Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of the COVID-19 Pandemic on Mental Health in Low and Middle Income Countries

We track the effects of the COVID-19 pandemic on mental health in eight Low and Middle Income Countries (LMICs) in Asia, Africa, and South America utilizing repeated surveys of 21,162 individuals. Many respondents were interviewed over multiple rounds pre- and post-pandemic, allowing us to control for time trends and within-year seasonal variation in mental health. We demonstrate how mental health fluctuates with agricultural crop cycles, deteriorating during pre-harvest "lean" periods. Ignoring this seasonal variation leads to unreliable inferences about the effects of the pandemic. Controlling for seasonality, we document a large, significant, negative impact of the pandemic on mental health, especially during the early months of lockdown. In a random effects aggregation across samples, depression symptoms increased by around 0.3 standard deviations in the four months following the onset of the pandemic. The pandemic could leave a lasting legacy of depression. Absent policy interventions, this could have adverse long-term consequences, particularly in settings with limited mental health support services, which is characteristic of many LMICs.
Cardiovascular and Renal Outcomes among Patients with Type 2 Diabetes using SGLT2 Inhibitors added to Metformin: A Population-Based Cohort Study from the United Kingdom

Introduction: As large numbers of patients with type 2 diabetes receive treatment with a sodium-glucose co-transporter-2 inhibitor (SGLT2i), we investigated whether the cardio-renal preventative effects found in clinical trials are also seen in clinical practice where patient characteristics and adherence to treatment differs. Research design and methods: Using UK primary care electronic health records, we followed two cohorts of patients with type 2 diabetes prescribed metformin: SGLT2is (N=12,978) and a matched comparator of patients not using a SGLT2i at the start of follow-up (N=44,286). Independent follow-ups were performed to identify the study outcomes: Cox regression to estimate adjusted hazard ratios (HRs) for the study outcomes; cardiovascular (CV) composite outcome (comprising non-fatal myocardial infarction [MI] or ischaemic stroke [IS] requiring hospitalisation and CV death), severe renal disease, and all-cause mortality. Results: Mean follow-up was 2.3 years (SGLTi cohort) and 2.1 years (comparison cohort). Mean age was 60.4 years (SGLTi cohort) and 60.4 years (comparison cohort). SGLT2i new users were associated with a reduced risk of the CV composite (HR 0.75, 95% CI: 0.61 to 0.93), severe renal disease (HR 0.55, 95% CI: 0.46 to 0.67), and all-cause mortality (HR 0.56, 95% CI: 0.49 to 0.63), with risk reductions similar irrespective of baseline CKD. Reduced risks were seen for IS (HR 0.51, 95% CI: 0.36 to 0.74) but not MI (HR 0.98, 95% CI: 0.74 to 1.28). Results were consistent in sensitivity analyses. Conclusions: In this population-based study, SGLT2is were associated with significant CV, renal and survival benefits among individuals with type 2 diabetes on metformin; the CV benefit was driven by a reduced risk of ischaemic stroke.
Protocol for the Tessa Jowell BRAIN MATRIX Platform Study

Introduction Gliomas are the most common primary tumour of the central nervous system (CNS), with an estimated annual incidence of 6.6 per 100 000 individuals in the USA and around 14 deaths per day from brain tumours in the UK. The genomic and biological landscape of brain tumours has been increasingly defined and, since 2016, the WHO classification of tumours of the CNS incorporates molecular data, along with morphology, to define tumour subtypes more accurately. The Tessa Jowell BRAIN MATRIX Platform (TJBM) study aims to create a transformative clinical research infrastructure that leverages UK National Health Service resources to support research that is patient centric and attractive to both academic and commercial investors. Methods and analysis The TJBM study is a programme of work with the principal purpose to improve the knowledge of glioma and treatment for patients with glioma. The programme includes a platform study and subsequent interventional clinical trials (as separate protocols). The platform study described here is the backbone data-repository of disease, treatment and outcome data from clinical, imaging and pathology data being collected in patients with glioma from secondary care hospitals. The primary outcome measure of the platform is time from biopsy to integrated histological–molecular diagnosis using whole-genome sequencing and epigenomic classification. Secondary outcome measures include those that are process centred, patient centred and framework based. Target recruitment for the study is 1000 patients with interim analyses at 100 and 500 patients. Ethics and dissemination The study will be performed in accordance with the recommendations guiding physicians in biomedical research involving human subjects, adopted by the 18th World Medical Association General Assembly, Helsinki, Finland and stated in the respective participating countries’ laws governing human research, and Good Clinical Practice. The protocol was initially approved on 18 February 2020 by West Midlands – Edgbaston Research Ethics Committee; the current protocol (v3.0) was approved on 15 June 2022. Participants will be required to provide written informed consent. A meeting will be held after the end of the study to allow discussion of the main results among the collaborators prior to publication. The results of this study will be disseminated through national and international presentations and peer-reviewed publications. Manuscripts will be prepared by the Study Management Group and authorship will be determined by mutual agreement. Trial registration number NCT04274283, 18-Feb-2020; ISRCTN14218060, 03-Feb-2020.
A shadow perspective on equivariant Hochschild homologies

Shadows for bicategories, deﬁned by Ponto, provide a useful framework that gener- alizes classical and topological Hochschild homology. In this paper, we deﬁne Hochschild-type invariants for monoids in a symmetric monoidal, simplicial model category V , as well as for small V -categories. We show that each of these constructions extends to a shadow on an appropriate bicategory, which implies in particular that they are Morita invariant. We also deﬁne a gener- alized theory of Hochschild homology twisted by an automorphism and show that it is Morita invariant. Hochschild homology of Green functors and C n -twisted topological Hochschild homology ﬁt into this framework, which allows us to conclude that these theories are Morita invariant. We also study linearization maps relating the topological and algebraic theories, proving that the linearization map for topological Hochschild homology arises as a lax shadow functor, and constructing a new linearization map relating topological restriction homology and algebraic restriction homology. Finally, we construct a twisted Dennis trace map from the ﬁxed points of equivariant algebraic K -theory to twisted topological Hochschild homology.
PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening

The fusion of multispectral and panchromatic images is always dubbed pansharpening. Most of the available deep learning-based pan-sharpening methods sharpen the multispectral images through a one-step scheme, which strongly depends on the reconstruction ability of the network. However, remote sensing images always have large variations, as a result, these one-step methods are vulnerable to the error accumulation and thus incapable of preserving spatial details as well as the spectral information. In this paper, we propose a novel two-step model for pan-sharpening that sharpens the MS image through the progressive compensation of the spatial and spectral information. Firstly, a deep multiscale guided generative adversarial network is used to preliminarily enhance the spatial resolution of the MS image. Starting from the pre-sharpened MS image in the coarse domain, our approach then progressively reﬁnes the spatial and spectral residuals over a couple of generative adversarial networks (GANs) that have reverse architectures. The whole model is composed of triple GANs, and based on the speciﬁc architecture, a joint compensation loss function is designed to enable the triple GANs to be trained simultaneously. Moreover, the spatial-spectral residual compensation structure proposed in this paper can be extended to other pan-sharpening methods to further enhance their fusion results. Extensive experiments are performed on diﬀerent datasets and the results demonstrate the eﬀectiveness and eﬃciency of our proposed method.
A Machine-learning Approach to Enhancing eROSITA Observations

The eROSITA X-ray telescope, launched in 2019, is predicted to observe roughly 100,000 galaxy clusters. Follow-up observations of these clusters from Chandra, for example, will be needed to resolve outstanding questions about galaxy cluster physics. Deep Chandra cluster observations are expensive, and it is unfeasible to follow up every eROSITA cluster, therefore the objects that are chosen for follow-up must be chosen with care. To address this, we have developed an algorithm for predicting longer-duration, background-free observations, based on mock eROSITA observations. We make use of the hydrodynamic cosmological simulation Magneticum, simulate eROSITA instrument conditions using SIXTE, and apply a novel convolutional neural network to output a deep Chandra-like “super observation” of each cluster in our simulation sample. Any follow-up merit assessment tool should be designed with a specific use case in mind; our model produces observations that accurately and precisely reproduce the cluster morphology, which is a critical ingredient for determining a cluster’s dynamical state and core type. Our model will advance our understanding of galaxy clusters by improving follow-up selection, and it demonstrates that image-to-image deep learning algorithms are a viable method for simulating realistic follow-up observations.
Automatic Reward Design via Learning Motivation-Consistent Intrinsic Rewards

Reward design is a critical part of the application of reinforcement learning, the performance of which strongly depends on how well the reward signal frames the goal of the designer and how well the signal assesses progress in reaching that goal. In many cases, the extrinsic rewards provided by the environment (e.g., win or loss of a game) are very sparse and make it difficult to train agents directly. Researchers usually assist the learning of agents by adding some auxiliary rewards in practice. However, designing auxiliary rewards is often turned to a trial-and-error search for reward settings that produces acceptable results. In this paper, we propose to automatically generate goal-consistent intrinsic rewards for the agent to learn, by maximizing which the expected accumulative extrinsic rewards can be maximized. To this end, we introduce the concept of motivation which captures the underlying goal of maximizing certain rewards and propose the motivation based reward design method. The basic idea is to shape the intrinsic rewards by minimizing the distance between the intrinsic and extrinsic motivations. We conduct extensive experiments and show that our method performs better than the state-of-the-art methods in handling problems of delayed reward, exploration, and credit assignment.
A universal model for the evolution of tidally stripped systems

Accurate models of the structural evolution of dark matter subhaloes, as they orbit within larger systems, are fundamental to understanding the detailed distribution of dark matter at the present day. Numerical simulations of subhalo evolution support the idea that the mass loss associated with tidal stripping is most naturally understood in energy space, with the particles that are the least bound being removed first. Starting from this premise, we recently proposed a zero-parameter ‘energy-truncation model’ for subhalo evolution. We tested this model with simulations of tidal stripping of satellites with initial NFW profiles, and showed that the energy-truncation model accurately predicts both the mass loss and density profiles. In this work, we apply the model to a variety of Hernquist, Einasto and King profiles. We show that it matches the simulation results quite closely in all cases, indicating that it may serve as a universal model to describe tidally stripped collisionless systems. A key prediction of the energy-truncation model is that the central density of dark matter subhaloes is conserved as they lose mass; this has important implications for dark matter annihilation calculations, and for other observational tests of dark matter.
Code Comment Inconsistency Detection with BERT and Longformer

Comments, or natural language descriptions of source code, are standard practice among software developers. By communicating important aspects of the code such as functionality and usage, comments help with software project maintenance. However, when the code is modified without an accompanying correction to the comment, an inconsistency between the comment and code can arise, which opens up the possibility for developer confusion and bugs. In this paper, we propose two models based on BERT (Devlin et al., 2019) and Longformer (Beltagy et al., 2020) to detect such inconsistencies in a natural language inference (NLI) context. Through an evaluation on a previously established corpus of comment-method pairs both during and after code changes, we demonstrate that our models outperform multiple baselines and yield comparable results to the state-of-the-art models that exclude linguistic and lexical features. We further discuss ideas for future research in using pretrained language models for both inconsistency detection and automatic comment updating.
Multiple Attribute Fairness: Application to Fraud Detection

We propose a fairness measure relaxing the equality conditions in the popular equal odds fairness regime for classification. We design an iterative, model-agnostic, grid-based heuristic that calibrates the outcomes per sensitive attribute value to conform to the measure. The heuristic is designed to handle high arity attribute values and performs a per attribute sanitization of outcomes across different protected attribute values. We also extend our heuristic for multiple attributes. Highlighting our motivating application, fraud detection, we show that the proposed heuristic is able to achieve fairness across multiple values of a single protected attribute, multiple protected attributes. When compared to current fairness techniques, that focus on two groups, we achieve comparable performance across several public data sets.
The number of bounded‐degree spanning trees

For a graph G$$ G $$ , let ck(G)$$ {c}_k(G) $$ be the number of spanning trees of G$$ G $$ with maximum degree at most k$$ k $$ . For k≥3$$ k\ge 3 $$ , it is proved that every connected n$$ n $$ ‐vertex r$$ r $$ ‐regular graph G$$ G $$ with r≥nk+1$$ r\ge \frac{n}{k+1} $$ satisfies ck(G)1/n≥(1−on(1))r·zk,$$ {c}_k{(G)}^{1/n}\ge \left(1-{o}_n(1)\right)r\cdotp {z}_k, $$where zk>0$$ {z}_k>0 $$ approaches 1 extremely fast (e.g., z10=0.999971$$ {z}_{10}=0.999971 $$ ). The minimum degree requirement is essentially tight as for every k≥2$$ k\ge 2 $$ there are connected n$$ n $$ ‐vertex r$$ r $$ ‐regular graphs G$$ G $$ with r=⌊n/(k+1)⌋−2$$ r=\left\lfloor n/\left(k+1\right)\right\rfloor -2 $$ for which ck(G)=0$$ {c}_k(G)=0 $$ . Regularity may be relaxed, replacing r$$ r $$ with the geometric mean of the degree sequence and replacing zk$$ {z}_k $$ with zk∗>0$$ {z}_k^{\ast }>0 $$ that also approaches 1, as long as the maximum degree is at most n(1−(3+ok(1))lnk/k)$$ n\left(1-\left(3+{o}_k(1)\right)\sqrt{\ln k/k}\right) $$ . The same holds with no restriction on the maximum degree as long as the minimum degree is at least nk(1+ok(1))$$ \frac{n}{k}\left(1+{o}_k(1)\right) $$ .
Global spherically symmetric solutions to degenerate compressible Navier-Stokes equations with large data and far field vacuum

We consider the initial-boundary value problem (IBVP) for the isentropic compressible Navier-Stokes equations ( CNS ) in the domain exterior to a ball in R d ( d = 2 or 3). When viscosity coeﬃcients are given as a constant multiple of the mass density ρ , based on some analysis of the nonlinear structure of this system, we prove the global existence of the unique spherically symmetric classical solution for (large) initial data with spherical symmetry and far ﬁeld vacuum in some inhomogeneous Sobolev spaces. Moreover, the solutions we obtained have the conserved total mass and ﬁnite total energy. ρ keeps positive in the domain considered but decays to zero in the far ﬁeld, which is consistent with the facts that the total mass is conserved, and CNS is a model of non-dilute ﬂuids where ρ is bounded away from the vacuum. To prove the existence, on the one hand, we consider a well-designed reformulated structure by introducing some new variables, which, actually, can transfer the degeneracies of the time evolution and the viscosity to the possible singularity of some special source terms. On the other hand, it is observed that, for the spherically symmetric ﬂow, the radial projection of the so-called eﬀective velocity v = U + ∇ ϕ ( ρ ) ( U is the velocity of the ﬂuid, and ϕ ( ρ ) is a function of ρ deﬁned via the shear viscosity coeﬃcient µ ( ρ ): ϕ ′ ( ρ ) = 2 µ ( ρ ) /ρ 2 ), veriﬁes a damped transport equation which provides the possibility to obtain its upper bound. Then combined with the BD entropy estimates, one can obtain the required uniform a priori estimates of the solution. It is worth pointing out that the frame work on the well-posedness theory established here can be applied to the shallow water equations.
Deep Reinforcement Learning for System-on-Chip: Myths and Realities

Neural schedulers based on deep reinforcement learning (DRL) have shown considerable potential for solving real-world resource allocation problems, as they have demonstrated significant performance gain in the domain of cluster computing. In this paper, we investigate the feasibility of neural schedulers for the domain of System-on-Chip (SoC) resource allocation through extensive experiments and comparison with non-neural, heuristic schedulers. The key finding is three-fold. First, neural schedulers designed for cluster computing domain do not work well for SoC due to i) heterogeneity of SoC computing resources and ii) variable action set caused by randomness in incoming jobs. Second, our novel neural scheduler technique, Eclectic Interaction Matching (EIM), overcomes the above challenges, thus significantly improving the existing neural schedulers. Specifically, we rationalize the underlying reasons behind the performance gain by the EIM-based neural scheduler. Third, we discover that the ratio of the average processing elements (PE) switching delay and the average PE computation time significantly impacts the performance of neural SoC schedulers even with EIM. Consequently, future neural SoC scheduler design must consider this metric as well as its implementation overhead for practical utility.
On the Maximum Gonality of a Curve over a Finite Field

The gonality of a smooth geometrically connected curve over a field $k$ is the smallest degree of a nonconstant $k$-morphism from the curve to the projective line. In general, the gonality of a curve of genus $g \ge 2$ is at most $2g - 2$. Over finite fields, a result of F.K. Schmidt from the 1930s can be used to prove that the gonality is at most $g+1$. Via a mixture of geometry and computation, we improve this bound: for a curve of genus $g \ge 5$ over a finite field, the gonality is at most $g$. For genus $g = 3$ and $g = 4$, the same result holds with exactly $217$ exceptions: There are two curves of genus $4$ and gonality $5$, and $215$ curves of genus $3$ and gonality $4$. The genus-$4$ examples were found in other papers, and we reproduce their equations here; in supplementary material, we provide equations for the genus-$3$ examples.
Lyα Escape from Low-mass, Compact, High-redshift Galaxies

We investigate the effects of stellar populations and sizes on Lyα escape in 27 spectroscopically confirmed and 35 photometric Lyα emitters (LAEs) at z ≈ 2.65 in seven fields of the Boötes region of the NOAO Deep Wide-Field Survey. We use deep HST/WFC3 imaging to supplement ground-based observations and infer key galaxy properties. Compared to typical star-forming galaxies (SFGs) at similar redshifts, the LAEs are less massive (M ⋆ ≈ 107–109 M ⊙), younger (ages ≲1 Gyr), smaller (r e < 1 kpc), and less dust-attenuated (E(B−V) ≤ 0.26 mag) but have comparable star formation rates (SFRs ≈ 1–100 M ⊙ yr−1). Some of the LAEs in the sample may be very young galaxies having low nebular metallicities (Z neb ≲ 0.2 Z ⊙) and/or high ionization parameters ( log(U)≳−2.4 ). Motivated by previous studies, we examine the effects of the concentration of star formation and gravitational potential on Lyα escape by computing SFR surface density, ΣSFR, and specific SFR surface density, ΣsSFR. For a given ΣSFR, the Lyα escape fraction is higher for LAEs with lower stellar masses. The LAEs have a higher ΣsSFR, on average, compared to SFGs. Our results suggest that compact star formation in a low gravitational potential yields conditions amenable to the escape of Lyα photons. These results have important implications for the physics of Lyα radiative transfer and for the type of galaxies that may contribute significantly to cosmic reionization.
Verification of the Quantum Jarzynski Equality on Digital Quantum Computers

The quantum Jarzynski equality and the Crooks relation are fundamental laws connecting equilibrium processes with nonequilibrium fluctuations. While they are well established theoretically and also experimental realizations for simple few-body systems already exist, a verification in the quantum many-body regime is still missing. Here, we present results for nonequilibrium protocols in systems with up to sixteen interacting degrees of freedom obtained on trapped ion and superconducting qubit quantum computers, which verify the quantum Jarzynski equality and the Crooks relation in the many-body regime. To achieve this, we overcome present-day limitations in the preparation of thermal ensembles and in the measurement of work distributions on noisy intermediate-scale quantum devices. We discuss the accuracy to which the Jarzynski equality holds on different quantum computing platforms subject to platform-specific errors. Our analysis reveals a novel dissipative nonequilibrium regime, where a fast unitary drive compensates for dissipation and restores the validity of Jarzynski's equality.
Phase Code Discovery for Pulse Compression Radar: A Genetic Algorithm Approach

Discovering sequences with desired properties has long been an interesting intellectual pursuit. In pulse compression radar (PCR), discovering phase codes with low aperiodic autocorrelations is essential for a good estimation performance. The design of phase code, however, is mathematically non-trivial as the aperiodic autocorrelation properties of a sequence are intractable to characterize. In this paper, we put forth a genetic algorithm (GA) approach to discover new phase codes for PCR with the mismatched filter (MMF) receiver. The developed GA, dubbed GASeq, discovers better phase codes than the state of the art. At a code length of 59, the sequence discovered by GASeq achieves a signal-to-clutter ratio (SCR) of 50.84, while the best-known sequence has an SCR of 45.16. In addition, the efficiency and scalability of GASeq enable us to search phase codes with a longer code length, which thwarts existing deep learning-based approaches. At a code length of 100, the best phase code discovered by GASeq exhibit an SCR of 63.23.