Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalising sequence models for epigenome predictions with tissue and assay embeddings

Sequence modelling approaches for epigenetic profile prediction have recently expanded in terms of sequence length, model size, and profile diversity. However, current models cannot infer on many experimentally feasible tissue and assay pairs due to poor usage of contextual information, limiting $\textit{in silico}$ understanding of regulatory genomics. We demonstrate that strong correlation can be achieved across a large range of experimental conditions by integrating tissue and assay embeddings into a Contextualised Genomic Network (CGN). In contrast to previous approaches, we enhance long-range sequence embeddings with contextual information in the input space, rather than expanding the output space. We exhibit the efficacy of our approach across a broad set of epigenetic profiles and provide the first insights into the effect of genetic variants on epigenetic sequence model training. Our general approach to context integration exceeds state of the art in multiple settings while employing a more rigorous validation procedure.
A link between shape dependent lifetimes and thermal escape in quantum dots and rings

Understanding the optical emission characteristics of semiconductor nanostructures is important when determing their device applicability. The emission depends on the material and its geometry, but also depends on other processes such as thermal escape from the nanostructure. Although it is widely accepted that scattering involving longitudinal optical phonons is the key process in thermal escape, it remains unclear why some quantum structures thermally emit excitons and other single charge carriers. To investigate this phenomena we theoretically determine the energy levels and temperature-lifetime relationships of quantum dots and rings. We find that replicating the observed temperature dependence of the exciton lifetime requires both an eigenspectrum and a thermal escape mechanism which are geometry dependent. This suggests that geometry may be a significant factor in determining the dominant thermal escape process in quantum structures.
UNCOVER: JWST Spectroscopy of Three Cold Brown Dwarfs at Kiloparsec-scale Distances

We report JWST/NIRSpec spectra of three distant T-type brown dwarfs identified in the Ultradeep NIRSpec and NIRCam ObserVations before the Epoch of Reionization (UNCOVER) survey of the Abell 2744 lensing field. One source was previously reported as a candidate T dwarf on the basis of NIRCam photometry, while two sources were initially identified as candidate active galactic nuclei. Low-resolution 1–5 μm spectra confirm the presence of molecular features consistent with T dwarf atmospheres, and comparison to spectral standards infers classifications of sdT1, T6, and T8–T9. The warmest source, UNCOVER-BD-1, shows evidence of subsolar metallicity, and atmosphere model fits indicate T eff = 1300 K and [M/H] ∼ −1.0, making this one of the few spectroscopically confirmed T subdwarfs known. The coldest source, UNCOVER-BD-3, is near the T/Y dwarf boundary with T eff = 550 K, and our analysis indicates the presence of PH3 in the 3–5 μm region, favored over CO2 and a possible indicator of subsolar metallicity. We estimate distances of 0.9–4.5 kpc from the Galactic midplane, making these the most distant brown dwarfs with spectroscopic confirmation. Population simulations indicate high probabilities of membership in the Galactic thick disk for two of these brown dwarfs, and potential halo membership for UNCOVER-BD-1. Our simulations indicate that there are approximately 5 T dwarfs and 1–2 L dwarfs in the Abell 2744 field down to F444W = 30 AB mag, roughly one-third of which are thick disk members. These results highlight the utility of deep JWST/NIRSpec spectroscopy for identifying and characterizing the oldest metal-poor brown dwarfs in the Milky Way.
A motivic Fundamental Lemma

In this paper we prove motivic versions of the Langlands-Shelstad Fundamental Lemma and Ng\^o's Geometric Stabilization. To achieve this, we follow the strategy from the recent proof by Groechenig, Wyss and Ziegler which avoided the use of perverse sheaves using instead $p$-adic integration and Tate duality. We make a key use of a construction of Denef and Loeser which assigns a virtual motive to any definable set in the theory of pseudo-finite fields.
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning

The recent surge of generative AI has been fueled by the generative power of diffusion probabilistic models and the scalable capabilities of large language models. Despite their potential, it remains elusive whether diffusion language models can solve general language tasks comparable to their autoregressive counterparts. This paper demonstrates that scaling diffusion models w.r. t. data, sizes, and tasks can effectively make them strong language learners. We build competent diffusion language models at scale by first acquiring knowledge from massive data via masked language modeling pretraining thanks to their intrinsic connections. We then reprogram pretrained masked language models into diffusion language models via diffusive adaptation, wherein task-specific finetuning and instruction finetuning are explored to unlock their versatility in solving general language tasks. Experiments show that scaling diffusion language models consistently improves performance across downstream language tasks. We further discover that instruction finetuning can elicit zero-shot and few-shot in-context learning abilities that help tackle many unseen tasks by following natural language instructions, and show promise in advanced and challenging abilities such as reasoning.
Thermal quench of a dynamical QCD model in an external electric field

In this article, we investigate the thermal equilibration of the holographic QCD model dual to the Einstein-Maxwell-Dilaton (EMD) gravity in the presence of an external electric field. The model captures the QCD features at finite temperature and finite chemical potential in both confinement and deconfinement phases and could be considered a good candidate to study the dynamics of the strongly interacting system in out-of-equilibrium conditions. For this purpose, we examine the instability imposed by an external electric field using the AdS/CFT dictionary and study the electric current flow and its relaxation for this holographic model in the presence of constant and time-dependent electric fields. We study the effects of temperature, electric field strength, and chemical potential on the stationary state. Additionally, we investigate the behavior of the equilibration time and its rescaled value for the time-dependent case. Finally, we compare our results to those from other holographic models.
The inequalities of Chern classes and Riemann-Roch type inequalities

Motivated by Koll\'{a}r-Matsusaka's Riemann-Roch type inequalities, applying effective very ampleness of adjoint bundles on Fujita conjecture and log-concavity given by Khovanskii-Teissier inequalities, we show that for any partition $\lambda$ of the positive integer $d$ there exists a universal bivariate polynomial $Q_\lambda(x, y)$ which has deg $Q \leq d$ and whose coefficients depend only on $n$, such that for any projective manifold $X$ of dimension $n$ and any ample line bundle $L$ on $X$, \begin{equation*} \left|c_\lambda(X)\cdot L^{n -d}\right|\leq \frac{Q_{\lambda}(L^{n}, K_X \cdot L^{n -1} )}{(L^{n})^{d-1}}, \end{equation*} where $K_X$ is the canonical bundle of $X$ and $c_\lambda(X)$ is the monomial Chern class given by the partition $\lambda$. As a special case, when $K_X$ or $-K_X$ is ample, this implies that there exists a constant $c_n$ depending only on $n$ such that for any monomial Chern classes of top degree, the Chern number ratios \begin{equation*} \left|\frac{c_\lambda(X)}{c_1 (X) ^{n}}\right|\leq c_n, \end{equation*} which recovers a recent result of Du-Sun. The main result also yields an asymptotic version of the sharper Riemann-Roch type inequality. Furthermore, using similar method we also obtain inequalities for Chern classes of the logarithmic tangent bundle.
Principal spectral theory and asymptotic behavior of the spectral bound for partially degenerate nonlocal dispersal systems

The purpose of this paper is to investigate the principal spectral theory and asymptotic behavior of the spectral bound for cooperative nonlocal dispersal systems, specifically focusing on the case where partial diffusion coefficients are zero, referred to as the partially degenerate case. We propose two sufficient conditions that ensure the existence of the principal eigenvalue in these partially degenerate systems. Additionally, we study the asymptotic behavior of the spectral bound for nonlocal dispersal operators with small and large diffusion coefficients, considering both non-degenerate and partially degenerate cases. Notably, we find a threshold-type result as the diffusion coefficients tend towards infinity in the partially degenerate case. Finally, we apply these findings to discuss the asymptotic behavior of the basic reproduction ratio in a viral diffusion model.
The effect of model freshwater flux biases on the multi-stable regime of the AMOC

It is known that global climate models (GCMs) have substantial biases in the surface freshwater flux which forces the ocean component of these models. Using numerical bifurcation analyses on a global ocean model, we study here the effect of a specific freshwater flux bias on the multiple equilibrium regime of the Atlantic Meridional Overturning Circulation (AMOC). We find that a (positive) freshwater flux bias over the Indian Ocean shifts the multiple equilibrium regime to larger values of North Atlantic freshwater input but hardly affects the associated hysteresis width. The magnitude of this shift depends on the way the anomalous North Atlantic freshwater flux is compensated. We explain the changes in bifurcation diagrams using the freshwater balance over the Atlantic basin. The results suggest that state-of-the-art GCMs may have an AMOC multiple equilibrium regime, but that it is located in a parameter regime that is considered unrealistic and hence is not explored.
MARC: Multipolicy and Risk-Aware Contingency Planning for Autonomous Driving

Generating safe and non-conservative behaviors in dense, dynamic environments remains challenging for automated vehicles due to the stochastic nature of traffic participants' behaviors and their implicit interaction with the ego vehicle. This letter presents a novel planning framework, Multipolicy And Risk-aware Contingency (MARC) planning, that systematically addresses these challenges by enhancing the multipolicy-based pipelines from both behavior and motion planning aspects. Specifically, MARC realizes a critical scenario set that reflects multiple possible futures conditioned on each semantic-level ego policy. Then, the generated policy-conditioned scenarios are further formulated into a tree-structured representation with a dynamic branchpoint based on the scene-level divergence. Moreover, to generate diverse driving maneuvers, we introduce risk-aware contingency planning, a bi-level optimization algorithm that simultaneously considers multiple future scenarios and user-defined risk tolerance levels. Owing to the more unified combination of behavior and motion planning layers, our framework achieves efficient decision-making and human-like driving maneuvers. Comprehensive experimental results demonstrate superior performance to other strong baselines in various environments.
Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction models to gain a competitive edge. However, existing literature mostly focuses on historical sales data and ignores the vital influence of macroeconomic conditions on consumer spending behavior. In this study, we bridge this gap by enriching time series data of customer demand with macroeconomic variables, such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and unemployment rates. Leveraging this comprehensive dataset, we develop and compare various regression and machine learning models to predict retail demand accurately.
First passage percolation on Erd\H{o}s-R\'{e}nyi graphs with general weights

We consider first passage percolation on the Erd\H{o}s-R\'{e}nyi graph with $n$ vertices in which each pair of distinct vertices is connected independently by an edge with probability $\lambda/n$ for some $\lambda>1$. The edges of the graph are given non-negative i.i. d. weights with a non-degenerate distribution such that the probability of zero is not too large. We consider the paths with small total weight between two distinct typical vertices and analyse the joint behaviour of the numbers of edges on such paths, the so-called hopcounts, and the total weights of these paths. For $n\to\infty$, we show that, after a suitable transformation, the pairs of hopcounts and total weights of these paths converge in distribution to a Cox process, i.e., a Poisson process with a random intensity measure. The random intensity measure is controlled by two independent random variables, whose distribution is the solution of a distributional fixed point equation and is related to branching processes. For non-arithmetic and arithmetic edge weight distributions we observe different behaviour. In particular, we derive the limiting distribution for the minimal total weight and the corresponding hopcount(s). Our results generalise earlier work of Bhamidi, van der Hofstad and Hooghiemstra, who assume edge weights are exponentially distributed. The main tool we employ is the method of moments.
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding

Graph transformers need strong inductive biases to derive meaningful attention scores. Yet, current methods often fall short in capturing longer ranges, hierarchical structures, or community structures, which are common in various graphs such as molecules, social networks, and citation networks. This paper presents a Hierarchical Distance Structural Encoding (HDSE) method to model node distances in a graph, focusing on its multi-level, hierarchical nature. We introduce a novel framework to seamlessly integrate HDSE into the attention mechanism of existing graph transformers, allowing for simultaneous application with other positional encodings. To apply graph transformer with HDSE to large-scale graphs, we further propose a hierarchical global attention mechanism with linear complexity. We theoretically prove the superiority of HDSE over shortest path distances in terms of expressivity and generalization. Empirically, we demonstrate that graph transformers with HDSE excel in graph classification, regression on 7 graph-level datasets, and node classification on 12 large-scale graphs, including those with up to a billion nodes.
Dual-Balancing for Multi-Task Learning

Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields. However, task balancing problem remains a significant challenge in MTL, with the disparity in loss/gradient scales often leading to performance compromises. In this paper, we propose a Dual-Balancing Multi-Task Learning (DB-MTL) method to alleviate the task balancing problem from both loss and gradient perspectives. Specifically, DB-MTL ensures loss-scale balancing by performing a logarithm transformation on each task loss, and guarantees gradient-magnitude balancing via normalizing all task gradients to the same magnitude as the maximum gradient norm. Extensive experiments conducted on several benchmark datasets consistently demonstrate the state-of-the-art performance of DB-MTL.
Identifying depression-related topics in smartphone-collected free-response speech tasks using an automatic speech recognition system and a deep learning topic model

Language use has been shown to correlate with depression, however, large-scale validation is still needed. Conventional clinic studies collecting spontaneous speech and written text samples is expensive and time-consuming. To address this challenge, many studies have applied natural language processing (NLP) to social media posts to predict depression, but limitations remain, including lack of clinically-validated depression labels, biased sampling of social media users, and no contextual information. This study aimed to identify topics associated with depression severity from smartphone-collected speech recordings and
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No

Out-of-distribution (OOD) detection refers to training the model on an in-distribution (ID) dataset to classify whether the input images come from unknown classes. Considerable effort has been invested in designing various OOD detection methods based on either convolutional neural networks or transformers. However, zero-shot OOD detection methods driven by CLIP, which only require class names for ID, have received less attention. This paper presents a novel method, namely CLIP saying "no" (CLIPN), which empowers the logic of saying "no" within CLIP. Our key motivation is to equip CLIP with the capability of distinguishing OOD and ID samples using positive-semantic prompts and negation-semantic prompts. Specifically, we design a novel learnable "no" prompt and a "no" text encoder to capture negation semantics within images. Subsequently, we introduce two loss functions: the image-text binary-opposite loss and the text semantic-opposite loss, which we use to teach CLIPN to associate images with "no" prompts, thereby enabling it to identify unknown samples. Furthermore, we propose two threshold-free inference algorithms to perform OOD detection by utilizing negation semantics from "no" prompts and the text encoder. Experimental results on 9 benchmark datasets (3 ID datasets and 6 OOD datasets) for the OOD detection task demonstrate that CLIPN, based on ViT-B-16, outperforms 7 well-used algorithms by at least 2.34% and 11.64% in terms of AUROC and FPR95 for zero-shot OOD detection on ImageNet-1K. Our CLIPN can serve as a solid foundation for effectively leveraging CLIP in downstream OOD tasks. The code is available on https://github.com/xmed-lab/CLIPN.
Evaluation of Faithfulness Using the Longest Supported Subsequence

As increasingly sophisticated language models emerge, their trustworthiness becomes a pivotal issue, especially in tasks such as summarization and question-answering. Ensuring their responses are contextually grounded and faithful is challenging due to the linguistic diversity and the myriad of possible answers. In this paper, we introduce a novel approach to evaluate faithfulness of machine-generated text by computing the longest noncontinuous substring of the claim that is supported by the context, which we refer to as the Longest Supported Subsequence (LSS). Using a new human-annotated dataset, we finetune a model to generate LSS. We introduce a new method of evaluation and demonstrate that these metrics correlate better with human ratings when LSS is employed, as opposed to when it is not. Our proposed metric demonstrates an 18% enhancement over the prevailing state-of-the-art metric for faithfulness on our dataset. Our metric consistently outperforms other metrics on a summarization dataset across six different models. Finally, we compare several popular Large Language Models (LLMs) for faithfulness using this metric. We release the human-annotated dataset built for predicting LSS and our fine-tuned model for evaluating faithfulness.
Interim summary of Sandia creep experiments on rock salt from the WIPP study area, southeastern New Mexico

Triaxial creep experiments on rock salt were carried out in support of the proposed Waste Isolation Pilot Plant (WIPP) in southeastern New Mexico. Results include digital data in condensed data files and numerous graphical comparisons for rock salt from two horizons 2000 to 2100 ft and 2600 to 2800 ft (625 to 650 m; 810 to 875 m) as a function of principal stress difference, temperature and confining pressure. Qualitative examinations showed that the rock salt exhibited all known stages of creep, i.e., primary, secondary and tertiary creep followed by creep fracture. Preliminary evaluations of axial strain time records during primary creep (epsilon/sub 1/, t) are discussed assuming the applicability of so-called power law creep but without implying that the conventional power relationship is a constitutive equation. The primary creep data of this study appear to be independent of pressure within fairly wide experimental scatter. Estimates of steady state creep rates of interest to the WIPP vary from epsilon/sub 1/ < 10/sup -10/ s/sup -1/ to approximately 10/sup -8/ s/sup -1/. Tertiary creep and creep fracture were observed at ambient temperature and at 100/sup 0/C. Creep fracture was not observed in a limited number of experiments at 200/sup 0/C andmore » is deemed unlikely except, possibly, at very low confining pressure, sigma/sub 3/ less than or equal to 100 psi (0.7 MPa). To predict creep fracture, the use of an empirical procedure was suggested which correlates permanent time-dependent strains (damage) with limiting damage which is established from complete quasi-static stress-strain curves including pre- and post-failure records. 19 figures. « less
Learning to Learn Financial Networks for Optimising Momentum Strategies

Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns. However, the current process of constructing financial networks relies heavily on expensive databases and financial expertise, limiting accessibility for small-sized and academic institutions. Furthermore, the traditional approach treats network construction and portfolio optimisation as separate tasks, potentially hindering optimal portfolio performance. To address these challenges, we propose L2GMOM, an end-to-end machine learning framework that simultaneously learns financial networks and optimises trading signals for network momentum strategies. The model of L2GMOM is a neural network with a highly interpretable forward propagation architecture, which is derived from algorithm unrolling. The L2GMOM is flexible and can be trained with diverse loss functions for portfolio performance, e.g. the negative Sharpe ratio. Backtesting on 64 continuous future contracts demonstrates a significant improvement in portfolio profitability and risk control, with a Sharpe ratio of 1.74 across a 20-year period.
AREA DISTRIBUTION DYNAMICS OF CASTANEA SATIVA FORESTS ON THE NORTHERN SLOPES OF BELASITSA MOUNTAIN

In order to study the temporal dynamics of the area covered by sweet chestnut forests on the northern slopes of Bulgarian part of Belasitsa mountain, a field verification of the first detailed forest inventory (since1964) was performed. All chestnut co-dominated and dominated forests (CCDF) were grouped into two categories (i) chestnut co-dominated forests (CCF) where chestnut basal area represented 5.5% to 55.5% of the total basal area and (ii) chestnut dominated forests (CDF) with chestnut basal area from 55.5% to 100% of the total basal area. CCDF have lost nearly 13% of their initial area during the studied period (from 1923 ha in 1964 to 1678 ha in 2009). CCF have increased their area from 604 ha to 1030 ha while the area of CDF has decreased from 1319 ha to 648 ha. Therefore, chestnut has lost its dominating role in a half of the initial area of 1319 ha where it used to be a dominant tree species 45 years ago and has became a co-dominant species. The distribution peak of CCDF have shifted with 100 m upwards (from 500 a. s. l. in 1964 to 600 m a.s.l. in 2009), CDF tending to decrease their area regardless the altitude.