Miracle-dz/UniFAR-FTUs huggingface dataset:
FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning

Deep learning models have achieved great success in automating skin lesion diagnosis. However, the ethnic disparity in these models' predictions, where lesions on darker skin types are usually underrepresented and have lower diagnosis accuracy, receives little attention. In this paper, we propose FairDisCo, a disentanglement deep learning framework with contrastive learning that utilizes an additional network branch to remove sensitive attributes, i.e. skin-type information from representations for fairness and another contrastive branch to enhance feature extraction. We compare FairDisCo to three fairness methods, namely, resampling, reweighting, and attribute-aware, on two newly released skin lesion datasets with different skin types: Fitzpatrick17k and Diverse Dermatology Images (DDI). We adapt two fairness-based metrics DPM and EOM for our multiple classes and sensitive attributes task, highlighting the skin-type bias in skin lesion classification. Extensive experimental evaluation demonstrates the effectiveness of FairDisCo, with fairer and superior performance on skin lesion classification tasks.
Spacetimes with No Position Drift

This paper compares three criteria for a spacetime to be free of position drift: those by Hasse and Perlick (HP), Krasi\'nski and Bolejko (KB) and Korzy\'nski and Kopi\'nski (KK). A spacetime having no position drift means that every observer sees all light sources in unchanging directions. The following is shown: (1) The HP criterion is a necessary condition for the KK criterion to apply. (2) If the spacetime metric obeys the Einstein equations with a perfect fluid source, then another necessary condition for the KK criterion is the Weyl tensor being zero. (3) Result (2) points to the Stephani metric, so it is shown that this metric obeys an equation which is still one more necessary condition for the KK criterion. (4) The general Szekeres metrics become drift-free by the KK criterion only in the Friedmann limit. (5) The HP and KB criteria coincide, and the HP zero-drift condition imposes on the Stephani metric the same restriction as found by Krasi\'nski and Bolejko (KB). The relations between the three criteria are displayed and compared in a diagram.
Towards a Classification of pseudo-Riemannian Geometries Admitting Twistor Spinors

We show that given a conformal structure whose holonomy representation fixes a totally lightlike subspace of arbitrary dimension, there is always a local metric in the conformal class off a singular set which is Ricci-isotropic and gives rise to a parallel, totally lightlike distribution on the tangent bundle. This naturally applies to parallel spin tractors resp. twistor spinors on conformal spin manifolds and clarifies which twistor spinors are locally equivalent to parallel spinors. Moreover, we study the zero set of a twistor spinor using the curved orbit decomposition for parabolic geometries. We can completely describe its local structure, construct a natural projective structure on it, and show that locally every twistor spinor with zero is equivalent to a parallel spinor off the zero set. An application of these results in low-dimensional split-signatures leads to a complete geometric description of local geometries admitting non-generic twistor spinors in signatures (3,2) and (3,3) which complements the well-known description of the generic case.
Prioritizing Samples in Reinforcement Learning with Reducible Loss

Most reinforcement learning algorithms take advantage of an experience replay buffer to repeatedly train on samples the agent has observed in the past. Not all samples carry the same amount of significance and simply assigning equal importance to each of the samples is a na\"ive strategy. In this paper, we propose a method to prioritize samples based on how much we can learn from a sample. We define the learn-ability of a sample as the steady decrease of the training loss associated with this sample over time. We develop an algorithm to prioritize samples with high learn-ability, while assigning lower priority to those that are hard-to-learn, typically caused by noise or stochasticity. We empirically show that our method is more robust than random sampling and also better than just prioritizing with respect to the training loss, i.e. the temporal difference loss, which is used in prioritized experience replay.
Extreme value statistics of the halo and stellar mass distributions at high redshift: are JWST results in tension with ΛCDM?

The distribution of dark matter halo masses can be accurately predicted in the ΛCDM cosmology. The presence of a single massive halo or galaxy at a particular redshift, assuming some baryon and stellar fraction for the latter, can therefore be used to test the underlying cosmological model. A number of recent measurements of very large galaxy stellar masses at high redshift (z > 8) motivate an investigation into whether any of these objects are in tension with ΛCDM. We use extreme value statistics to generate confidence regions in the mass-redshift plane for the most extreme mass haloes and galaxies. Tests against numerical models show no tension, neither in their dark matter halo masses nor their galaxy stellar masses. However, we find tentative >3σ tension with recent observational determinations of galaxy masses at high redshift from both HST & JWST, despite using conservative estimates for the stellar fraction (f⋆ ∼ 1). Either these galaxies are in tension with ΛCDM, or there are unaccounted for uncertainties in their stellar mass or redshift estimates.
Exploiting Instance and Variable Similarity to Improve Learning-Enhanced Branching

In many operational applications, it is necessary to routinely find, within a very limited time window, provably good solutions to challenging mixed-integer linear programming (MILP) problems. An example is the Security-Constrained Unit Commitment (SCUC) problem, solved daily to clear the day-ahead electricity markets. Previous research demonstrated that machine learning (ML) methods can produce high-quality heuristic solutions to combinatorial problems, but proving the optimality of these solutions, even with recently-proposed learning-enhanced branching methods, can still be time-consuming. In this paper, we propose a simple modification to improve the performance of learning-enhanced branching methods based on the key observation that, in such operational applications, instances are significantly similar to each other. Specifically, instances typically share the same size and problem structure, with slight differences only on matrix coefficients, right-hand sides and objective function. In addition, certain groups of variables within a given instance are also typically similar to each other. Therefore, unlike previous works in the literature which predicted all branching scores with a single ML model, we propose training separate ML models per variable or per groups of variables, based on their similarity. We evaluate this enhancement on realistic large-scale SCUC instances and we obtain significantly better gap closures than previous works with the same amount of training data.
A cubulation with no factor system

The primary method for showing that a given cubulated group is hierarchically hyperbolic is by constructing a factor system on the cube complex. In this paper we show that such a construction is not always possible, namely we construct a cubulated group for which the cube complex does not have a factor system. We also construct a cubulated group for which the induced action on the contact graph is not acylindrical.
Landscape modification meets spin systems: from torpid to rapid mixing, tunneling and annealing in the low-temperature regime

Given a target Gibbs distribution $\pi^0_{\beta} \propto e^{-\beta \mathcal{H}}$ to sample from in the low-temperature regime on $\Sigma_N := \{-1,+1\}^N$, in this paper we propose and analyze Metropolis dynamics that instead target an alternative distribution $\pi^{f}_{\alpha,c,1/\beta} \propto e^{-\mathcal{H}^{f}_{\alpha,c,1/\beta}}$, where $\mathcal{H}^{f}_{\alpha,c,1/\beta}$ is a transformed Hamiltonian whose landscape is suitably modified and controlled by the parameters $f,\alpha,c$ and $\beta$ and shares the same set of stationary points as $\mathcal{H}$. With appropriate tuning of these parameters, the major advantage of the proposed Metropolis dynamics on the modified landscape is that it enjoys an $\mathcal{O}(1)$ critical height while its stationary distribution $\pi^{f}_{\alpha,c,1/\beta}$ maintains close proximity with the original target $\pi^0_{\beta}$ in the low-temperature. We prove rapid mixing and tunneling on the modified landscape with polynomial dependence on the system size $N$ and the inverse temperature $\beta$, while the original Metropolis dynamics mixes torpidly with exponential dependence on the critical height and $\beta$. In the setting of simulated annealing, we prove its long-time convergence under a power-law cooling schedule that is faster than the typical logarithmic cooling in the classical setup. We illustrate our results on a host of models including the Ising model on various deterministic and random graphs as well as Derrida's Random Energy Model. In these applications, the original dynamics mixes torpidly while the proposed dynamics on the modified landscape mixes rapidly with polynomial dependence on both $\beta$ and $N$ and find the approximate ground state provably in $\mathcal{O}(N^4)$ time. This paper highlights a novel use of the geometry and structure of the landscape to the design of accelerated samplers or optimizers.
Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective

The Vision Transformer has emerged as a powerful tool for image classification tasks, surpassing the performance of convolutional neural networks (CNNs). Recently, many researchers have attempted to understand the robustness of Transformers against adversarial attacks. However, previous researches have focused solely on perturbations in the spatial domain. This paper proposes an additional perspective that explores the adversarial robustness of Transformers against frequency-selective perturbations in the spectral domain. To facilitate comparison between these two domains, an attack framework is formulated as a flexible tool for implementing attacks on images in the spatial and spectral domains. The experiments reveal that Transformers rely more on phase and low frequency information, which can render them more vulnerable to frequency-selective attacks than CNNs. This work offers new insights into the properties and adversarial robustness of Transformers.
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEiT-3, which achieves state-of-the-art transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: backbone architecture, pretraining task, and model scaling up. We introduce Multiway Transformers for general-purpose modeling, where the modular architecture enables both deep fusion and modality-specific encoding. Based on the shared backbone, we perform masked"language"modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner. Experimental results show that BEiT-3 obtains state-of-the-art performance on object detection (COCO), semantic segmentation (ADE20K), image classification (ImageNet), visual reasoning (NLVR2), visual question answering (VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO).
Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild

Compared with the image-based static facial expression recognition (SFER) task, the dynamic facial expression recognition (DFER) task based on video sequences is closer to the natural expression recognition scene. However, DFER is often more challenging. One of the main reasons is that video sequences often contain frames with different expression intensities, especially for the facial expressions in the real-world scenarios, while the images in SFER frequently present uniform and high expression intensities. Nevertheless, if the expressions with different intensities are treated equally, the features learned by the networks will have large intra-class and small inter-class differences, which are harmful to DFER. To tackle this problem, we propose the global convolution-attention block (GCA) to rescale the channels of the feature maps. In addition, we introduce the intensity-aware loss (IAL) in the training process to help the network distinguish the samples with relatively low expression intensities. Experiments on two in-the-wild dynamic facial expression datasets (i.e., DFEW and FERV39k) indicate that our method outperforms the state-of-the-art DFER approaches. The source code will be available at https://github.com/muse1998/IAL-for-Facial-Expression-Recognition.
Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization

We propose and analyze several stochastic gradient algorithms for finding stationary points or local minimum in nonconvex, possibly with nonsmooth regularizer, finite-sum and online optimization problems. First, we propose a simple proximal stochastic gradient algorithm based on variance reduction called ProxSVRG+. We provide a clean and tight analysis of ProxSVRG+, which shows that it outperforms the deterministic proximal gradient descent (ProxGD) for a wide range of minibatch sizes, hence solves an open problem proposed in Reddi et al. (2016b). Also, ProxSVRG+ uses much less proximal oracle calls than ProxSVRG (Reddi et al., 2016b) and extends to the online setting by avoiding full gradient computations. Then, we further propose an optimal algorithm, called SSRGD, based on SARAH (Nguyen et al., 2017) and show that SSRGD further improves the gradient complexity of ProxSVRG+ and achieves the optimal upper bound, matching the known lower bound of (Fang et al., 2018; Li et al., 2021). Moreover, we show that both ProxSVRG+ and SSRGD enjoy automatic adaptation with local structure of the objective function such as the Polyak-\L{}ojasiewicz (PL) condition for nonconvex functions in the finite-sum case, i.e., we prove that both of them can automatically switch to faster global linear convergence without any restart performed in prior work ProxSVRG (Reddi et al., 2016b). Finally, we focus on the more challenging problem of finding an $(\epsilon, \delta)$-local minimum instead of just finding an $\epsilon$-approximate (first-order) stationary point (which may be some bad unstable saddle points). We show that SSRGD can find an $(\epsilon, \delta)$-local minimum by simply adding some random perturbations. Our algorithm is almost as simple as its counterpart for finding stationary points, and achieves similar optimal rates.
Analysis of the interaction between φ meson and nucleus

In this work, we systematically study the φ meson and nucleus interaction by analyzing and fitting the cross sections of γN→ φN (N represent the nucleus) reactions near the threshold. With the help of vector meson dominant model, the distribution of φ-N scattering length as a function of energy is presented, and the results show that there is a slight increase in scattering length with increasing energy. Based on this, the average scattering length of φ-proton is obtained as 0.10 ± 0.01 fm by combining experimental data and theoretical models. Moreover, the average scattering length of φ-deuteron interaction is derived to be 0.014 ± 0.002 fm for the first time. Further, the effect of the momentum transfer |tmin| on the φ-N scattering length at the threshold is discussed. The obtained results not only provide important theoretical information for a more comprehensive and accurate study of the φ-N scattering length, but also provide a basis for future experimental measurements of φ meson production.
A Lagrangian with $E_8\times E_8$ symmetry for the standard model and pre-gravitation I. -- The bosonic Lagrangian, and a theoretical derivation of the weak mixing angle

Building on earlier work, we propose an elementary Lagrangian for the uniﬁcation of the standard model with pre-gravitation, assumed to have an unbroken E 8 × E 8 symmetry. The Lagrangian is patterned after the kinetic energy of a free particle in Newtonian dynamics, generalising it to the matrix-valued Lagrangian dynamics of a 2-brane on a split bioctonionic space. Symmetry breaking gives rise to the standard model quarks and leptons of three generations along with the known gauge interactions, and a novel right-handed counterpart SU (3) grav × SU (2) R × U (1) g identiﬁed as pre-gravitation. The goal of the present series of papers is to investigate if this pre-quantum, pre-spacetime matrix-valued Lagrangian dynamics can lead to the emergence of quantum ﬁeld theory for the standard model, and classical general relativity, possibly with some additional corrections. In this paper we work out the bosonic part of the Lagrangian, and show how the anticipated 32 gauge bosons are recovered from the elementary Lagrangian. As an application, we show that the asymptotic low energy weak mixing angle θ W is given by a solution of the equation 1 = (1 / 2) (cid:112) cos( θ W / 2) + (cid:112) sin( θ W / 2), yielding sin 2 θ W ≈ 0 . 24996.
Intrahepatic Viral Kinetics during Direct-Acting Antivirals for Hepatitis C in HIV Co-infection:The ACTG A5335S Substudy.

Direct-acting antivirals (DAA) targeting hepatitis C virus (HCV) have revolutionized outcomes in HIV co-infection. We examined early events in liver and plasma through A5335S, a substudy of trial A5329 (paritaprevir/ritonavir, ombitasvir, dasabuvir, with ribavirin) that enrolled chronic genotype 1a HCV-infected persons co-infected with suppressed HIV: 5/6 treatment-naïve enrollees completed A5335S. Mean baseline plasma HCV RNA=6.7 log10 IU/mL and changed by -4.1 log10 IU/mL by Day 7. In liver, laser capture microdissection was used to quantify HCV. At liver biopsy 1, mean (95% CI) %HCV-infected cells=25.2% (7.4%, 42.9%), correlating with plasma HCV RNA (Spearman rank correlation r=0.9); biopsy 2 (Day 7 in 4/5 participants) mean (95% CI) %HCV-infected cells=1.0% (0.2%, 1.7%)(p<0.05 for change), and DAAs were detectable in liver. Plasma CXCL10 concentrations changed by mean=-160 pg/mL/day at 24 hours, but no further after Day 4. We conclude that HCV infection is rapidly cleared from liver with DAA leaving <2% HCV-infected hepatocytes at Day 7. We extrapolate that HCV eradication could occur in these participants by 63 days, although immune activation might persist. Single-cell longitudinal estimates of HCV clearance from liver have never been reported previously and could be applied to estimating the minimum treatment duration required for HCV infection.
Transcutaneous electrical stimulation in obstructive sleep apnoea: current developments and concepts of the TESLA-home programme

Purpose of review Obstructive sleep apnoea (OSA) is a highly prevalent condition affecting about 1 billion people worldwide. The first line therapy for most patients with OSA is continuous positive airway pressure (CPAP) therapy. However, there are significant limitations with long-term adherence to CPAP therapy, which may be as low as 30–60%. Recent finding Electrical stimulation of the hypoglossal nerve has been studied in recent years. It achieves upper airway patency by causing a contraction of the genioglossus muscle, the strongest dilator of the upper airway, and by maintaining its neuromuscular tone in the asleep patient with OSA. Electrical stimulation can be delivered invasively, hypoglossal nerve stimulation (HNS), and noninvasively, transcutaneous electrical stimulation in OSA (TESLA). However, randomised controlled trials, the STAR and the TESLA trial, have provided promising results on efficacy and safety of the methods. Summary Patient and public involvement underlines the interest in TESLA and HNS and highlights the need to provide non-CPAP therapeutic options to those who may find it difficult to cope with first line therapies. The relatively low costs and the favourable safety profile of the TESLA approach provide the chance to offer this treatment to patients with OSA following further development of the evidence.
A Qualitative Longitudinal Study of Workplace Issues, Authorities and Media, and Relationships Recounted by Oklahoma City Bombing Survivors After Nearly a Quarter Century

This study’s longitudinal findings suggest that after a terrorist incident, directly-exposed survivors remember their early postdisaster experience and continue to have psychosocial needs. Because their lives have been substantially altered by the bombing (with psychosocial sequelae not necessarily representing long-term psychopathology), disaster-related interventions continue to be warranted. Objective The aim of the study is to examine the long-term course of disaster-related experience among survivors of a terrorist bombing and the long-term recollection of initial workplace effects across nearly a quarter century. Methods From an initial randomly selected sample of highly trauma-exposed survivors of the 1995 Oklahoma City bombing, 103 participated in qualitative open-ended interviews about their bombing experience approximately 23 years after disaster. Results The survivors described their bombing experience clearly with extensive detail and expression of persistent strong emotion. Their discussions reflected findings from earlier assessments and also continued over the course of the next decades to complete their stories of the course of their occupational and interpersonal postdisaster journeys. Conclusions Long-term psychosocial ramifications in these survivors’ lives continue to warrant psychosocial interventions, such as occupational and interpersonal counseling.
Endospores associated with deep seabed geofluid features in the eastern Gulf of Mexico

Recent studies have reported up to 1.9 × 1029 bacterial endospores in the upper kilometre of deep subseafloor marine sediments, however, little is understood about their origin and dispersal. In cold ocean environments, the presence of thermospores (endospores produced by thermophilic bacteria) suggests that distribution is governed by passive migration from warm anoxic sources possibly facilitated by geofluid flow, such as advective hydrocarbon seepage sourced from petroleum deposits deeper in the subsurface. This study assesses this hypothesis by measuring endospore abundance and distribution across 60 sites in Eastern Gulf of Mexico (EGM) sediments using a combination of the endospore biomarker 2,6‐pyridine dicarboxylic acid or ‘dipicolinic acid’ (DPA), sequencing 16S rRNA genes of thermospores germinated in 50°C sediment incubations, petroleum geochemistry in the sediments and acoustic seabed data from sub‐bottom profiling. High endospore abundance is associated with geologically active conduit features (mud volcanoes, pockmarks, escarpments and fault systems), consistent with subsurface fluid flow dispersing endospores from deep warm sources up into the cold ocean. Thermospores identified at conduit sites were most closely related to bacteria associated with the deep biosphere habitats including hydrocarbon systems. The high endospore abundance at geological seep features demonstrated here suggests that recalcitrant endospores and their chemical components (such as DPA) can be used in concert with geochemical and geophysical analyses to locate discharging seafloor features. This multiproxy approach can be used to better understand patterns of advective fluid flow in regions with complex geology like the EGM basin.