Miracle-dz/UniFAR-FTUs huggingface dataset:
Casimir Physics beyond the Proximity Force Approximation: The Derivative Expansion

We review the derivative expansion (DE) method in Casimir physics, an approach which extends the proximity force approximation (PFA). After introducing and motivating the DE in contexts other than the Casimir effect, we present different examples which correspond to that realm. We focus on different particular geometries, boundary conditions, types of fields, and quantum and thermal fluctuations. Besides providing various examples where the method can be applied, we discuss a concrete example for which the DE cannot be applied; namely, the case of perfect Neumann conditions in 2+1 dimensions. By the same example, we show how a more realistic type of boundary condition circumvents the problem. We also comment on the application of the DE to the Casimir–Polder interaction which provides a broader perspective on particle–surface interactions.
Rigorously proven chaos in chemical kinetics

This study addresses a longstanding question regarding the mathematical proof of chaotic behavior in kinetic differential equations. Following the numerous numerical and experimental results in the past 50 years, we introduce two formal chemical reactions that rigorously demonstrate this behavior. Our approach involves transforming chaotic equations into kinetic differential equations and subsequently realizing these equations through formal chemical reactions. The findings present a novel perspective on chaotic dynamics within chemical kinetics, thereby resolving a longstanding open problem.
Homogeneity of arithmetic quantum limits for hyperbolic $4$-manifolds

We work toward the arithmetic quantum unique ergodicity (AQUE) conjecture for sequences of Hecke--Maass forms on hyperbolic $4$-manifolds. We show that limits of such forms can only scar on totally geodesic $3$-submanifolds, and in fact that all ergodic components of the microlocal lift other than the uniform measure arise from the uniform measures on these submanifolds.
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, they sometimes suffer from producing hallucinations, particularly in cases where they may generate untruthful responses despite possessing the correct knowledge. In this paper, we propose TruthX, an inference-time method to elicit the truthfulness of LLMs by editing their internal representations in truthful space. TruthX employs an auto-encoder to map LLM's representations into semantic and truthful latent spaces respectively, and applies contrastive learning to identify a truthful editing direction within the truthful space. During inference, by editing LLM's internal representations in truthful space, TruthX effectively enhances the truthfulness of LLMs. Experiments show that TruthX effectively improves the truthfulness of 13 advanced LLMs by an average of 20% on TruthfulQA benchmark. Further analyses suggest that the truthful space acquired by TruthX plays a pivotal role in controlling LLM to produce truthful or hallucinatory responses.
Graph Neural Networks and Arithmetic Circuits

We characterize the computational power of neural networks that follow the graph neural network (GNN) architecture, not restricted to aggregate-combine GNNs or other particular types. We establish an exact correspondence between the expressivity of GNNs using diverse activation functions and arithmetic circuits over real numbers. In our results the activation function of the network becomes a gate type in the circuit. Our result holds for families of constant depth circuits and networks, both uniformly and non-uniformly, for all common activation functions.
Response of Atmospheric pCO2 to Glacial Changes in the Southern Ocean Amplified by Carbonate Compensation

Atmospheric carbon dioxide concentration (pCO2) varies by about 100ppm during glacial‐interglacial cycles. Previous studies suggest that the strongly stratified Southern Ocean at the Last Glacial Maximum increases the oceanic storage of carbon, but the glacial reduction of atmospheric pCO2 simulated by ocean general circulation models (OGCMs) does not reach 100ppm. One candidate for the underestimation is that carbonate compensation is not explicitly incorporated in the previous OGCM simulations. Therefore, we quantitatively evaluate the impact of carbonate compensation on the glacial atmospheric pCO2 by using an OGCM coupled with an ocean sediment model. As suggested by previous box model studies, our OGCM simulations show that the enhanced Southern Ocean stratification amplifies the decrease in atmospheric pCO2 due to carbonate compensation. Considering the enhanced stratification in the Southern Ocean, we obtain a 26‐ppm drawdown of atmospheric pCO2 by carbonate compensation, and the full reduction from our pre‐industrial simulation reaches 73ppm. Both the increase in ventilation ages in the deep Atlantic and Southern Oceans and the growth of export production in the subantarctic region reduce the bottom‐water carbonate ion and promote deposited carbonate dissolution. Consequently, a greater imbalance between the river inflow and burial loss of carbonate rises ocean alkalinity, lowering atmospheric pCO2. We suggest that the reproducibility of the Southern Ocean process is essential for controlling the magnitude of atmospheric pCO2 decline due to carbonate compensation.
Lower Bounds for Leaf Rank of Leaf Powers

Leaf powers and $k$-leaf powers have been studied for over 20 years, but there are still several aspects of this graph class that are poorly understood. One such aspect is the leaf rank of leaf powers, i.e. the smallest number $k$ such that a graph $G$ is a $k$-leaf power. Computing the leaf rank of leaf powers has proved a hard task, and furthermore, results about the asymptotic growth of the leaf rank as a function of the number of vertices in the graph have been few and far between. We present an infinite family of rooted directed path graphs that are leaf powers, and prove that they have leaf rank exponential in the number of vertices (utilizing a type of subtree model first presented by Rautenbach [Some remarks about leaf roots. Discrete mathematics, 2006]). This answers an open question by Brandst\"adt et al. [Rooted directed path graphs are leaf powers. Discrete mathematics, 2010].
Saving the legacy of Hero Ibash: Evaluating Four Language Models for Aminoacian

This study assesses four cutting-edge language models in the underexplored Aminoacian language. Through evaluation, it scrutinizes their adaptability, effectiveness, and limitations in text generation, semantic coherence, and contextual understanding. Uncovering insights into these models' performance in a low-resourced language, this research pioneers pathways to bridge linguistic gaps. By offering benchmarks and understanding challenges, it lays groundwork for future advancements in natural language processing, aiming to elevate the applicability of language models in similar linguistic landscapes, marking a significant step toward inclusivity and progress in language technology.
Robotising Psychometrics: Validating Wellbeing Assessment Tools in Child-Robot Interactions

The interdisciplinary nature of Child-Robot Interaction (CRI) fosters incorporating measures and methodologies from many established domains. However, when employing CRI approaches to sensitive avenues of health and wellbeing, caution is critical in adapting metrics to retain their safety standards and ensure accurate utilisation. In this work, we conducted a secondary analysis to previous empirical work, investigating the reliability and construct validity of established psychological questionnaires such as the Short Moods and Feelings Questionnaire (SMFQ) and three subscales (generalised anxiety, panic and low mood) of the Revised Child Anxiety and Depression Scale (RCADS) within a CRI setting for the assessment of mental wellbeing. Through confirmatory principal component analysis, we have observed that these measures are reliable and valid in the context of CRI. Furthermore, our analysis revealed that scales communicated by a robot demonstrated a better fit than when self-reported, underscoring the efficiency and effectiveness of robot-mediated psychological assessments in these settings. Nevertheless, we have also observed variations in item contributions to the main factor, suggesting potential areas of examination and revision (e.g., relating to physiological changes, inactivity and cognitive demands) when used in CRI. Findings from this work highlight the importance of verifying the reliability and validity of standardised metrics and assessment tools when employed in CRI settings, thus, aiming to avoid any misinterpretations and misrepresentations.
EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs

Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range of amplitudes from very small to relatively large due to factors such as the inter-subject variability, risks the models to be dominated by higher amplitude ranges, and (3) The absence of explicit segmentation in the continuous-valued sequences which can result in less informative representations. To address these challenges, we introduce EEG2Rep, a self-prediction approach for self-supervised representation learning from EEG. Two core novel components of EEG2Rep are as follows: 1) Instead of learning to predict the masked input from raw EEG, EEG2Rep learns to predict masked input in latent representation space, and 2) Instead of conventional masking methods, EEG2Rep uses a new semantic subsequence preserving (SSP) method which provides informative masked inputs to guide EEG2Rep to generate rich semantic representations. In experiments on 6 diverse EEG tasks with subject variability, EEG2Rep significantly outperforms state-of-the-art methods. We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50\% of EEG recordings will result in the most accurate results on all 6 tasks on average. Finally, we show that EEG2Rep is robust to noise addressing a significant challenge that exists in EEG data. Models and code are available at: https://github.com/Navidfoumani/EEG2Rep
Polos: Multimodal Metric Learning from Human Feedback for Image Captioning

Establishing an automatic evaluation metric that closely aligns with human judgments is essential for effectively developing image captioning models. Recent data-driven metrics have demonstrated a stronger correlation with human judgments than classic metrics such as CIDEr; however they lack sufficient capabilities to handle hallucinations and generalize across diverse images and texts partially because they compute scalar similarities merely using embeddings learned from tasks unrelated to image captioning evaluation. In this study, we propose Polos, a supervised automatic evaluation metric for image captioning models. Polos computes scores from multimodal inputs, using a parallel feature extraction mechanism that leverages embeddings trained through large-scale contrastive learning. To train Polos, we introduce Multimodal Metric Learning from Human Feedback (M$^2$LHF), a framework for developing metrics based on human feedback. We constructed the Polaris dataset, which comprises 131K human judgments from 550 evaluators, which is approximately ten times larger than standard datasets. Our approach achieved state-of-the-art performance on Composite, Flickr8K-Expert, Flickr8K-CF, PASCAL-50S, FOIL, and the Polaris dataset, thereby demonstrating its effectiveness and robustness.
Fractional Linear Matroid Matching is in quasi-NC

The matching and linear matroid intersection problems are solvable in quasi-NC, meaning that there exist deterministic algorithms that run in polylogarithmic time and use quasi-polynomially many parallel processors. However, such a parallel algorithm is unknown for linear matroid matching, which generalizes both of these problems. In this work, we propose a quasi-NC algorithm for fractional linear matroid matching, which is a relaxation of linear matroid matching and commonly generalizes fractional matching and linear matroid intersection. Our algorithm builds upon the connection of fractional matroid matching to non-commutative Edmonds' problem recently revealed by Oki and Soma~(2023). As a corollary, we also solve black-box non-commutative Edmonds' problem with rank-two skew-symmetric coefficients.
Automated Statistical Model Discovery with Language Models

Statistical model discovery involves a challenging search over a vast space of models subject to domain-specific modeling constraints. Efficiently searching over this space requires human expertise in modeling and the problem domain. Motivated by the domain knowledge and programming capabilities of large language models (LMs), we introduce a method for language model driven automated statistical model discovery. We cast our automated procedure within the framework of Box's Loop: the LM iterates between proposing statistical models represented as probabilistic programs, acting as a modeler, and critiquing those models, acting as a domain expert. By leveraging LMs, we do not have to define a domain-specific language of models or design a handcrafted search procedure, key restrictions of previous systems. We evaluate our method in three common settings in probabilistic modeling: searching within a restricted space of models, searching over an open-ended space, and improving classic models under natural language constraints (e.g., this model should be interpretable to an ecologist). Our method matches the performance of previous systems, identifies models on par with human expert designed models, and extends classic models in interpretable ways. Our results highlight the promise of LM driven model discovery.
From Inverse Optimization to Feasibility to ERM

Inverse optimization involves inferring unknown parameters of an optimization problem from known solutions, and is widely used in fields such as transportation, power systems and healthcare. We study the contextual inverse optimization setting that utilizes additional contextual information to better predict the unknown problem parameters. We focus on contextual inverse linear programming (CILP), addressing the challenges posed by the non-differentiable nature of LPs. For a linear prediction model, we reduce CILP to a convex feasibility problem allowing the use of standard algorithms such as alternating projections. The resulting algorithm for CILP is equipped with a linear convergence guarantee without additional assumptions such as degeneracy or interpolation. Next, we reduce CILP to empirical risk minimization (ERM) on a smooth, convex loss that satisfies the Polyak-Lojasiewicz condition. This reduction enables the use of scalable first-order optimization methods to solve large non-convex problems, while maintaining theoretical guarantees in the convex setting. Finally, we experimentally validate our approach on both synthetic and real-world problems, and demonstrate improved performance compared to existing methods.
Performance limits of information engines

We review recent studies of a colloidal information engine that consists of a bead in water and held by an optical trap. The bead is ratcheted upward without any apparent external work, by taking advantage of favorable thermal fluctuations. Much of the previous work on such engines aimed to show that accounting for information-processing costs can reconcile the observed motion with the second law of thermodynamics. By contrast, we focus on the factors that limit the performance of such engines by optimizing variously the upward velocity, rate of gravitational free-energy extraction, or ability to track a trajectory. We then consider measurement noise, which degrades engine performance. A naive use of noisy measurements in the feedback algorithm leads to a phase transition at finite signal-to-noise ratio: below the transition, the engine no longer functions. A more sophisticated, `Bayesian' algorithm eliminates the phase transition and improves performance. Finally, operating the information engine in a nonequilibrium environment with extra force fluctuations can enhance the performance by orders of magnitude, even to the point where the energy extracted exceeds that needed to run the information processing. Autonomous implementations of an information engine in such environments could be powered entirely by the additional energy of the bath.
Multi-objective Differentiable Neural Architecture Search

Pareto front profiling in multi-objective optimization (MOO), i.e. finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives like neural network training. Typically, in MOO neural architecture search (NAS), we aim to balance performance and hardware metrics across devices. Prior NAS approaches simplify this task by incorporating hardware constraints into the objective function, but profiling the Pareto front necessitates a search for each constraint. In this work, we propose a novel NAS algorithm that encodes user preferences for the trade-off between performance and hardware metrics, and yields representative and diverse architectures across multiple devices in just one search run. To this end, we parameterize the joint architectural distribution across devices and multiple objectives via a hypernetwork that can be conditioned on hardware features and preference vectors, enabling zero-shot transferability to new devices. Extensive experiments with up to 19 hardware devices and 3 objectives showcase the effectiveness and scalability of our method. Finally, we show that, without additional costs, our method outperforms existing MOO NAS methods across qualitatively different search spaces and datasets, including MobileNetV3 on ImageNet-1k and a Transformer space on machine translation.
A patient-derived amyotrophic lateral sclerosis blood-brain barrier cell model reveals focused ultrasound-mediated anti-TDP-43 antibody delivery

Background Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disorder with minimally effective treatment options. An important hurdle in ALS drug development is the non-invasive therapeutic access to the motor cortex currently limited by the presence of the blood-brain barrier (BBB). Focused ultrasound and microbubble (FUS+MB) treatment is an emerging technology that was successfully used in ALS patients to temporarily open the cortical BBB. However, FUS+MB-mediated drug delivery across ALS patients’ BBB has not yet been reported. Similarly, the effects of FUS+MB on human ALS BBB cells remain unexplored. Methods Here we established the first FUS+MB-compatible, fully-human ALS patient-cell-derived BBB model based on induced brain endothelial-like cells (iBECs) to study anti-TDP-43 antibody delivery and FUS+MB bioeffects in vitro. Results Generated ALS iBECs recapitulated disease-specific hallmarks of BBB pathology, including changes to BBB integrity, permeability and TDP-43 proteinopathy. Our results also identified differences between sporadic ALS and familial (C9orf72 expansion carrying) ALS iBECs reflecting patient heterogeneity associated with disease subgroups. Studies in these models revealed successful ALS iBEC monolayer opening in vitro with a lack of adverse cellular effects of FUS+MB. This was accompanied by the molecular bioeffects of FUS+MB in ALS iBECs including changes in expression of tight and adherens junction markers, and drug transporter and inflammatory mediators, with sporadic and C9orf72 ALS iBECs generating transient specific responses. Additionally, we demonstrated an effective increase in the delivery of anti-TDP-43 antibody with FUS+MB in C9orf72 (2.7-fold) and sporadic (1.9-fold) ALS iBECs providing the first proof-of-concept evidence that FUS+MB can be used to enhance the permeability of large molecule therapeutics across the BBB in a human ALS in vitro model. Conclusions Together, our study describes the first characterisation of cellular and molecular responses of ALS iBECs to FUS+MB and provides a fully-human platform for FUS+MB-mediated drug delivery screening on an ALS BBB in vitro model.
Decoupled degradation and translation enables noise-modulation by poly(A)-tails

Poly(A)-tails are crucial for mRNA translation and degradation, but the exact relationship between tail length and mRNA kinetics remains unclear. Here we employ a small library of identical mRNAs that differ only in their poly(A)-tail length to examine their behavior in human embryonic kidney cells. We find that tail length strongly correlates with mRNA degradation rates, but is decoupled from translation. Interestingly, an optimal tail length of ∼100 nucleotides displays the highest translation rate, which is identical to the average endogenous tail length measured by nanopore sequencing. Furthermore, poly(A)-tail length variability––a feature of endogenous mRNAs––impacts translation efficiency but not mRNA degradation rates. Stochastic modelling combined with single-cell tracking reveals that poly(A)-tails provide cells with an independent handle to tune gene expression fluctuations by decoupling mRNA degradation and translation. Together, this work contributes to the basic understanding of gene expression regulation and has potential applications in nucleic acid therapeutics.
Association of Macular Pigment Density with Plasma Macular Carotenoids levels, And lipids in Indian patients with early Age-Related Macular Degeneration

Purpose The objective of the present study was to investigate the relationship of macular pigment optical density (MPOD) with plasma carotenoids [(L) and (Z)] and serum lipids in South Indian young healthy volunteers and patients with early AMD. Methods Two hundred and fourteen (N= 214) study participants (Healthy control group (N) = 178; Early AMD group (N) = 36) were enrolled after getting their written informed consent. The MPOD of the study participants was assessed using MPS II (Electron Technology, UK) after completing their routine ocular examination. Serum lipids were measured by the standard technique. Plasma levels of L, Z, lycopene and beta-carotene were estimated by HPLC with PDA detector. Statistical analysis used Correlations among variables in serum, plasma and the MPOD were established using Spearman’s rho correlation coefficient. Results The overall mean MPOD in healthy control group and early AMD group was found to be 0.47 ± 0.16 (N= 178; 317 eyes) and 0.35 ± 0.22 (N=36; 38 eyes) at 1° eccentricity respectively and was found to be significantly low as compared to healthy control group (p<0.001). A strong positive association was found between plasma L, Z and L+Z and MPOD. Serum HDL showed a strong negative association with MPOD and other lipids showed very weak association. MPOD was unaffected by BMI. Conclusions MPOD is positively associated with plasma L,Z and L+Z, adding further evidence that additional intake of L/Z may be beneficial in delaying the risk of AMD in our population