Miracle-dz/UniFAR-FTUs huggingface dataset:
Nucleocytoplasmic transport at the crossroads of proteostasis, neurodegeneration and neuroprotection

Nucleocytoplasmic transport comprises the multistep assembly, transport, and disassembly of protein and RNA cargoes entering and exiting nuclear pores. Accruing evidence supports that impairments to nucleocytoplasmic transport are a hallmark of neurodegenerative diseases. These impairments cause dysregulations in nucleocytoplasmic partitioning and proteostasis of nuclear transport receptors and client substrates that promote intracellular deposits – another hallmark of neurodegeneration. Disturbances in liquid–liquid phase separation (LLPS) between dense and dilute phases of biomolecules implicated in nucleocytoplasmic transport promote micrometer‐scale coacervates, leading to proteinaceous aggregates. This Review provides historical and emerging principles of LLPS at the interface of nucleocytoplasmic transport, proteostasis, aging and noxious insults, whose dysregulations promote intracellular aggregates. E3 SUMO‐protein ligase Ranbp2 constitutes the cytoplasmic filaments of nuclear pores, where it acts as a molecular hub for rate‐limiting steps of nucleocytoplasmic transport. A vignette is provided on the roles of Ranbp2 in nucleocytoplasmic transport and at the intersection of proteostasis in the survival of photoreceptor and motor neurons under homeostatic and pathophysiological environments. Current unmet clinical needs are highlighted, including therapeutics aiming to manipulate aggregation‐dissolution models of purported neurotoxicity in neurodegeneration.
GhXB38D represses cotton fibre elongation through ubiquitination of ethylene biosynthesis enzymes GhACS4 and GhACO1

Summary Ethylene plays an essential role in the development of cotton fibres. Ethylene biosynthesis in plants is elaborately regulated by the activities of key enzymes, 1‐aminocyclopropane‐1‐carboxylate oxidase (ACO) and 1‐aminocyclopropane‐1‐carboxylate synthase (ACS); however, the potential mechanism of post‐translational modification of ACO and ACS to control ethylene synthesis in cotton fibres remains unclear. Here, we identify an E3 ubiquitin ligase, GhXB38D, that regulates ethylene biosynthesis during fibre elongation in cotton. GhXB38D gene is highly expressed in cotton fibres during the rapid elongation stage. Suppressing GhXB38D expression in cotton significantly enhanced fibre elongation and length, accompanied by the up‐regulation of genes associated with ethylene signalling and fibre elongation. We demonstrated that GhXB38D interacts with the ethylene biosynthesis enzymes GhACS4 and GhACO1 in elongating fibres and specifically mediates their ubiquitination and degradation. The inhibition of GhXB38D gene expression increased the stability of GhACS4 and GhACO1 proteins in cotton fibres and ovules, resulting in an elevated concentration of ethylene. Our findings highlight the role of GhXB38D as a regulator of ethylene synthesis by ubiquitinating ACS4 and ACO1 proteins and modulating their stability. GhXB38D acts as a negative regulator of fibre elongation and serves as a potential target for enhancing cotton fibre yield and quality through gene editing strategy.
Beyond trial‐and‐error: Individualizing therapeutic transcranial neuromodulation for chronic pain

Repetitive transcranial magnetic stimulation (rTMS) applied to the motor cortex provides supplementary relief for some individuals with chronic pain who are refractory to pharmacological treatment. As rTMS slowly enters treatment guidelines for pain relief, its starts to be confronted with challenges long known to pharmacological approaches: efficacy at the group‐level does not grant pain relief for a particular patient. In this review, we present and discuss a series of ongoing attempts to overcome this therapeutic challenge in a personalized medicine framework.
Causal SAR ATR with Limited Data via Dual Invariance

Synthetic aperture radar automatic target recognition (SAR ATR) with limited data has recently been a hot research topic to enhance weak generalization. Despite many excellent methods being proposed, a fundamental theory is lacked to explain what problem the limited SAR data causes, leading to weak generalization of ATR. In this paper, we establish a causal ATR model demonstrating that noise $N$ that could be blocked with ample SAR data, becomes a confounder with limited data for recognition. As a result, it has a detrimental causal effect damaging the efficacy of feature $X$ extracted from SAR images, leading to weak generalization of SAR ATR with limited data. The effect of $N$ on feature can be estimated and eliminated by using backdoor adjustment to pursue the direct causality between $X$ and the predicted class $Y$. However, it is difficult for SAR images to precisely estimate and eliminated the effect of $N$ on $X$. The limited SAR data scarcely powers the majority of existing optimization losses based on empirical risk minimization (ERM), thus making it difficult to effectively eliminate $N$'s effect. To tackle with difficult estimation and elimination of $N$'s effect, we propose a dual invariance comprising the inner-class invariant proxy and the noise-invariance loss. Motivated by tackling change with invariance, the inner-class invariant proxy facilitates precise estimation of $N$'s effect on $X$ by obtaining accurate invariant features for each class with the limited data. The noise-invariance loss transitions the ERM's data quantity necessity into a need for noise environment annotations, effectively eliminating $N$'s effect on $X$ by cleverly applying the previous $N$'s estimation as the noise environment annotations. Experiments on three benchmark datasets indicate that the proposed method achieves superior performance.
A Dirichlet character analogue of Ramanujan's formula for odd zeta values

In 2001, Kanemitsu, Tanigawa, and Yoshimoto studied the following generalized Lambert series, $$ \sum_{n=1}^{\infty} \frac{n^{N-2h} }{\exp(n^N x)-1}, $$ for $N \in \mathbb{N}$ and $h\in \mathbb{Z}$ with some restriction on $h$. Recently, Dixit and the last author pointed out that this series has already been present in the Lost Notebook of Ramanujan with a more general form. Although, Ramanujan did not provide any transformation identity for it. In the same paper, Dixit and the last author found an elegant generalization of Ramanujan's celebrated identity for $\zeta(2m+1)$ while extending the results of Kanemitsu et al. In a subsequent work, Kanemitsu et al. explored another extended version of the aforementioned series, namely, $$\sum_{r=1}^{q}\sum_{n=1}^{\infty} \frac{\chi(r)n^{N-2h}{\exp\left(-\frac{r}{q}n^N x\right)}}{1-\exp({-n^N x})},$$ where $\chi$ denotes a Dirichlet character modulo $q$, $N\in 2\mathbb{N}$ and with some restriction on the variable $h$. In the current paper, we investigate the above series for {\it any} $N \in \mathbb{N}$ and $h \in \mathbb{Z}$. We obtain a Dirichlet character analogue of Dixit and the last author's identity and there by derive a two variable generalization of Ramanujan's identity for $\zeta(2m+1)$. Moreover, we establish a new identity for $L(1/3, \chi)$ analogous to Ramanujan's famous identity for $\zeta(1/2)$.
Are They All Good? Studying Practitioners' Expectations on the Readability of Log Messages

Developers write logging statements to generate logs that provide run-time information for various tasks. The readability of log messages in the logging statements (i.e., the descriptive text) is rather crucial to the value of the generated logs. Immature log messages may slow down or even obstruct the process of log analysis. Despite the importance of log messages, there is still a lack of standards on what constitutes good readability of log messages and how to write them. In this paper, we conduct a series of interviews with 17 industrial practitioners to investigate their expectations on the readability of log messages. Through the interviews, we derive three aspects related to the readability of log messages, including Structure, Information, and Wording, along with several specific practices to improve each aspect. We validate our findings through a series of online questionnaire surveys and receive positive feedback from the participants. We then manually investigate the readability of log messages in large-scale open source systems and find that a large portion (38.1%) of the log messages have inadequate readability. Motivated by such observation, we further explore the potential of automatically classifying the readability of log messages using deep learning and machine learning models. We find that both deep learning and machine learning models can effectively classify the readability of log messages with a balanced accuracy above 80.0% on average. Our study provides comprehensive guidelines for composing log messages to further improve practitioners' logging practices.
D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field

Realistic virtual humans play a crucial role in numerous industries, such as metaverse, intelligent healthcare, and self-driving simulation. But creating them on a large scale with high levels of realism remains a challenge. The utilization of deep implicit function sparks a new era of image-based 3D clothed human reconstruction, enabling pixel-aligned shape recovery with fine details. Subsequently, the vast majority of works locate the surface by regressing the deterministic implicit value for each point. However, should all points be treated equally regardless of their proximity to the surface? In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface. This simple "value ⇒ distribution" transition yields significant improvements on nearly all the baselines. Furthermore, qualitative results demonstrate that the models trained using our uncertainty distribution loss, can capture more intricate wrinkles, and realistic limbs. Code and models are available for research purposes at github.com/psyai-net/D-IF release.
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces

As responsible AI gains importance in machine learning algorithms, properties like fairness, adversarial robustness, and causality have received considerable attention in recent years. However, despite their individual significance, there remains a critical gap in simultaneously exploring and integrating these properties. In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models (SCMs) in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes. We use SCMs and sensitive attributes to create a fair metric and apply it to measure semantic similarity among individuals. By introducing a novel causal adversarial perturbation (CAP) and applying adversarial training, we create a new regularizer that combines individual fairness, causality, and robustness in the classifier. Our method is evaluated on both real-world and synthetic datasets, demonstrating its effectiveness in achieving an accurate classifier that simultaneously exhibits fairness, adversarial robustness, and causal awareness.
Minimum Path Cover: The Power of Parameterization

Computing a minimum path cover (MPC) of a directed acyclic graph (DAG) is a fundamental problem with a myriad of applications, including reachability. Although it is known how to solve the problem by a simple reduction to minimum flow, recent theoretical advances exploit this idea to obtain algorithms parameterized by the number of paths of an MPC, known as the width. These results obtain fast [M\"akinen et al., TALG] and even linear time [C\'aceres et al., SODA 2022] algorithms in the small-width regime. In this paper, we present the first publicly available high-performance implementation of state-of-the-art MPC algorithms, including the parameterized approaches. Our experiments on random DAGs show that parameterized algorithms are orders-of-magnitude faster on dense graphs. Additionally, we present new pre-processing heuristics based on transitive edge sparsification. We show that our heuristics improve MPC-solvers by orders-of-magnitude.
Entropy and Thermodynamic Temperature in Nonequilibrium Classical Thermodynamics as Immediate Consequences of the Hahn-Banach Theorem: I. Existence

The Kelvin-Planck statement of the Second Law of Thermodynamics is a stricture on the nature of heat receipt by any body suffering a cyclic process. It makes no mention of temperature or of entropy. Beginning with a Kelvin-Planck statement of the Second Law, we show that entropy and temperature -- in particular, existence of functions that relate the local specific entropy and thermodynamic temperature to the local state in a material body -- emerge immediately and simultaneously as consequences of the Hahn-Banach Theorem. Existence of such functions of state requires no stipulation that their domains be restricted to equilibrium states. Further properties, including uniqueness, are addressed in a companion paper.
A Comparative Analysis of Nature-Inspired Feature Selection Algorithms in Predicting Student Performance

Predicting student performance is key in leveraging effective pre-failure interventions for at-risk students. As educational data grows larger, more effective means of analyzing student data in a timely manner are needed in order to provide useful predictions and interventions. In this paper, an analysis was conducted to determine the relative performance of a suite of nature-inspired algorithms in the feature-selection portion of ensemble algorithms used to predict student performance. A Swarm Intelligence ML engine (SIMLe) was developed to run this suite in tandem with a series of traditional ML classification algorithms to analyze three student datasets: instance-based clickstream data, hybrid single-course performance with pre-course rnetadata, and student meta-performance when taking multiple courses simultaneously. These results were then compared to previous predictive algorithms and, for all datasets analyzed, it was found that leveraging an ensemble approach using nature-inspired algorithms for feature selection and traditional ML algorithms for classification significantly increased predictive accuracy while also reducing feature set size by up to 65%. The SIMLe test engine was also able to identify keystone data features useful in creating pre- and intra-course interventions.
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning

With the success of self-supervised learning, multimodal foundation models have rapidly adapted a wide range of downstream tasks driven by vision and language (VL) pretraining. State-of-the-art methods achieve impressive performance by pre-training on large-scale datasets. However, bridging the semantic gap between the two modalities remains a nonnegligible challenge for VL tasks. In this work, we propose an efficient computation framework for multimodal alignment by introducing a novel visual semantic module to further improve the performance of the VL tasks. Specifically, we propose a flexible model, namely Artificial-Spiking Hierarchical Networks (ASH-Nets), which combines the complementary advantages of Artificial neural networks (ANNs) and Spiking neural networks (SNNs) to enrich visual semantic representations. In particular, a visual concrete encoder and a semantic abstract encoder are constructed to learn continuous and discrete latent variables to enhance the flexibility of semantic encoding. Considering the spatio-temporal properties of SNNs modeling, we introduce a contrastive learning method to optimize the inputs of similar samples. This can improve the computational efficiency of the hierarchical network, while the augmentation of hard samples is beneficial to the learning of visual representations. Furthermore, the Spiking to Text Uni-Alignment Learning (STUA) pre-training method is proposed, which only relies on text features to enhance the encoding ability of abstract semantics. We validate the performance on multiple well-established downstream VL tasks. Experiments show that the proposed ASH-Nets achieve competitive results.
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making

Market making (MM) has attracted significant attention in financial trading owing to its essential function in ensuring market liquidity. With strong capabilities in sequential decision-making, Reinforcement Learning (RL) technology has achieved remarkable success in quantitative trading. Nonetheless, most existing RL-based MM methods focus on optimizing single-price level strategies which fail at frequent order cancellations and loss of queue priority. Strategies involving multiple price levels align better with actual trading scenarios. However, given the complexity that multi-price level strategies involves a comprehensive trading action space, the challenge of effectively training profitable RL agents for MM persists. Inspired by the efficient workflow of professional human market makers, we propose Imitative Market Maker (IMM), a novel RL framework leveraging both knowledge from suboptimal signal-based experts and direct policy interactions to develop multi-price level MM strategies efficiently. The framework start with introducing effective state and action representations adept at encoding information about multi-price level orders. Furthermore, IMM integrates a representation learning unit capable of capturing both short- and long-term market trends to mitigate adverse selection risk. Subsequently, IMM formulates an expert strategy based on signals and trains the agent through the integration of RL and imitation learning techniques, leading to efficient learning. Extensive experimental results on four real-world market datasets demonstrate that IMM outperforms current RL-based market making strategies in terms of several financial criteria. The findings of the ablation study substantiate the effectiveness of the model components.
A hybrid Decoder-DeepONet operator regression framework for unaligned observation data

Deep neural operators (DNOs) have been utilized to approximate nonlinear mappings between function spaces. However, DNOs are confronted with challenges stemming from expanded dimensionality and computational costs tied to unaligned observation data, which ultimately compromise the accuracy of predictions. In this study, we present a hybrid Decoder-DeepONet framework to effectively handle unaligned data. This framework is advanced through its extension to the Multi-Decoder-DeepONet, which leverages an average field to enhance input augmentation. Furthermore, on the basis of the universal approximation theorem, we demonstrate that these frameworks preserve consistencies with operator approximation theory despite the substitution of the product with a decoder net. Two numerical experiments, Darcy problem and flow-field around an airfoil, are conducted to demonstrate the advantages of the proposed methods over conventional DeepONet approaches. The results reveal that both Decoder-DeepONet and Multi-Decoder-DeepONet utilize more compact training data dimensions and occupy less space, markedly enhancing prediction accuracy in the context of unaligned data.
A Novel Method of Function Extrapolation Inspired by Techniques in Low-entangled Many-body Physics

We introduce a novel extrapolation algorithm inspired by quantum mechanics and evaluate its performance against linear prediction. Our method involves mapping function values onto a quantum state and estimating future function values by minimizing entanglement entropy. We demonstrate the effectiveness of our approach on various simple functions, both with and without noise, comparing it to linear prediction. Our results show that the proposed algorithm produces extrapolations comparable to linear prediction, while exhibiting improved performance for functions with sharp features.
Efficient Algorithms for Attributed Graph Alignment with Vanishing Edge Correlation

Graph alignment refers to the task of finding the vertex correspondence between two positively correlated graphs. Extensive study has been done on polynomial-time algorithms for the graph alignment problem under the Erd\H{o}s--R\'enyi graph pair model, where the two graphs are Erd\H{o}s--R\'enyi graphs with edge probability $q_\mathrm{u}$, correlated under certain vertex correspondence. To achieve exact recovery of the vertex correspondence, all existing algorithms at least require the edge correlation coefficient $\rho_\mathrm{u}$ between the two graphs to satisfy $\rho_\mathrm{u}>\sqrt{\alpha}$, where $\alpha \approx 0.338$ is Otter's tree-counting constant. Moreover, it is conjectured in [1] that no polynomial-time algorithm can achieve exact recovery under weak edge correlation $\rho_\mathrm{u}<\sqrt{\alpha}$. In this paper, we show that with a vanishing amount of additional attribute information, exact recovery is polynomial-time feasible under vanishing edge correlation $\rho_\mathrm{u} \ge n^{-\Theta(1)}$. We identify a local tree structure, which incorporates one layer of user information and one layer of attribute information, and apply the subgraph counting technique to such structures. A polynomial-time algorithm is proposed that recovers the vertex correspondence for all but a vanishing fraction of vertices. We then further refine the algorithm output to achieve exact recovery. The motivation for considering additional attribute information comes from the widely available side information in real-world applications, such as the user's birthplace and educational background on LinkedIn and Twitter social networks.
Rethinking Image Forgery Detection via Contrastive Learning and Unsupervised Clustering

Image forgery detection aims to detect and locate forged regions in an image. Most existing forgery detection algorithms formulate classification problems to classify pixels into forged or pristine. However, the definition of forged and pristine pixels is only relative within one single image, e.g., a forged region in image A is actually a pristine one in its source image B (splicing forgery). Such a relative definition has been severely overlooked by existing methods, which unnecessarily mix forged (pristine) regions across different images into the same category. To resolve this dilemma, we propose the FOrensic ContrAstive cLustering (FOCAL) method, a novel, simple yet very effective paradigm based on contrastive learning and unsupervised clustering for the image forgery detection. Specifically, FOCAL 1) utilizes pixel-level contrastive learning to supervise the high-level forensic feature extraction in an image-by-image manner, explicitly reflecting the above relative definition; 2) employs an on-the-fly unsupervised clustering algorithm (instead of a trained one) to cluster the learned features into forged/pristine categories, further suppressing the cross-image influence from training data; and 3) allows to further boost the detection performance via simple feature-level concatenation without the need of retraining. Extensive experimental results over six public testing datasets demonstrate that our proposed FOCAL significantly outperforms the state-of-the-art competing algorithms by big margins: +24.3% on Coverage, +18.6% on Columbia, +17.5% on FF++, +14.2% on MISD, +13.5% on CASIA and +10.3% on NIST in terms of IoU. The paradigm of FOCAL could bring fresh insights and serve as a novel benchmark for the image forgery detection task. The code is available at https://github.com/HighwayWu/FOCAL.