Miracle-dz/UniFAR-FTUs huggingface dataset:
Pairing interacting protein sequences using masked language modeling

Predicting which proteins interact together from amino-acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments, such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called DiffPALM that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing. Significance statement Deep learning has brought major advances to the analysis of biological sequences. Self-supervised models, based on approaches from natural language processing and trained on large ensembles of protein sequences, efficiently learn statistical dependence in this data. This includes coevolution patterns between structurally or functionally coupled amino acids, which allows them to capture structural contacts. We propose a method to pair interacting protein sequences which leverages the power of a protein language model trained on multiple sequence alignments. Our method performs well for small datasets that are challenging for existing methods. It can improve structure prediction of protein complexes by supervised methods, which remains more challenging than that of single-chain proteins.
Improving the Estimation of Site-Specific Effects and their Distribution in Multisite Trials

In multisite trials, statistical goals often include obtaining individual site-specific treatment effects, determining their rankings, and examining their distribution across multiple sites. This paper explores two strategies for improving inferences related to site-specific effects: (a) semiparametric modeling of the prior distribution using Dirichlet process mixture (DPM) models to relax the normality assumption, and (b) using estimators other than the posterior mean, such as the constrained Bayes or triple-goal estimators, to summarize the posterior. We conduct a large-scale simulation study, calibrated to multisite trials common in education research. We then explore the conditions and degrees to which these strategies and their combinations succeed or falter in the limited data environments. We found that the average reliability of within-site effect estimates is crucial for determining effective estimation strategies. In settings with low-to-moderate data informativeness, flexible DPM models perform no better than the simple parametric Gaussian model coupled with a posterior summary method tailored to a specific inferential goal. DPM models outperform Gaussian models only in select high-information settings, indicating considerable sensitivity to the level of cross-site information available in the data. We discuss the implications of our findings for balancing trade-offs associated with shrinkage for the design and analysis of future multisite randomized experiments.
Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology

The rapid accessibility of portable and affordable retinal imaging devices has made early differential diagnosis easier. For example, color funduscopy imaging is readily available in remote villages, which can help to identify diseases like age-related macular degeneration (AMD), glaucoma, or pathological myopia (PM). On the other hand, astronauts at the International Space Station utilize this camera for identifying spaceflight-associated neuro-ocular syndrome (SANS). However, due to the unavailability of experts in these locations, the data has to be transferred to an urban healthcare facility (AMD and glaucoma) or a terrestrial station (e.g, SANS) for more precise disease identification. Moreover, due to low bandwidth limits, the imaging data has to be compressed for transfer between these two places. Different super-resolution algorithms have been proposed throughout the years to address this. Furthermore, with the advent of deep learning, the field has advanced so much that x2 and x4 compressed images can be decompressed to their original form without losing spatial information. In this paper, we introduce a novel model called Swin-FSR that utilizes Swin Transformer with spatial and depth-wise attention for fundus image super-resolution. Our architecture achieves Peak signal-to-noise-ratio (PSNR) of 47.89, 49.00 and 45.32 on three public datasets, namely iChallenge-AMD, iChallenge-PM, and G1020. Additionally, we tested the model's effectiveness on a privately held dataset for SANS provided by NASA and achieved comparable results against previous architectures.
Comment on `Painlev\'e Integrability and Multi-wave pattern for (2+1) dimensional Long wave-Short wave Resonance Interaction system'

In the paper by Sivatharani et al [1], the authors make a tall claim about the integrability of a 2 component (2+1) dimensional Long wave Short wave Resonance Interaction (2C(2+1)LSRI) equation with mixed sign which was already claimed to be non integrable and hence known not to satisfy Painlev\'e [2] property which the authors show to pass Painlev\'e test. We have categorically shown how the system does not pass Painlev\'e test and hence non-integrable reinforcing the claim made by Maruno et al [2]. The authors claim to derive the solutions of 2C(2+1)LSRI equation which ironically do not satisfy the equation. To top it all, the authors claim to have generated lumps and dromions which again defy their very own definition.
Constraints on holographic QCD phase transitions from PTA observations

The underlying physics of QCD phase transition in the early Universe remains largely unknown due to its strong-coupling nature during the quark-gluon plasma/hadron gas transition, yet a holographic model has been proposed to quantitatively fit the lattice QCD data while with its duration of the first-order phase transition (FoPT) left undetermined. At specific baryon chemical potential, the first-order QCD phase transition agrees with the observational constraint of baryon asymmetry. It therefore provides a scenario for phase transition gravitational waves (GWs) within the Standard Model of particle physics. If these background GWs could contribute dominantly to the recently claimed common-spectrum red noise from pulsar timing array (PTA) observations, the duration of this FoPT can be well constrained but disfavored by the constraints from curvature perturbations. However, the associated primordial black holes are still allowed by current observations. Therefore, either the QCD phase transition is not described by our holographic model or the other GW sources must be presented to dominate over the GWs from this FoPT.
Contrastive Bi-Projector for Unsupervised Domain Adaption

This paper proposes a novel unsupervised domain adaption (UDA) method based on contrastive bi-projector (CBP), which can improve the existing UDA methods. It is called CBPUDA here, which effectively promotes the feature extractors (FEs) to reduce the generation of ambiguous features for classification and domain adaption. The CBP differs from traditional bi-classifier-based methods at that these two classifiers are replaced with two projectors of performing a mapping from the input feature to two distinct features. These two projectors and the FEs in the CBPUDA can be trained adversarially to obtain more refined decision boundaries so that it can possess powerful classification performance. Two properties of the proposed loss function are analyzed here. The first property is to derive an upper bound of joint prediction entropy, which is used to form the proposed loss function, contrastive discrepancy (CD) loss. The CD loss takes the advantages of the contrastive learning and the bi-classifier. The second property is to analyze the gradient of the CD loss and then overcome the drawback of the CD loss. The result of the second property is utilized in the development of the gradient scaling (GS) scheme in this paper. The GS scheme can be exploited to tackle the unstable problem of the CD loss because training the CBPUDA requires using contrastive learning and adversarial learning at the same time. Therefore, using the CD loss with the GS scheme overcomes the problem mentioned above to make features more compact for intra-class and distinguishable for inter-class. Experimental results express that the CBPUDA is superior to conventional UDA methods under consideration in this paper for UDA and fine-grained UDA tasks.
AudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codes

We propose a method named AudioFormer,which learns audio feature representations through the acquisition of discrete acoustic codes and subsequently fine-tunes them for audio classification tasks. Initially,we introduce a novel perspective by considering the audio classification task as a form of natural language understanding (NLU). Leveraging an existing neural audio codec model,we generate discrete acoustic codes and utilize them to train a masked language model (MLM),thereby obtaining audio feature representations. Furthermore,we pioneer the integration of a Multi-Positive sample Contrastive (MPC) learning approach. This method enables the learning of joint representations among multiple discrete acoustic codes within the same audio input. In our experiments,we treat discrete acoustic codes as textual data and train a masked language model using a cloze-like methodology,ultimately deriving high-quality audio representations. Notably,the MPC learning technique effectively captures collaborative representations among distinct positive samples. Our research outcomes demonstrate that AudioFormer attains significantly improved performance compared to prevailing monomodal audio classification models across multiple datasets,and even outperforms audio-visual multimodal classification models on select datasets. Specifically,our approach achieves remarkable results on datasets including AudioSet (2M,20K),and FSD50K,with performance scores of 53.9,45.1,and 65.6,respectively. We have openly shared both the code and models: https://github.com/LZH-0225/AudioFormer.git.
An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM

Natural Language Processing (NLP) has emerged as a crucial technology for understanding and generating human language, playing an essential role in tasks such as machine translation, sentiment analysis, and more pertinently, question classification. As a subfield within NLP, question classification focuses on determining the type of information being sought, a fundamental step for downstream applications like question answering systems. This study presents an innovative ensemble approach for question classification, combining the strengths of Electra, GloVe, and LSTM models. Rigorously tested on the well-regarded TREC dataset, the model demonstrates how the integration of these disparate technologies can lead to superior results. Electra brings in its transformer-based capabilities for complex language understanding, GloVe offers global vector representations for capturing word-level semantics, and LSTM contributes its sequence learning abilities to model long-term dependencies. By fusing these elements strategically, our ensemble model delivers a robust and efficient solution for the complex task of question classification. Through rigorous comparisons with well-known models like BERT, RoBERTa, and DistilBERT, the ensemble approach verifies its effectiveness by attaining an 80% accuracy score on the test dataset.
The well-posedness of three-dimensional Navier-Stokes and magnetohydrodynamic equations with partial fractional dissipation

It is well-known that if one replaces standard velocity and magnetic dissipation by $(-\Delta)^\alpha u$ and $(-\Delta)^\beta b$ respectively, the magnetohydrodynamic equations are well-posed for $\alpha\ge\frac{5}{4}$ and $\alpha + \beta \ge \frac{5}{2}$. This paper considers the 3D Navier-Stokes and magnetohydrodynamic equations with partial fractional hyper-dissipation. It is proved that when each component of the velocity and magnetic field lacks dissipation along some direction, the existence and conditional uniqueness of the solution still hold. This paper extends the previous results in (Yang, Jiu and Wu J. Differential Equations 266(1): 630-652, 2019) to a more general case.
Effect Of Inversion Winds On Topographic Detail And Mass Balance On Inland Ice Sheets

Steady-state gravity flow of air (inversion wind) on sloping snow-covered ice sheets is analyzed for sensitivity to local topography. Topographic features of the order of a few kilometres or less in length are too small to affect the direction and speed of this air flow. Air flow on a longer scale should however conform cosely to topography. Surface roughness on ice sheets is consistent with these results. Features of length shorter than a few kilometers (drifts and sastrugi) are transient, but longer features (surface undulations) remain essentially unaltered for many years. On the longer scale, inversion wind speed and therefore the amount of drifting and blowing snow should vary with the surface slope even where slope changes by as little as 1/10%. Observed variations in surface mass balance (aceumulated snow) in upper Marie Byrd Land, Antarctica, support this hypothesis. Snow drift and inversion winds thus constitute a feed-back mechanism on the form of ice sheets and some of the topographic detail, formerly attributed to ice-flow character alone, may be in large part due to this mechanism.
Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research

Visualization for machine learning (VIS4ML) research aims to help experts apply their prior knowledge to develop, understand, and improve the performance of machine learning models. In conceiving VIS4ML systems, researchers characterize the nature of human knowledge to support human-in-the-loop tasks, design interactive visualizations to make ML components interpretable and elicit knowledge, and evaluate the effectiveness of human-model interchange. We survey recent VIS4ML papers to assess the generalizability of research contributions and claims in enabling human-in-the-loop ML. Our results show potential gaps between the current scope of VIS4ML research and aspirations for its use in practice. We find that while papers motivate that VIS4ML systems are applicable beyond the specific conditions studied, conclusions are often overfitted to non-representative scenarios, are based on interactions with a small set of ML experts and well-understood datasets, fail to acknowledge crucial dependencies, and hinge on decisions that lack justification. We discuss approaches to close the gap between aspirations and research claims and suggest documentation practices to report generality constraints that better acknowledge the exploratory nature of VIS4ML research.
Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges

The past decade has witnessed the rapid development and adoption of ML&DL methodologies in agricultural systems, showcased by great successes in agricultural applications. However, these conventional ML/DL models have certain limitations: they heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, large pre-trained models, also known as FMs, have demonstrated remarkable successes in language, vision, and decision-making tasks across various domains. These models are trained on a large amount of data from multiple domains and modalities. Once trained, they can accomplish versatile tasks with just minor fine-tuning and minimal task-specific labeled data. Despite their proven effectiveness and huge potential, there has been little exploration of applying FMs to agriculture AI. Thus, this study aims to explore the potential of FMs in the field of smart agriculture. In particular, conceptual tools and technical background are presented to help the understanding of the problem space and uncover new research directions. To this end, recent FMs in the general CS domain are reviewed, and the models are categorized into four categories: language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs. Then, the steps of developing agriculture FMs (AFMs) are outlined and potential applications in smart agriculture are discussed. Moreover, challenges and risks associated with developing AFMs are discussed, including model training, validation, and deployment. In summary, the advancement of AI in agriculture is explored by introducing AFMs as a promising paradigm that can significantly mitigate the reliance on extensive labeled datasets and enhance the efficiency, effectiveness, and generalization of agricultural AI systems.
A Unifying Generator Loss Function for Generative Adversarial Networks

A unifying α-parametrized generator loss function is introduced for a dual-objective generative adversarial network (GAN) that uses a canonical (or classical) discriminator loss function such as the one in the original GAN (VanillaGAN) system. The generator loss function is based on a symmetric class probability estimation type function, Lα, and the resulting GAN system is termed Lα-GAN. Under an optimal discriminator, it is shown that the generator’s optimization problem consists of minimizing a Jensen-fα-divergence, a natural generalization of the Jensen-Shannon divergence, where fα is a convex function expressed in terms of the loss function Lα. It is also demonstrated that this Lα-GAN problem recovers as special cases a number of GAN problems in the literature, including VanillaGAN, least squares GAN (LSGAN), least kth-order GAN (LkGAN), and the recently introduced (αD,αG)-GAN with αD=1. Finally, experimental results are provided for three datasets—MNIST, CIFAR-10, and Stacked MNIST—to illustrate the performance of various examples of the Lα-GAN system.
On the cohomology of GL_2 and SL_2 over imaginary quadratic fields

We report on computations of the cohomology of GL_2(O_D) and SL_2(O_D), where D<0 is a fundamental discriminant. These computations go well beyond earlier results of Vogtmann and Scheutzow. We use the technique of homology of Voronoi complexes, and our computations recover the integral cohomology away from the primes 2, 3. We observed exponential growth in the torsion subgroup of H^2 as $D$ increases, and compared our data to bounds of Rohlfs.
Precipitation nowcasting with generative diffusion models

In recent years traditional numerical methods for accurate weather prediction have been increasingly challenged by deep learning methods. Numerous historical datasets used for short and medium-range weather forecasts are typically organized into a regular spatial grid structure. This arrangement closely resembles images: each weather variable can be visualized as a map or, when considering the temporal axis, as a video. Several classes of generative models, comprising Generative Adversarial Networks, Variational Autoencoders, or the recent Denoising Diffusion Models have largely proved their applicability to the next-frame prediction problem, and is thus natural to test their performance on the weather prediction benchmarks. Diffusion models are particularly appealing in this context, due to the intrinsically probabilistic nature of weather forecasting: what we are really interested to model is the probability distribution of weather indicators, whose expected value is the most likely prediction. In our study, we focus on a specific subset of the ERA-5 dataset, which includes hourly data pertaining to Central Europe from the years 2016 to 2021. Within this context, we examine the efficacy of diffusion models in handling the task of precipitation nowcasting. Our work is conducted in comparison to the performance of well-established U-Net models, as documented in the existing literature. Our proposed approach of Generative Ensemble Diffusion (GED) utilizes a diffusion model to generate a set of possible weather scenarios which are then amalgamated into a probable prediction via the use of a post-processing network. This approach, in comparison to recent deep learning models, substantially outperformed them in terms of overall performance.
Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks

Predicting pedestrians' trajectories is a crucial capability for autonomous vehicles' safe navigation, especially in spaces shared with pedestrians. Pedestrian motion in shared spaces is influenced by both the presence of vehicles and other pedestrians. Therefore, effectively modelling both pedestrian-pedestrian and pedestrian-vehicle interactions can increase the accuracy of the pedestrian trajectory prediction models. Despite the huge literature on ways to encode the effect of interacting agents on a pedestrian's predicted trajectory using deep-learning models, limited effort has been put into the effective selection of interacting agents. In the majority of cases, the interaction features used are mainly based on relative distances while paying less attention to the effect of the velocity and approaching direction in the interaction formulation. In this paper, we propose a heuristic-based process of selecting the interacting agents based on collision risk calculation. Focusing on interactions of potentially colliding agents with a target pedestrian, we propose the use of time-to-collision and the approach direction angle of two agents for encoding the interaction effect. This is done by introducing a novel polar collision grid map. Our results have shown predicted trajectories closer to the ground truth compared to existing methods (used as a baseline) on the HBS dataset.
CausalLM is not optimal for in-context learning

Recent empirical evidence indicates that transformer based in-context learning performs better when using a prefix language model (prefixLM), in which in-context samples can all attend to each other, compared to causal language models (causalLM), which use auto-regressive attention that prohibits in-context samples to attend to future samples. While this result is intuitive, it is not understood from a theoretical perspective. In this paper we take a theoretical approach and analyze the convergence behavior of prefixLM and causalLM under a certain parameter construction. Our analysis shows that both LM types converge to their stationary points at a linear rate, but that while prefixLM converges to the optimal solution of linear regression, causalLM convergence dynamics follows that of an online gradient descent algorithm, which is not guaranteed to be optimal even as the number of samples grows infinitely. We supplement our theoretical claims with empirical experiments over synthetic and real tasks and using various types of transformers. Our experiments verify that causalLM consistently underperforms prefixLM in all settings.