Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictable MDP Abstraction for Unsupervised Model-Based RL

A key component of model-based reinforcement learning (RL) is a dynamics model that predicts the outcomes of actions. Errors in this predictive model can degrade the performance of model-based controllers, and complex Markov decision processes (MDPs) can present exceptionally difficult prediction problems. To mitigate this issue, we propose predictable MDP abstraction (PMA): instead of training a predictive model on the original MDP, we train a model on a transformed MDP with a learned action space that only permits predictable, easy-to-model actions, while covering the original state-action space as much as possible. As a result, model learning becomes easier and more accurate, which allows robust, stable model-based planning or model-based RL. This transformation is learned in an unsupervised manner, before any task is specified by the user. Downstream tasks can then be solved with model-based control in a zero-shot fashion, without additional environment interactions. We theoretically analyze PMA and empirically demonstrate that PMA leads to significant improvements over prior unsupervised model-based RL approaches in a range of benchmark environments. Our code and videos are available at https://seohong.me/projects/pma/
On the non-linear stability of the Cosmological region of the Schwarzschild-de Sitter spacetime

The non-linear stability of the sub-extremal Schwarzschild-de Sitter spacetime in the stationary region near the conformal boundary is analysed using a technique based on the extended conformal Einstein field equations and a conformal Gaussian gauge. This strategy relies on the observation that the Cosmological stationary region of this exact solution can be covered by a non-intersecting congruence of conformal geodesics. Thus, the future domain of dependence of suitable spacelike hypersurfaces in the Cosmological region of the spacetime can be expressed in terms of a conformal Gaussian gauge. A perturbative argument then allows to prove existence and stability results close to the conformal boundary and away from the asymptotic points where the Cosmological horizon intersects the conformal boundary. In particular, we show that small enough perturbations of initial data for the sub-extremal Schwarzschild-de Sitter spacetime give rise to a solution to the Einstein field equations which is regular at the conformal boundary. The analysis in this article can be regarded as a first step towards a stability argument for perturbation data on the Cosmological horizons.
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach

The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, where the goal is to infer a global posterior from partitioned client data (Al-Shedivat et al., 2021). This paper extends the inference view and describes a variational inference formulation of federated learning where the goal is to find a global variational posterior that well-approximates the true posterior. This naturally motivates an expectation propagation approach to federated learning (FedEP), where approximations to the global posterior are iteratively refined through probabilistic message-passing between the central server and the clients. We conduct an extensive empirical study across various algorithmic considerations and describe practical strategies for scaling up expectation propagation to the modern federated setting. We apply FedEP on standard federated learning benchmarks and find that it outperforms strong baselines in terms of both convergence speed and accuracy.
Finiteness of Local Cohomology Modules Defined by a Pair of Ideals

Let R be a Noetherian ring and I, J two ideals of R. Let M be a ZD-module such that Hom R (R/I, M/L) is finitely generated for any submodule L of M. It is shown that if t is an integer such that is minimax for all i < t, then is finitely generated. Let M be a finitely generated R-module such that dim R M = n. It is proved that (1) has finite length; (2) if R is local with dim R/I + J = 0 and dim R M/JM = d > 0, then is not finitely generated.
Rotation curve of the Milky Way and the mass of Sagittarius A*

In the present work we derive an analytical expression for the mass density of an object with spherical symmetry, whose corresponding potential allows obtaining a circular velocity around it that is in agreement with the observed rotation curve of galaxies. The rotation curve of our galaxy is analyzed, determining the properties of the central object, its radius and mass whose value obtained is very close to that reported in the literature.
Graph Representation Learning via Aggregation Enhancement

Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and propagating information between layers, which limits their performance. We tackle this problem with the kernel regression (KR) approach, using KR loss as the primary loss in self-supervised settings or as a regularization term in supervised settings. We show substantial performance improvements compared to state-of-the-art in both scenarios on multiple transductive and inductive node classification datasets, especially for deep networks. As opposed to mutual information (MI), KR loss is convex and easy to estimate in high-dimensional cases, even though it indirectly maximizes the MI between its inputs. Our work highlights the potential of KR to advance the field of graph representation learning and enhance the performance of GNNs. The code to reproduce our experiments is available at https://github.com/Anonymous1252022/KR_for_GNNs
Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several types such as genomic, transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block-wise missing multi-omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi-omics data sets, we compare the predictive performances of several of these approaches for different block-wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models

We introduce a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM). These models realize generative trajectories for $N$ dimensional data by embedding paths in $N{+}D$ dimensional space while still controlling the progression with a simple scalar norm of the $D$ additional variables. The new models reduce to PFGM when $D{=}1$ and to diffusion models when $D{\to}\infty$. The flexibility of choosing $D$ allows us to trade off robustness against rigidity as increasing $D$ results in more concentrated coupling between the data and the additional variable norms. We dispense with the biased large batch field targets used in PFGM and instead provide an unbiased perturbation-based objective similar to diffusion models. To explore different choices of $D$, we provide a direct alignment method for transferring well-tuned hyperparameters from diffusion models ($D{\to} \infty$) to any finite $D$ values. Our experiments show that models with finite $D$ can be superior to previous state-of-the-art diffusion models on CIFAR-10/FFHQ $64{\times}64$ datasets, with FID scores of $1.91/2.43$ when $D{=}2048/128$. In class-conditional setting, $D{=}2048$ yields current state-of-the-art FID of $1.74$ on CIFAR-10. In addition, we demonstrate that models with smaller $D$ exhibit improved robustness against modeling errors. Code is available at https://github.com/Newbeeer/pfgmpp
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks. We find that it is better at understanding non-Latin script languages than generating them. It is able to generate multimodal content from textual prompts, via an intermediate code generation step. Moreover, we find that ChatGPT is 63.41% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning, hence making it an unreliable reasoner. It is, for example, better at deductive than inductive reasoning. ChatGPT suffers from hallucination problems like other LLMs and it generates more extrinsic hallucinations from its parametric memory as it does not have access to an external knowledge base. Finally, the interactive feature of ChatGPT enables human collaboration with the underlying LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++ on machine translation, in a multi-turn"prompt engineering"fashion. We also release codebase for evaluation set extraction.
Multimodal Recommender Systems: A Survey

The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short video, news and etc., understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, Multimodal Recommender System (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to three categories, i.e., Feature Interaction, Feature Enhancement and Model Optimization. To make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper.
Hilbert's tenth problem in Anticyclotomic towers of number fields

Let $K$ be an imaginary quadratic field and $p$ be an odd prime which splits in $K$. Let $E_1$ and $E_2$ be elliptic curves over $K$ such that the $Gal(\bar{K}/K)$-modules $E_1[p]$ and $E_2[p]$ are isomorphic. We show that under certain explicit additional conditions on $E_1$ and $E_2$, the anticyclotomic $\mathbb{Z}_p$-extension $K_{anti}$ of $K$ is integrally diophantine over $K$. When such conditions are satisfied, we deduce new cases of Hilbert's tenth problem. In greater detail, the conditions imply that Hilbert's tenth problem is unsolvable for all number fields that are contained in $K_{anti}$. We illustrate our results by constructing an explicit example for $p=3$ and $K=\mathbb{Q}(\sqrt{-5})$.
Hypersimple Rings and Modules

In this paper a simple right R-module S over a ring R is called hypersimple if its injective hull E(S) is cyclic, and a ring R is called right hypersimple if every simple right R-module is hypersimple. We initiate a study of these new notions, and revisit Osofsky's work on hypercyclic rings, i.e. rings whose cyclic right modules have cyclic injective hulls.
Scale-free localization and 
PT
 symmetry breaking from local non-Hermiticity

We show that a local non-Hermitian perturbation in a Hermitian lattice system generically induces scale-free localization for the continuous-spectrum eigenstates. When the perturbation lies at a finite distance to the boundary, the scale-free eigenstates are promoted to exponentially localized modes, whose number is proportional to the distance. Furthermore, when the local non-Hermitian perturbation respects parity-time (PT) symmetry, the PT symmetry breaking is always accompanied by the emergence of scale-free or exponential localization. Intriguingly, we find a concise band-structure condition, which tells not only when the continuous-spectrum PT breaking of scale-free modes can occur, but also the precise PT-breaking energy window. Our results uncover a series of unexpected generic phenomena induced by a local non-Hermitian perturbation, which has interesting interplay with PT symmetry.
Studies in elastic fracture mechanics based on the material force method

The object of this work is to discuss a further improvement of the material force method for non‐linear hyperelastostatic fracture mechanics. We investigate the accuracy of the material force method within a ‘modified boundary layer’‐formulation using a Ramberg–Osgood material type for the sake of comparison. The proposed improvement leads to a reliable and very accurate method to compute the vectorial J‐integral in fracture mechanics. Copyright © 2003 John Wiley & Sons, Ltd.
Bell-inequality in path-entangled single photon and purity of single photon state

Different degrees of freedom of single photons have been entangled and are used as a resource for various quantum technology applications. We present a simple scheme to perform Bell's test and show the violation of CHSH inequality in a path-entangled single photon state using interferometric and its equivalent non-interferometric approach in beam splitter setting. We demonstrate this experimentally by generating and controlling path-entangled state using both, heralded and un-heralded single photons from spontaneous parametric down-conversion. The experimental results we present show the transition to violation of CHSH inequality when the purity of single photons state visibility increase above 70\% , $\mathcal{P}>0.7$. Our procedure using single beam splitter and two detector module for un-heralded single photon source allows a simple way to test for purity of any single photon source and to study quantum correlations on systems driven by dynamics where single particle entanglement with position space is prominent.
(-1)-Enumerations of arrowed Gelfand-Tsetlin patterns

Arrowed Gelfand-Tsetlin patterns have recently been introduced to study alternating sign matrices. In this paper, we show that a $(-1)$-enumeration of arrowed Gelfand-Tsetlin patterns can be expressed by a simple product formula. The numbers are a one-parameter generalization of the numbers $2^{n(n-1)/2} \prod_{j=0}^{n-1} \frac{(4j+2)! }{(n+2j+1)! }$ that appear in recent work of Di Francesco. A second result concerns the (-1)-enumeration of arrowed Gelfand-Tsetlin patterns when excluding double-arrows as decoration in which case we also obtain a simple product formula. We are also able to provide signless interpretations of our results. The proofs of the enumeration formulas are based on a recent Littlewood-type identity, which allows us to reduce the problem to the evaluations of two determinants. The evaluations are accomplished by means of the LU-decompositions of the underlying matrices, and an extension of Sister Celine's algorithm as well as creative telescoping to evaluate certain triple sums. In particular, we use implementations of such algorithms by Koutschan, and by Wegschaider and Riese.
The Moderating Effect of Mental Health on the Relationship Between Cardiovascular Disease Awareness and Health Behaviors of Middle-Aged Korean Chinese Workers With Cardiovascular Risk Factors in Korea

Introduction: Cardiovascular disease (CVD) remains a leading cause of death in China and Korea, yet little is known about in Korean Chinese workers. The purpose of the study was to investigate the effects of CVD awareness and mental health on health behaviors and evaluate the moderating effect of mental health on the relationship between CVD awareness and health behaviors among Korean Chinese workers. Method: We used a cross-sectional design and convenience sampling of participants from Seoul in December 2021. Data were analyzed using descriptive statistics, Pearson’s correlation coefficient, independent t tests, and hierarchical multiple regression analysis. Results: CVD awareness, mental health, and health behaviors were relatively low and mental health showed a moderating effect on the relationship between the two variables. Discussion: Understanding relationships between CVD awareness, socioculturally mediated health behaviors, and mental health issues is critical to CVD prevention among participants; moreover, mental health should be specifically assessed and targeted for improvement.
How to overcome resistance to immune checkpoint inhibitors in colorectal cancer: From mechanisms to translation

Immunotherapy, especially with immune checkpoint inhibitors (ICIs), has shown advantages in cancer treatment and is a new hope for patients who have failed multiline therapy. However, in colorectal cancer (CRC), the benefit is limited to a small subset of patients with microsatellite instability‐high (MSI‐H) or mismatch repair‐deficient (dMMR) metastatic CRC (mCRC). In addition, 45% to 60% of dMMR/MSI‐H mCRC patients showed primary or acquired resistance to ICIs. This means that these patients may have potential unknown pathways mediating immune escape. Almost all mismatch repair‐proficient (pMMR) or microsatellite‐stable (MSS) mCRC patients do not benefit from ICIs. In this review, we discuss the mechanisms of action of ICIs and their current status in CRC. We then discuss the mechanisms of primary and acquired resistance to ICIs in CRC. Finally, we discuss promising therapeutic strategies to overcome resistance to ICIs in the clinic.
Effects of satisfying and violating expectations on serial dependence

Serial dependence refers to the phenomenon that observers tend to report stimuli as being more similar to previous stimuli than they really are (attractive dependence) or, in some cases, as more different than they really are (repulsive dependence). Numerous experiments have demonstrated serial dependence for a range of modalities and stimulus features, highlighting the role of bottom-up sensory interactions. However, comparatively less research has focused on how higher-level cognitive factors, such as expectations, might influence serial dependence. Here, we manipulated expectations by having observers respond to target luminance gratings that occurred at the end of a sequence of non-target gratings. The sequence either rotated predictably (inducing an expectation), varied randomly (inducing no expectation), or rotated predictably but had a random target orientation (violating expectations). We found that observers produced less errors and indicated less uncertainty in their estimations of expected stimuli but their responses were biased away from the penultimate stimulus in the sequence (repulsive dependence). In contrast, following random sequences, responses showed an attractive bias to the penultimate stimulus in the sequence. Unexpected targets showed a mixture of both biases, such that when targets happened (by chance) to appear as expected, responses were repulsed, but responses to target orientations that more clearly violated expectations were attracted. These results indicate that, whereas attraction to previous stimuli may be a default strategy employed in response to random and unexpected events, certain expectations can reverse the default bias into a repulsive one.
Hypoxemia and hypertension in obstructive sleep apnea: the forgotten variable

The apnea-hypopnea index (AHI), expressed as events/h, is used in order to define normality and to classify the severity of obstructive sleep apnea (OSA). (1) AHI measures the frequency of respiratory events throughout the night and the degree of sleep fragmentation, since apnea/hypopnea events are frequently followed by an electroencephalographic reaction. In addition, AHI gives us indirect information about hypoxemia, because respiratory events are often accompanied by a variable decrease in SpO2. (2) The proportion of time spent in SpO2 ≤ 90% (T90) is an accurate parameter to assess nighttime hypoxemia, since a 90% SpO2 at sea level equals a PaO2 of approximately 60 mmHg according to the oxygen-hemoglobin dissociation curve. (2) The correlation between AHI and T90 is moderate (r = 0.60.7), because not all respiratory events are followed by a drop in SpO2 ≤ 90%. OSA has been identified as a risk factor for hypertension in population-based studies. (3,4) Underlying mechanisms would be related to sympathetic activity secondary to hypoxia/hypercapnia cycles, increased intrathoracic pressure, and microarousals that follow apneas/hypopneas, which favor an increase in blood pressure. (4) Both animal and human studies have shown that intermittent hypoxemia can trigger hypertension. (4) Similarly to the hypothesis of other authors, ours was that T90 might be associated to hypertension in OSA patients.
The role of rice cultivation in changes in atmospheric methane concentration and the Global Methane Pledge

Resumption of the increase in atmospheric methane (CH4) concentrations since 2007 is of global concern and may partly have resulted from emissions from rice cultivation. Estimates of CH4 emissions from rice fields and abatement potential are essential to assess the contribution of improved rice management in achieving the targets of the Global Methane Pledge agreed upon by over 100 countries at COP26. However, the contribution of CH4 emissions from rice fields to the resumed CH4 growth and the global abatement potential remains unclear. In this study, we estimated the global CH4 emissions from rice fields to be 27 ± 6 Tg CH4 year−1 in the recent decade (2008–2017) based on the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The trend of CH4 emissions from rice cultivation showed an increase followed by no significant change and then, a stabilization over 1990–2020. Consequently, the contribution of CH4 emissions from rice fields to the renewed increase in atmospheric CH4 concentrations since 2007 was minor. We summarized the existing low‐cost measures and showed that improved water and straw management could reduce one‐third of global CH4 emissions from rice fields. Straw returned as biochar could reduce CH4 emissions by 12 Tg CH4 year−1, equivalent to 10% of the total reduction of all anthropogenic emissions. We conclude that other sectors than rice cultivation must have contributed to the renewed increase in atmospheric CH4 concentrations, and that optimizing multiple mitigation measures in rice fields could contribute significantly to the abatement goal outlined in the Global Methane Pledge.