Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrastive Conditional Latent Diffusion for Audio-visual Segmentation

We propose a latent diffusion model with contrastive learning for audio-visual segmentation (AVS) to extensively explore the contribution of audio. We interpret AVS as a conditional generation task, where audio is defined as the conditional variable for sound producer(s) segmentation. With our new interpretation, it is especially necessary to model the correlation between audio and the final segmentation map to ensure its contribution. We introduce a latent diffusion model to our framework to achieve semantic-correlated representation learning. Specifically, our diffusion model learns the conditional generation process of the ground-truth segmentation map, leading to ground-truth aware inference when we perform the denoising process at the test stage. As a conditional diffusion model, we argue it is essential to ensure that the conditional variable contributes to model output. We then introduce contrastive learning to our framework to learn audio-visual correspondence, which is proven consistent with maximizing the mutual information between model prediction and the audio data. In this way, our latent diffusion model via contrastive learning explicitly maximizes the contribution of audio for AVS. Experimental results on the benchmark dataset verify the effectiveness of our solution. Code and results are online via our project page: https://github.com/OpenNLPLab/DiffusionAVS.
On higher multiplicity hyperplane and polynomial covers for symmetry preserving subsets of the hypercube

Alon and F\"uredi (European J. Combin. 1993) gave a tight bound for the following hyperplane covering problem: find the minimum number of hyperplanes required to cover all points of the n-dimensional hypercube {0,1}^n except the origin. Their proof is among the early instances of the polynomial method, which considers a natural polynomial (a product of linear factors) associated to the hyperplane arrangement, and gives a lower bound on its degree, whilst being oblivious to the (product) structure of the polynomial. Thus, their proof gives a lower bound for a weaker polynomial covering problem, and it turns out that this bound is tight for the stronger hyperplane covering problem. In a similar vein, solutions to some other hyperplane covering problems were obtained, via solutions of corresponding weaker polynomial covering problems, in some special cases in the works of the fourth author (Electron. J. Combin. 2022), and the first three authors (Discrete Math. 2023). In this work, we build on these and solve a hyperplane covering problem for general symmetric sets of the hypercube, where we consider hyperplane covers with higher multiplicities. We see that even in this generality, it is enough to solve the corresponding polynomial covering problem. Further, this seems to be the limit of this approach as far as covering symmetry preserving subsets of the hypercube is concerned. We gather evidence for this by considering the class of blockwise symmetric sets of the hypercube (which is a strictly larger class than symmetric sets), and note that the same proof technique seems to only solve the polynomial covering problem.
Nanocomposite volume holographic gratings incorporated with ultrahigh refractive index hyperbranched polymer for diffractive optical elements

We report on the characterization of volume holographic gratings recorded in photopolymerizable nanocomposites incorporated with hyperbranched polymer (HBP) acting as transporting organic nanoparticles. The HBP possesses ultrahigh index of refraction owing to the inclusion of triazine and aromatic ring units. Such an HBP was easy to disperse into acrylate monomer without any aggregation, so that films with good optical quality are available. A transmission volume grating with refractive index changes up to 2.3×10-2 was obtained at 27 vol.% HBP concentration and 7 vol.% multifunctional acrylate monomer at a wavelength of 532 nm.
AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?

Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after his/her current action (e.g. crack eggs)? What if we also know the longer-term goal of the actor (e.g. making egg fried rice)? The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of verb and noun sequences, and it is crucial for human-machine interaction. We propose to formulate the LTA task from two perspectives: a bottom-up approach that predicts the next actions autoregressively by modeling temporal dynamics; and a top-down approach that infers the goal of the actor and plans the needed procedure to accomplish the goal. We hypothesize that large language models (LLMs), which have been pretrained on procedure text data (e.g. recipes, how-tos), have the potential to help LTA from both perspectives. It can help provide the prior knowledge on the possible next actions, and infer the goal given the observed part of a procedure, respectively. To leverage the LLMs, we propose a two-stage framework, AntGPT. It first recognizes the actions already performed in the observed videos and then asks an LLM to predict the future actions via conditioned generation, or to infer the goal and plan the whole procedure by chain-of-thought prompting. Empirical results on the Ego4D LTA v1 and v2 benchmarks, EPIC-Kitchens-55, as well as EGTEA GAZE+ demonstrate the effectiveness of our proposed approach. AntGPT achieves state-of-the-art performance on all above benchmarks, and can successfully infer the goal and thus perform goal-conditioned"counterfactual"prediction via qualitative analysis. Code and model will be released at https://brown-palm.github. io/AntGPT
The Hydra Effect: Emergent Self-repair in Language Model Computations

We investigate the internal structure of language model computations using causal analysis and demonstrate two motifs: (1) a form of adaptive computation where ablations of one attention layer of a language model cause another layer to compensate (which we term the Hydra effect) and (2) a counterbalancing function of late MLP layers that act to downregulate the maximum-likelihood token. Our ablation studies demonstrate that language model layers are typically relatively loosely coupled (ablations to one layer only affect a small number of downstream layers). Surprisingly, these effects occur even in language models trained without any form of dropout. We analyse these effects in the context of factual recall and consider their implications for circuit-level attribution in language models.
Rings whose Nil-Clean and Clean Elements are Uniquely Nil-Clean

We consider and study those rings in which each nil-clean or clean element is uniquely nil-clean. We establish that, for abelian rings, these rings have a satisfactory description and even it is shown that the classes of abelian rings and the rings in which nil-clean elements are uniquely nil-clean do coincide. Moreover, we prove that the rings in which clean elements are uniquely nil-clean coincide with the subclass of abelian rings consisting of only unipotent units and, in particular, that in the semipotent case we have a complete characterization only in terms of the former ring and its divisions. Likewise, some extension properties and group rings for such kinds of rings are also considered.
LP-MusicCaps: LLM-Based Pseudo Music Captioning

Automatic music captioning, which generates natural language descriptions for given music tracks, holds significant potential for enhancing the understanding and organization of large volumes of musical data. Despite its importance, researchers face challenges due to the costly and time-consuming collection process of existing music-language datasets, which are limited in size. To address this data scarcity issue, we propose the use of large language models (LLMs) to artificially generate the description sentences from large-scale tag datasets. This results in approximately 2.2M captions paired with 0.5M audio clips. We term it Large Language Model based Pseudo music caption dataset, shortly, LP-MusicCaps. We conduct a systemic evaluation of the large-scale music captioning dataset with various quantitative evaluation metrics used in the field of natural language processing as well as human evaluation. In addition, we trained a transformer-based music captioning model with the dataset and evaluated it under zero-shot and transfer-learning settings. The results demonstrate that our proposed approach outperforms the supervised baseline model.
Solution to the Uniformly Fully Inert Subgroups Problem for Abelian Groups

A famous conjecture attributed to Dardano-Dikranjan-Rinauro-Salce states that any uniformly fully inert subgroup of a given group is commensurable with a fully invariant subgroup (see, respectively, [5] and [6]). In this short note, we completely settle this problem in the affirmative for an arbitrary Abelian group.
Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap system

Meetings play a critical infrastructural role in the coordination of work. In recent years, due to shift to hybrid and remote work, more meetings are moving to online Computer Mediated Spaces. This has led to new problems (e.g. more time spent in less engaging meetings) and new opportunities (e.g. automated transcription/captioning and recap support). Recent advances in large language models (LLMs) for dialog summarization have the potential to improve the experience of meetings by reducing individuals' meeting load and increasing the clarity and alignment of meeting outputs. Despite this potential, they face technological limitation due to long transcripts and inability to capture diverse recap needs based on user's context. To address these gaps, we design, implement and evaluate in-context a meeting recap system. We first conceptualize two salient recap representations -- important highlights, and a structured, hierarchical minutes view. We develop a system to operationalize the representations with dialogue summarization as its building blocks. Finally, we evaluate the effectiveness of the system with seven users in the context of their work meetings. Our findings show promise in using LLM-based dialogue summarization for meeting recap and the need for both representations in different contexts. However, we find that LLM-based recap still lacks an understanding of whats personally relevant to participants, can miss important details, and mis-attributions can be detrimental to group dynamics. We identify collaboration opportunities such as a shared recap document that a high quality recap enables. We report on implications for designing AI systems to partner with users to learn and improve from natural interactions to overcome the limitations related to personal relevance and summarization quality.
STL: A Signed and Truncated Logarithm Activation Function for Neural Networks

Activation functions play an essential role in neural networks. They provide the non-linearity for the networks. Therefore, their properties are important for neural networks' accuracy and running performance. In this paper, we present a novel signed and truncated logarithm function as activation function. The proposed activation function has significantly better mathematical properties, such as being odd function, monotone, differentiable, having unbounded value range, and a continuous nonzero gradient. These properties make it an excellent choice as an activation function. We compare it with other well-known activation functions in several well-known neural networks. The results confirm that it is the state-of-the-art. The suggested activation function can be applied in a large range of neural networks where activation functions are necessary.
A new Gradient TD Algorithm with only One Step-size: Convergence Rate Analysis using L-λ Smoothness

Gradient Temporal Difference (GTD) algorithms (Sutton et al., 2008, 2009) are the first $O(d)$ ($d$ is the number features) algorithms that have convergence guarantees for off-policy learning with linear function approximation. Liu et al. (2015) and Dalal et. al. (2018) proved the convergence rates of GTD, GTD2 and TDC are $O(t^{-\alpha/2})$ for some $\alpha \in (0,1)$. This bound is tight (Dalal et al., 2020), and slower than $O(1/\sqrt{t})$. GTD algorithms also have two step-size parameters, which are difficult to tune. In literature, there is a"single-time-scale"formulation of GTD. However, this formulation still has two step-size parameters. This paper presents a truly single-time-scale GTD algorithm for minimizing the Norm of Expected td Update (NEU) objective, and it has only one step-size parameter. We prove that the new algorithm, called Impression GTD, converges at least as fast as $O(1/t)$. Furthermore, based on a generalization of the expected smoothness (Gower et al. 2019), called $L$-$\lambda$ smoothness, we are able to prove that the new GTD converges even faster, in fact, with a linear rate. Our rate actually also improves Gower et al.'s result with a tighter bound under a weaker assumption. Besides Impression GTD, we also prove the rates of three other GTD algorithms, one by Yao and Liu (2008), another called A-transpose-TD (Sutton et al., 2008), and a counterpart of A-transpose-TD. The convergence rates of all the four GTD algorithms are proved in a single generic GTD framework to which $L$-$\lambda$ smoothness applies. Empirical results on Random walks, Boyan chain, and Baird counterexample show that Impression GTD converges much faster than existing GTD algorithms for both on-policy and off-policy learning problems, with well-performing step-sizes in a big range.
Laplacian Immanantal Polynomials of a Bipartite Graph and Graph Shift Operation

Let $G$ be a bipartite graph on $n$ vertices with the Laplacian matrix $L_G$. When $G$ is a tree, inequalities involving coefficients of immanantal polynomials of $L_G$ are known as we go up $GTS_n$ poset of unlabelled trees with $n$ vertices. We extend $GTS$ operation on a tree to an arbitrary graph, we call it generalized graph shift (hencefourth $GGS$) operation. Using $GGS$ operation, we generalize these known inequalities associated with trees to bipartite graphs. Using vertex orientations of $G$, we give a combinatorial interpretation for each coefficient of the Laplacian immanantal polynomial of $G$ which is used to prove counter parts of Schur theorem and Lieb's conjecture for these coefficients. We define $GGS_n$ poset on $\Omega_{C_k}^v(n)$, the set of unlabelled unicyclic graphs with $n$ vertices where each vertex of the cycle $C_k$ has degree $2$ except one vertex $v$. Using $GGS_n$ poset on $\Omega_{C_{2k}}^v(n)$, we solves an extreme value problem of finding the max-min pair in $\Omega_{C_{2k}}^v(n)$ for each coefficient of the generalized Laplacian polynomials. At the end of this paper, we also discuss the monotonicity of the spectral radius and the Wiener index of an unicyclic graph when we go up along $GGS_n$ poset of $\Omega_{C_k}^v(n)$.
Analytic Hall magnetohydrodynamics toroidal equilibria via the energy-Casimir variational principle

Equilibrium equations for magnetically confined, axisymmetric plasmas are derived by means of the energy-Casimir variational principle in the context of Hall magnetohydrodynamics (MHD). This approach stems from the noncanonical Hamiltonian structure of Hall MHD, the simplest, quasineutral two-fluid model that incorporates contributions due to ion Hall drifts. The axisymmetric Casimir invariants are used, along with the Hamiltonian functional to apply the energy-Casimir variational principle for axisymmetric two-fluid plasmas with incompressible ion flows. This results in a system of equations of the Grad–Shafranov–Bernoulli (GSB) type with four free functions. Two families of analytic solutions to the GSB system are then calculated, based on specific choices for the free functions. These solutions are subsequently applied to Tokamak-relevant configurations using proper boundary shaping methods. The Hall MHD model predicts a departure of the ion velocity surfaces from the magnetic surfaces which are frozen in the electron fluid. This separation of the characteristic surfaces is corroborated by the analytic solutions calculated in this study. The equilibria constructed by these solutions exhibit favorable characteristics for plasma confinement, for example they possess closed and nested magnetic and flow surfaces with pressure profiles peaked at the plasma core. The relevance of these solutions to laboratory and astrophysical plasmas is finally discussed, with particular focus on systems that involve length scales on the order of the ion skin depth.
MSStyleTTS: Multi-Scale Style Modeling With Hierarchical Context Information for Expressive Speech Synthesis

Expressive speech synthesis is crucial for many human-computer interaction scenarios, such as audiobooks, podcasts, and voice assistants. Previous works focus on predicting the style embeddings at one single scale from the information within the current sentence. Whereas, context information in neighboring sentences and multi-scale nature of style in human speech are neglected, making it challenging to convert multi-sentence text into natural and expressive speech. In this article, we propose MSStyleTTS, a style modeling method for expressive speech synthesis, to capture and predict styles at different levels from a wider range of context rather than a sentence. Two sub-modules, including multi-scale style extractor and multi-scale style predictor, are trained together with a FastSpeech 2 based acoustic model. The predictor is designed to explore the hierarchical context information by considering structural relationships in context and predict style embeddings at global-level, sentence-level and subword-level. The extractor extracts multi-scale style embedding from the ground-truth speech and explicitly guides the style prediction. Evaluations on both in-domain and out-of-domain audiobook datasets demonstrate that the proposed method significantly outperforms the three baselines. In addition, we conduct the analysis of the context information and multi-scale style representations that have never been discussed before.
Association Between Age and Low-Density Lipoprotein Cholesterol Response to Statins

BACKGROUND There is large patient-to-patient variability in the low-density lipoprotein cholesterol (LDL-C) response to statin treatment. The reduction in LDL-C may depend on the age of the patient treated-particularly in older adults, who have been substantially underrepresented in randomized controlled trials. OBJECTIVE To investigate the association between age and the LDL-C reduction by statins. DESIGN Nationwide, register-based cohort study. SETTING Denmark, 2008 to 2018. PARTICIPANTS 82 958 simvastatin or atorvastatin initiators with LDL-C measurements before and during statin use. MEASUREMENTS Statin response, defined as percentage reduction in prestatin LDL-C level, and percentage reduction differences (PRDs) according to age and simvastatin or atorvastatin dose based on a longitudinal model for LDL-C. RESULTS Among 82 958 statin initiators, 10 388 (13%) were aged 75 years or older. With low- to moderate-intensity statins, initiators aged 75 years or older had greater mean LDL-C percentage reductions than initiators younger than 50 years-for example, 39.0% versus 33.8% for simvastatin, 20 mg, and 44.2% versus 40.2% for atorvastatin, 20 mg. The adjusted PRD for initiators aged 75 years compared with initiators aged 50 years was 2.62 percentage points. This association was consistent for primary prevention (2.54 percentage points) and secondary prevention (2.32 percentage points) but smaller for initiators of high-intensity statins (atorvastatin, 40 mg: 1.36 percentage points; atorvastatin, 80 mg: -0.58 percentage point). LIMITATION Use of administrative data, observational pre-post comparison with a moderately high proportion of missing data, lack of information on body mass index, and the mainly White study population may limit generalizability. CONCLUSION Low- to moderate-intensity statins were associated with a greater reduction in LDL-C levels in older persons than younger persons and may be more appealing as initial treatment in older adults who are at increased risk for adverse events. PRIMARY FUNDING SOURCE The Independent Research Fund Denmark, Brødrene Hartmanns Fond, and Fonden til Lægevidenskabens Fremme.
How group structure impacts the numbers at risk for coronary artery disease: polygenic risk scores and non-genetic risk factors in the UK Biobank cohort

The UK Biobank is a large cohort study that recruited over 500,000 British participants aged 40-69 in 2006-2010 at 22 assessment centres from across the UK. Self-reported health outcomes and hospital admission data are two types of records that include participants' disease status. Coronary artery disease (CAD) is the most common cause of death in the UK Biobank cohort. After distinguishing between prevalence and incidence CAD events for all UK Biobank participants, we identified geographical variations in age-standardised rates of CAD between assessment centres. Significant distributional differences were found between the pooled cohort equation scores of UK Biobank participants from England and Scotland using the Mann-Whitney test. Polygenic risk scores of UK Biobank participants from England and Scotland and from different assessment centres differed significantly using permutation tests. Our aim was to discriminate between assessment centres with different disease rates by collecting data on disease-related risk factors. However, relying solely on individual-level predictions and averaging them to obtain group-level predictions proved ineffective, particularly due to the presence of correlated covariates resulting from participation bias. By using the Mundlak model, which estimates a random effects regression by including the group means of the independent variables in the model, we effectively addressed these issues. In addition, we designed a simulation experiment to demonstrate the functionality of the Mundlak model. Our findings have applications in public health funding and strategy, as our approach can be used to predict case rates in the future, as both population structure and lifestyle changes are uncertain.