66.75% more energy-efficient than benchmarks.
Quantum geometry and Landau levels of quadratic band crossings

We study the relation between the quantum geometry of wave functions and the Landau level (LL) spectrum of two-band Hamiltonians with a quadratic band crossing point (QBCP) in two-dimensions. By investigating the influence of interband coupling parameters on the wave function geometry of general QBCPs, we demonstrate that the interband coupling parameters can be entirely determined by the projected elliptic image of the wave functions on the Bloch sphere, which can be characterized by three parameters, i.e., the major $d_1$ and minor $d_2$ diameters of the ellipse, and one angular parameter $\phi$ describing the orientation of the ellipse. These parameters govern the geometric properties of the system such as the Berry phase and modified LL spectra. Explicitly, by comparing the LL spectra of two quadratic band models with and without interband couplings, we show that the product of $d_1$ and $d_2$ determines the constant shift in LL energy while their ratio governs the initial LL energies near a QBCP. Also, by examining the influence of the rotation and time-reversal symmetries on the wave function geometry, we construct a minimal continuum model which exhibits various wave function geometries. We calculate the LL spectra of this model and discuss how interband couplings give LL structure for dispersive bands as well as nearly flat bands.
Clustering MIC data through Bayesian mixture models: an application to detect M. Tuberculosis resistance mutations

Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to standard phenotypic tests; second, it is possible to statistically associate the phenotypic patterns of resistance to specific mutations in the genome. Therefore, it is now possible to develop catalogues of genomic variants associated with resistance to specific antibiotics, in order to improve prediction of resistance and suggest treatments. It is essential to have robust methods for identifying mutations associated to resistance and continuously updating the available catalogues. This work proposes a general method to study minimal inhibitory concentration (MIC) distributions and to identify clusters of strains showing different levels of resistance to antimicrobials. Once the clusters are identified and strains allocated to each of them, it is possible to perform regression method to identify with high statistical power the mutations associated with resistance. The method is applied to a new 96-well microtiter plate used for testing M. Tuberculosis.
Fragmented superconductivity in the Hubbard model as solitons in Ginzburg-Landau theory

The phenomena of superconductivity and charge density waves are observed in close vicinity in many strongly correlated materials. Increasing evidence from experiments and numerical simulations suggests both phenomena can also occur in an intertwined manner, where the superconducting order parameter is coupled to the electronic density. Employing density matrix renormalization group simulations, we investigate the nature of such an intertwined state of matter stabilized in the phase diagram of the elementary $t$-$t^\prime$-$U$ Hubbard model in the strong coupling regime. Remarkably, the condensate of Cooper pairs is shown to be fragmented in the presence of a charge density wave where more than one pairing wave function is macroscopically occupied. Moreover, we provide conclusive evidence that the macroscopic wave functions of the superconducting fragments are well-described by soliton solutions of a Ginzburg-Landau equation in a periodic potential constituted by the charge density wave. In the presence of an orbital magnetic field, the order parameters are gauge invariant, and superconducting vortices are pinned between the stripes. This intertwined Ginzburg-Landau theory is proposed as an effective low-energy description of the stripe fragmented superconductor.
On the cohomology of measurable sets

If T is an ergodic automorphism of a Lebesgue probability space (X,A,m), the set of coboundries B = db =T(b)+b with symmetric difference + form a subgroup of the set of cocycles A. Using tools from descriptive set theory, Greg Hjorth showed in 1995 that the first cohomology group H=A/B is uncountable. This can surprise, given that in the case of a finite ergodic probability space, H has only 2 elements. Hjorth's proof used descriptive set theory in the complete metric space (A,d(a,b)=m(a+b)), leading to the statement that B is meager in A. We use a spectral genericity result of Barry Simon to establish the same. It leads to the statement noted first by Karl Petersen in 1973 that for a generic a in A, the induced system T_a is weakly mixing, which is slightly stronger than a result of Nate Friedman and Donald Ornstein about density of weakly mixing in the space of all induced systems T_a coming from an ergodic automorphism T.
Identification Robust Inference for the Risk Premium in Term Structure Models

We propose identification robust statistics for testing hypotheses on the risk premia in dynamic affine term structure models. We do so using the moment equation specification proposed for these models in Adrian et al. (2013). We extend the subset (factor) Anderson-Rubin test from Guggenberger et al. (2012) to models with multiple dynamic factors and time-varying risk prices. Unlike projection-based tests, it provides a computationally tractable manner to conduct identification robust tests on a larger number of parameters. We analyze the potential identification issues arising in empirical studies. Statistical inference based on the three-stage estimator from Adrian et al. (2013) requires knowledge of the factors' quality and is misleading without full-rank beta's or with sampling errors of comparable size as the loadings. Empirical applications show that some factors, though potentially weak, may drive the time variation of risk prices, and weak identification issues are more prominent in multi-factor models.
Violation of Ferromagnetic Ordering of Energy Levels in Spin Rings by Weak Paramagnetism of the Singlet

For the quantum Heisenberg antiferromagnet with spins-$j$ on a bipartite, balanced graph, the Lieb-Mattis"Ordering of energy levels"theorem guarantees that the ground state is a spin singlet. Moreover, defining $E^{\textrm{AF}}_{\min}(S)$ to be the minimum eigenvalue of the Hamiltonian in the invariant subspace consisting of all total-spin $S$ vectors, the Lieb-Mattis theorem guarantees the function $E^{\textrm{AF}}_{\min}(S)$ is monotonically increasing for $0\leq S\leq jN$ where $N$ is the number of vertices. For the ferromagnet, defining $E_{\min}^{\textrm{FM}}(S)$ to be the minimum energy in the total spin-$S$ sector, the absolute ground state is $E_{\min}^{\textrm{FM}}(jN)$. We say that the graph satisfies"ferromagnetic ordering of energy levels"at order $n$, or FOEL-$n$, if : $\forall m$ such that $m \leq n$ and $\forall \ell$ such that $\ell \geq m$, we have $E_{\min}^{\mathrm{FM}}(jN-m)\leq E_{\min}^{\mathrm{FM}}(jN-\ell)$. Caputo, Liggett and Richthammer proved a theorem which generally implies FOEL-$1$ is true. We discuss the apparent fact that for an even length spin ring of length $L>4$, FOEL-$n$ is violated for $n=jL-1$. The reason is that $E_{\min}^{\mathrm{FM}}(0)4$. We consider $E_{\min}^{\mathrm{FM}}(1)-E_{\min}^{\mathrm{FM}}(0)$ using the single mode approximation, to help explain this. We supplement this with numerical computation. Using the Bethe ansatz, Sutherland already considered the spin ring with $j=1/2$ and notably proved weak paramagnetism, which was later also considered by Dhar and Shastry. We review all that.
A Generalized Schwarz-type Non-overlapping Domain Decomposition Method using Physics-constrained Neural Networks

We present a meshless Schwarz-type non-overlapping domain decomposition method based on artificial neural networks for solving forward and inverse problems involving partial differential equations (PDEs). To ensure the consistency of solutions across neighboring subdomains, we adopt a generalized Robin-type interface condition, assigning unique Robin parameters to each subdomain. These subdomain-specific Robin parameters are learned to minimize the mismatch on the Robin interface condition, facilitating efficient information exchange during training. Our method is applicable to both the Laplace's and Helmholtz equations. It represents local solutions by an independent neural network model which is trained to minimize the loss on the governing PDE while strictly enforcing boundary and interface conditions through an augmented Lagrangian formalism. A key strength of our method lies in its ability to learn a Robin parameter for each subdomain, thereby enhancing information exchange with its neighboring subdomains. We observe that the learned Robin parameters adapt to the local behavior of the solution, domain partitioning and subdomain location relative to the overall domain. Extensive experiments on forward and inverse problems, including one-way and two-way decompositions with crosspoints, demonstrate the versatility and performance of our proposed approach.
Learning Provably Robust Estimators for Inverse Problems via Jittering

Deep neural networks provide excellent performance for inverse problems such as denoising. However, neural networks can be sensitive to adversarial or worst-case perturbations. This raises the question of whether such networks can be trained efficiently to be worst-case robust. In this paper, we investigate whether jittering, a simple regularization technique that adds isotropic Gaussian noise during training, is effective for learning worst-case robust estimators for inverse problems. While well studied for prediction in classification tasks, the effectiveness of jittering for inverse problems has not been systematically investigated. In this paper, we present a novel analytical characterization of the optimal $\ell_2$-worst-case robust estimator for linear denoising and show that jittering yields optimal robust denoisers. Furthermore, we examine jittering empirically via training deep neural networks (U-nets) for natural image denoising, deconvolution, and accelerated magnetic resonance imaging (MRI). The results show that jittering significantly enhances the worst-case robustness, but can be suboptimal for inverse problems beyond denoising. Moreover, our results imply that training on real data which often contains slight noise is somewhat robustness enhancing.
Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models

Recent advancements in natural language and Large Language Models (LLMs) have enabled AI agents to simulate human-like interactions within virtual worlds. However, these interactions still face limitations in complexity and flexibility, particularly in scenarios involving multiple characters and novel objects. Pre-defining all interactable objects in the agent's world model presents challenges, and conveying implicit intentions to multiple characters through complex interactions remains difficult. To address these issues, we propose integrating virtual Game Masters (GMs) into the agent's world model, drawing inspiration from Tabletop Role-Playing Games (TRPGs). GMs play a crucial role in overseeing information, estimating players' intentions, providing environment descriptions, and offering feedback, compensating for current world model deficiencies. To facilitate future explorations for complex interactions, we introduce a benchmark named Tachikuma, comprising a Multiple character and novel Object based interaction Estimation (MOE) task and a supporting dataset. MOE challenges models to understand characters' intentions and accurately determine their actions within intricate contexts involving multi-character and novel object interactions. Besides, the dataset captures log data from real-time communications during gameplay, providing diverse, grounded, and complex interactions for further explorations. Finally, we present a simple prompting baseline and evaluate its performance, demonstrating its effectiveness in enhancing interaction understanding. We hope that our dataset and task will inspire further research in complex interactions with natural language, fostering the development of more advanced AI agents.