Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the UV and IR of a type-II holographic superconductor using a dyonic black hole

In this study, we investigate a type-II holographic superconductor with a perturbative scalar field over a (3 + 1)-dimensional electric and magnetically charged planar AdS black hole. After consistently decoupling the scalar field sector from the complete Einstein-Maxwell-Scalar system, we delve into the thermodynamical properties of the background relevant for the dual description of the Ginzburg-Landau density of superconducting states. The adoption of a London gauge allowed us to consider the magnetic field as a uniform external field over which the holographic superconductor is subject. This consideration enables a consistent description of the appearance of Abrikosov vortex lattices typical in type-II superconductors. Thus, by matching near horizon and boundary expansions of the scalar field, we obtained an expression for the upper critical magnetic field as a function of temperature in both, the canonical and grand canonical ensemble. These novel results confirm that our perturbative scalar field model consistently reproduces the well-known temperature behavior of the upper critical magnetic field according to the Ginzburg-Landau theory and other Abelian-Higgs holographic developments for type-II superconductors. In addition, a new analysis of the scalar field equation in terms of a Schr\"odinger potential led us to observe the existence of potential wells distributed along the holographic coordinate. We interpret these regions with a local minimum as those in which bound states can exist, dual to the Cooper pairs density. These results provide evidence for the existence of an IR order parameter near the extremality. In view of this, we performed a closer inspection of the IR effective scalar equation in which the geometry adopts a Schwarzschild AdS$_{2}\times \mathbb{R}^{2}$ structure.
SGD with Clipping is Secretly Estimating the Median Gradient

There are several applications of stochastic optimization where one can benefit from a robust estimate of the gradient. For example, domains such as distributed learning with corrupted nodes, the presence of large outliers in the training data, learning under privacy constraints, or even heavy-tailed noise due to the dynamics of the algorithm itself. Here we study SGD with robust gradient estimators based on estimating the median. We first consider computing the median gradient across samples, and show that the resulting method can converge even under heavy-tailed, state-dependent noise. We then derive iterative methods based on the stochastic proximal point method for computing the geometric median and generalizations thereof. Finally we propose an algorithm estimating the median gradient across iterations, and find that several well known methods - in particular different forms of clipping - are particular cases of this framework.
How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts

The remarkable advancements in Multimodal Large Language Models (MLLMs) have not rendered them immune to challenges, particularly in the context of handling deceptive information in prompts, thus producing hallucinated responses under such conditions. To quantitatively assess this vulnerability, we present MAD-Bench, a carefully curated benchmark that contains 850 test samples divided into 6 categories, such as non-existent objects, count of objects, spatial relationship, and visual confusion. We provide a comprehensive analysis of popular MLLMs, ranging from GPT-4V, Gemini-Pro, to open-sourced models, such as LLaVA-1.5 and CogVLM. Empirically, we observe significant performance gaps between GPT-4V and other models; and previous robust instruction-tuned models, such as LRV-Instruction and LLaVA-RLHF, are not effective on this new benchmark. While GPT-4V achieves 75.02% accuracy on MAD-Bench, the accuracy of any other model in our experiments ranges from 5% to 35%. We further propose a remedy that adds an additional paragraph to the deceptive prompts to encourage models to think twice before answering the question. Surprisingly, this simple method can even double the accuracy; however, the absolute numbers are still too low to be satisfactory. We hope MAD-Bench can serve as a valuable benchmark to stimulate further research to enhance models' resilience against deceptive prompts.
Evaluation of toxicity from leachate lagoons sediments, using the toxicity leaching procedure - TCLP and acute toxicity tests

Los rellenos sanitarios son la alternativa mas empleada para disposicion final de residuos solidos municipales, sin embargo, generan problemas de contaminacion por la generacion de lixiviados, sedimentos y emision de gases toxicos. Los sedimentos surgen de la precipitacion de los materiales organicos e inorganicos de los lixiviados depositados en lagunas de almacenamiento que pueden presentar caracteristicas toxicas que afectan el ambiente. En este trabajo se evaluo la potencial toxicidad del material sedimentado de lagunas de lixiviados de un relleno sanitario municipal, utilizando el procedimiento de lixiviacion para la caracteristica de toxicidad - TCLP y ensayos de toxicidad aguda con Daphnia pulex y Poecilia reticulata. Los resultados mostraron que los sedimentos no excedieron los niveles establecidos por la EPA para TCLP, ni fueron toxicos para los organismos empleados, lo que sugiere su potencial aprovechamiento agricola en suelos acidos, dado que presentan un pH alcalino y alto contenido de materia organica y nutrientes.
OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification

There has been an increasing interest in large speech models that can perform multiple speech processing tasks in a single model. Such models usually adopt the encoder-decoder or decoder-only architecture due to their popularity and good performance in many domains. However, autoregressive models can be slower during inference compared to non-autoregressive models and also have potential risks of hallucination. Though prior studies observed promising results of non-autoregressive models for certain tasks at small scales, it remains unclear if they can be scaled to speech-to-text generation in diverse languages and tasks. Inspired by the Open Whisper-style Speech Model (OWSM) project, we propose OWSM-CTC, a novel encoder-only speech foundation model based on Connectionist Temporal Classification (CTC). It is trained on 180k hours of public audio data for multilingual automatic speech recognition (ASR), speech translation (ST), and language identification (LID). Compared to encoder-decoder OWSM, our OWSM-CTC achieves competitive results on ASR and up to 25% relative improvement on ST, while it is more robust and 3 to 4 times faster for inference. OWSM-CTC also improves the long-form ASR result with 20x speed-up. We will publicly release our codebase, pre-trained model, and training logs to promote open science in speech foundation models.
Testing Calibration in Subquadratic Time

In the recent literature on machine learning and decision making, calibration has emerged as a desirable and widely-studied statistical property of the outputs of binary prediction models. However, the algorithmic aspects of measuring model calibration have remained relatively less well-explored. Motivated by [BGHN23], which proposed a rigorous framework for measuring distances to calibration, we initiate the algorithmic study of calibration through the lens of property testing. We define the problem of calibration testing from samples where given $n$ draws from a distribution $\mathcal{D}$ on (predictions, binary outcomes), our goal is to distinguish between the case where $\mathcal{D}$ is perfectly calibrated, and the case where $\mathcal{D}$ is $\varepsilon$-far from calibration. We design an algorithm based on approximate linear programming, which solves calibration testing information-theoretically optimally (up to constant factors) in time $O(n^{1.5} \log(n))$. This improves upon state-of-the-art black-box linear program solvers requiring $\Omega(n^\omega)$ time, where $\omega>2$ is the exponent of matrix multiplication. We also develop algorithms for tolerant variants of our testing problem, and give sample complexity lower bounds for alternative calibration distances to the one considered in this work. Finally, we present preliminary experiments showing that the testing problem we define faithfully captures standard notions of calibration, and that our algorithms scale to accommodate moderate sample sizes.
A New Species Of The Miniaturized Frog Genus Paedophryne (Anura: Microhylidae) From Papua New Guinea

(1) ABSTRACT – I describe a new species of Paedophryne from low-elevation foothill rainforest on Normanby Island off southeastern Papua New Guinea. This new species is the largest member of the genus so far discovered, with body length of the sole adult male 11.1 mm, and with adult females ranging from 10.6–11.6 mm. As well, it differs from other species in the genus in having smooth dorsal skin, a disc on the second toe, relatively larger first digits, and in a number of body proportions and details of color pattern. As for other species in the genus, it is an inhabitant of rainforest leaf litter restricted to a portion of the East Papuan Composite Terrane, an agglomeration of terranes that were assembled offshore of New Guinea and sutured to it during the early Miocene.
Improving Deep Generative Models on Many-To-One Image-to-Image Translation

Deep generative models have been applied to multiple applications in image-to-image translation. Generative Adversarial Networks and Diffusion Models have presented impressive results, setting new state-of-the-art results on these tasks. Most methods have symmetric setups across the different domains in a dataset. These methods assume that all domains have either multiple modalities or only one modality. However, there are many datasets that have a many-to-one relationship between two domains. In this work, we first introduce a Colorized MNIST dataset and a Color-Recall score that can provide a simple benchmark for evaluating models on many-to-one translation. We then introduce a new asymmetric framework to improve existing deep generative models on many-to-one image-to-image translation. We apply this framework to StarGAN V2 and show that in both unsupervised and semi-supervised settings, the performance of this new model improves on many-to-one image-to-image translation.
Simpson's Paradox and the Accuracy-Fluency Tradeoff in Translation

A good translation should be faithful to the source and should respect the norms of the target language. We address a theoretical puzzle about the relationship between these objectives. On one hand, intuition and some prior work suggest that accuracy and fluency should trade off against each other, and that capturing every detail of the source can only be achieved at the cost of fluency. On the other hand, quality assessment researchers often suggest that accuracy and fluency are highly correlated and difficult for human raters to distinguish (Callison-Burch et al. 2007). We show that the tension between these views is an instance of Simpson's paradox, and that accuracy and fluency are positively correlated at the level of the corpus but trade off at the level of individual source segments. We further suggest that the relationship between accuracy and fluency is best evaluated at the segment (or sentence) level, and that the trade off between these dimensions has implications both for assessing translation quality and developing improved MT systems.
Tree-Planted Transformers: Large Language Models with Implicit Syntactic Supervision

Large Language Models (LLMs) have achieved remarkable success thanks to scalability on large text corpora, but have some drawback in training efficiency. In contrast, Syntactic Language Models (SLMs) can be trained efficiently to reach relatively high performance thanks to syntactic supervision, but have trouble with scalability. Thus, given these complementary advantages of LLMs and SLMs, it is necessary to develop an architecture that integrates the scalability of LLMs with the training efficiency of SLMs, namely Syntactic Large Language Models (SLLM). In this paper, we propose a novel method dubbed tree-planting: implicitly"plant"trees into attention weights of Transformer LMs to reflect syntactic structures of natural language. Specifically, Transformer LMs trained with tree-planting will be called Tree-Planted Transformers (TPT), which learn syntax on small treebanks via tree-planting and then scale on large text corpora via continual learning with syntactic scaffolding. Targeted syntactic evaluations on the SyntaxGym benchmark demonstrated that TPTs, despite the lack of explicit syntactic supervision, significantly outperformed various SLMs with explicit syntactic supervision that generate hundreds of syntactic structures in parallel, suggesting that tree-planting and TPTs are the promising foundation for SLLMs.
An investigation of flow rate motivated mobilization of entrapped organic liquids in two-fluid phase porous medium systems

Groundwater is one of the nation’s most important natural resources, composing a large portion of the total freshwater on earth. Therefore, groundwater contamination is a rapidly growing concern among groundwater hydrologists and the general public alike. Organic contaminants, often present as non-aqueous phase liquid (NAPL) in the groundwater, are major groundwater contaminants of concern. The purpose of this work was to investigate the mobilization of entrapped disconnected NAPL in porous medium systems. Tetrachloroethylene (PCE) was chosen as a model NAPL compound. Fluid properties of PCE were analyzed and column experiments as well as lattice Boltzmann simulations were performed to determine the relationships between the residual NAPL saturation, the fluid properties, and the viscous forces. Furthermore, trapping number (NT) was tested as a criterion to predict and validate residual NAPL mobilization in porous media. Residual saturation was found to decrease with increasing viscous force and NT. The experimental data and simulation results were compared and showed good agreement.
Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

Self-supervised learning (SSL) has emerged as a powerful method for extracting meaningful representations from vast, unlabeled datasets, already transforming computer vision and natural language processing. Similarly, in single-cell genomics (SCG), representation learning is well-recognized for offering insights into complex biological data, even more so by the advent of early foundation model approaches. However, despite these advancements, identifying scenarios in SCG where SSL outperforms traditional supervised or unsupervised learning methods remains a nuanced challenge. Furthermore, selecting the most effective pretext tasks within the SSL framework for SCG is a critical yet unresolved question. Here, we address this gap by adapting and benchmarking SSL techniques in SCG, including masked autoencoders with multiple masking strategies and contrastive learning approaches. Trained on over 20 million cells, this study rigorously examines multiple downstream tasks, including cell type prediction, gene expression reconstruction, cross-modality prediction, and data integration. Our empirical analyses underscore the nuanced role of SSL, namely in transfer learning scenarios leveraging auxiliary data or analyzing novel datasets. Masked autoencoders excel over contrastive methods in SCG, diverging from computer vision trends. Moreover, our findings reveal notable capabilities of SSL in zero-shot cell type prediction and offer insights into its potential benefits in cross-modality prediction and data integration. In summary, we study the application of SSL in SCG, minimizing model bias through simple, fully connected networks, and benchmark SSL’s utility across key representation learning scenarios.
Discovering and exploring the hidden diversity of human gut viruses using highly enriched virome samples

Viruses are an abundant and crucial component of the human microbiome, but accurately discovering them via metagenomics is still challenging. Currently, the available viral reference genomes poorly represent the diversity in microbiome samples, and expanding such a set of viral references is difficult. As a result, many viruses are still undetectable through metagenomics even when considering the power of de novo metagenomic assembly and binning, as viruses lack universal markers. Here, we describe a novel approach to catalog new viral members of the human gut microbiome and show how the resulting resource improves metagenomic analyses. We retrieved >3,000 viral-like particles (VLP) enriched metagenomic samples (viromes), evaluated the efficiency of the enrichment in each sample to leverage the viromes of highest purity, and applied multiple analysis steps involving assembly and comparison with hundreds of thousands of metagenome-assembled genomes to discover new viral genomes. We reported over 162,000 viral sequences passing quality control from thousands of gut metagenomes and viromes. The great majority of the retrieved viral sequences (∼94.4%) were of unknown origin, most had a CRISPR spacer matching host bacteria, and four of them could be detected in >50% of a set of 18,756 gut metagenomes we surveyed. We included the obtained collection of sequences in a new MetaPhlAn 4.1 release, which can quantify reads within a metagenome matching the known and newly uncovered viral diversity. Additionally, we released the viral database for further virome and metagenomic studies of the human microbiome.
Large-scale visualisation of α-synuclein oligomers in Parkinson’s disease brain tissue

Parkinson’s disease (PD) is a common neurodegenerative condition characterised by the presence in the brain of large intraneuronal aggregates, known as Lewy bodies and Lewy neurites, containing fibrillar α-synuclein. According to the amyloid hypothesis, these large end-stage species form from smaller soluble protein assemblies, often termed oligomers, which are proposed as early drivers of pathogenesis. To date, however, this hypothesis has remained controversial, at least in part because it has not been possible to directly visualise oligomeric aggregates in human brain tissue. Therefore, their presence, abundance and distributions have remained elusive. Here, we present ASA-PD (Advanced Sensing of Aggregates - Parkinson’s Disease), an imaging method to generate large-scale α-synuclein oligomer maps in post-mortem human brain tissue. We combined autofluorescence suppression with single-molecule fluorescence methods, which together, enable the detection of nanoscale α-synuclein aggregates. To demonstrate the utility of this platform, we captured ∼1.2 million oligomers from the anterior cingulate cortex in human post-mortem brain samples from PD and healthy control patients. Our data revealed a specific subpopulation of nanoscale oligomers that represent an early hallmark of the proteinopathy that underlies PD. We anticipate that quantitative information about oligomer distributions provided by ASA-PD will enable mechanistic studies to reveal the pathological processes caused by α-synuclein aggregation.
CYP1A1/1A2 enzymes mediate glucose homeostasis and insulin secretion in mice in a sex-specific manner

Aims/hypothesis The aryl hydrocarbon receptor (AhR) pathway is involved in cellular responses to a broad range of external stressors, making it an excellent candidate for understanding the interaction between environmental factors and type 2 diabetes risk. Studies suggest deleting or downregulating AhR protects against metabolic dysfunction in high-fat diet (HFD) fed mice; however, the contribution of downstream AhR targets in driving this phenotype remains unexamined. Cytochrome P450 1A1 and 1A2 (CYP1A1/1A2) are canonical AhR targets that encode xenobiotic metabolism enzymes. Interestingly, we have demonstrated that HFD feeding increases Cyp1a1 expression in mouse islets, which suggests CYP1A enzymes are involved in the response to metabolic stress. Since CYP1A1/1A2 activity can produce reactive oxygen intermediates, we hypothesized that chronic activation of these enzymes in tissues critical for regulating glucose homeostasis (e.g., liver, adipose, islets) will contribute to metabolic dysfunction following HFD feeding. Methods At 29 to 31 weeks of age, male and female global Cyp1a1/1a2 knockout (CypKO) and wildtype littermate control (CypWT) mice were fed either a 45% HFD or standard rodent chow for 14 weeks. Metabolic assessments were conducted throughout the study. Results CypKO females were partially protected from HFD-induced glucose intolerance compared to CypWT females, but both genotypes exhibited similar levels of insulin resistance. CypKO females also had lower plasma insulin levels in vivo and suppressed insulin secretion in isolated islets ex vivo compared to CypWT females. Gene expression patterns in female islets were generally similar across genotype and diet groups. In contrast, CypWT males became hyperinsulinemic and insulin resistant within 2 weeks of HFD feeding, while CypKO males maintained normal plasma insulin levels and insulin sensitivity. HFD feeding upregulated Cyp1a1 in CypWT male islets and this was accompanied by elevation of other islet stress genes. Interestingly, HFD feeding did not induce these stress gene responses in CypKO male islets, suggesting the islet stress response is mediated by activation of CYP1A1. We expected the global deletion of Cyp1a1/1a2 to have pronounced effects in the liver, but surprisingly, changes in liver pathology were predominantly driven by diet and not genotype in both sexes. Similarly, overall adiposity and adipose tissue inflammation were not affected by genotype. Conclusions Our study highlights a novel role of islet Cyp1a1/1a2 in shaping the systemic metabolic response to HFD feeding. Our data suggest that CYP1A1/1A2 enzymes are involved in glucose homeostasis, insulin secretion, and the islet stress response. Importantly, the effects of Cyp1a1/1a2 deletion are sex-dependent.
High-resolution single-particle imaging at 100-200 keV with the Gatan Alpine direct electron detector

Developments in direct electron detector technology have played a pivotal role in enabling high-resolution structural studies by cryo-EM at 200 and 300 keV. Yet, theory and recent experiments indicate advantages to imaging at 100 keV, energies for which the current detectors have not been optimized. In this study, we evaluated the Gatan Alpine detector, designed for operation at 100 and 200 keV. Compared to the Gatan K3, Alpine demonstrated a significant DQE improvement at these voltages, specifically a ~4-fold improvement at Nyquist at 100 keV. In single-particle cryo-EM experiments, Alpine datasets yielded better than 2 Å resolution reconstructions of apoferritin at 120 and 200 keV on a ThermoFisher Scientific (TFS) Glacios microscope. We also achieved a ~3.2 Å resolution reconstruction for a 115 kDa asymmetric protein complex, proving its effectiveness with complex biological samples. In-depth analysis revealed that Alpine reconstructions are comparable to K3 reconstructions at 200 keV, and remarkably, reconstruction from Alpine at 120 keV on a TFS Glacios surpassed all but the 300 keV data from a TFS Titan Krios with GIF/K3. Additionally, we show Alpine’s capability for high-resolution data acquisition and screening on lower-end systems by obtaining ~3 Å resolution reconstructions of apoferritin and aldolase at 100 keV and detailed 2D averages of a 55 kDa sample using a side-entry cryo holder. Overall, we show that Gatan Alpine performs well with the standard 200 keV imaging systems and may potentially capture the benefits of lower accelerating voltages, possibly bringing smaller sized particles within the scope of cryo-EM.