Miracle-dz/UniFAR-FTUs huggingface dataset:
A uniﬁed model for the human response to endotoxin challenges

This study develops a uniﬁed model predicting the whole-body response to endotoxin. We simulate dynamics using di ﬀ erential equations examining the response to a lipopolysaccharide (LPS) injection. The model tracks pro- and anti-inﬂammatory cytokines (TNF- U , IL-6, IL-10), concentrations of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and cortisol in the hypothalamic-pituitary-adrenal (HPA) axis. Daily hormonal variations are integrated into the model by including circadian oscillations when tracking CRH. Ad-ditionally, the model tracks heart rate, blood pressure, body temperature, and pain perception. Studied quantities function on timescales ranging from minutes to days. To understand how endotoxin impacts the body over this vast span of timescales, we examine the response to variations in LPS administration methods (single dose, repeated dose, and continuous dose) as well as the timing of the administration and the amount of endotoxin released into the system. We calibrate the model to literature data for a 2 ng/kg LPS bolus injection. Results show that LPS administration during early morning or late evening generates a more pronounced hormonal response. Most of the LPS e ﬀ ects are eliminated from the body 24 hours after administration, the main impact of inﬂammation remains in the system for 48 hours, and repeated dose simulations show that residual e ﬀ ects remain more than 10 days after the initial injection. We also show that if the LPS administration method or total dosage is increased, the system response is ampliﬁed, posing a greater risk of hypotension and pyrexia.
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation

Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation. Previous TTA schemes assume that the test samples are independent and identically distributed (i.i.d.), even though they are often temporally correlated (non-i.i.d.) in application scenarios, e.g., autonomous driving. We discover that most existing TTA methods fail dramatically under such scenarios. Motivated by this, we present a new test-time adaptation scheme that is robust against non-i.i. d. test data streams. Our novelty is mainly two-fold: (a) Instance-Aware Batch Normalization (IABN) that corrects normalization for out-of-distribution samples, and (b) Prediction-balanced Reservoir Sampling (PBRS) that simulates i.i. d. data stream from non-i.i. d. stream in a class-balanced manner. Our evaluation with various datasets, including real-world non-i.i. d. streams, demonstrates that the proposed robust TTA not only outperforms state-of-the-art TTA algorithms in the non-i.i. d. setting, but also achieves comparable performance to those algorithms under the i.i. d. assumption. Code is available at https://github.com/TaesikGong/NOTE.
DO INTERMEDIATE-MASS BLACK HOLES EXIST IN GLOBULAR CLUSTERS?

The existence of intermediate-mass black holes (IMBHs) in globular clusters (GCs) remains a crucial problem. Searching for IMBHs in GCs reveals a discrepancy between radio observations and dynamical modelings: the upper mass limits constrained by radio observations are systematically lower than that of dynamical modelings. One possibility for such a discrepancy is that, as we suggest in this work, there exist outflows in accretion flows. Our results indicate that, for most sources, current radio observations cannot rule out the possibility that IMBHs may exist in GCs. In addition, we adopt an relation to revisit this issue, which confirms the results obtained by the fundamental plane relation.
Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Study

Multihop Question Answering is a complex Natural Language Processing task that requires multiple steps of reasoning to find the correct answer to a given question. Previous research has explored the use of models based on Graph Neural Networks for tackling this task. Various architectures have been proposed, including Relational Graph Convolutional Networks (RGCN). For these many node types and relations between them have been introduced, such as simple entity co-occurrences, modelling coreferences, or"reasoning paths"from questions to answers via intermediary entities. Nevertheless, a thoughtful analysis on which relations, node types, embeddings and architecture are the most beneficial for this task is still missing. In this paper we explore a number of RGCN-based Multihop QA models, graph relations, and node embeddings, and empirically explore the influence of each on Multihop QA performance on the WikiHop dataset.
AN EXPONENTIAL DECLINE AT THE BRIGHT END OF THE z = 6 GALAXY LUMINOSITY FUNCTION

We present the results of a search for the most luminous star-forming galaxies at redshifts z ≈ 6 based on Canada–France–Hawaii Telescope Legacy Survey data. We identify a sample of 40 Lyman break galaxies (LBGs) brighter than magnitude z′ = 25.3 across an area of almost 4 deg2. Sensitive spectroscopic observations of seven galaxies provide redshifts for four, of which only two have moderate to strong Lyα emission lines. All four have clear continuum breaks in their spectra. Approximately half of the LBGs are spatially resolved in 0.7 arcsec seeing images, indicating larger sizes than lower luminosity galaxies discovered with the Hubble Space Telescope, possibly due to ongoing mergers. The stacked optical and infrared photometry is consistent with a galaxy model with stellar mass ∼1010 M☉. There is strong evidence for substantial dust reddening with a best-fit AV = 0.75 and AV > 0.48 at 2σ confidence, in contrast to the typical dust-free galaxies of lower luminosity at this epoch. The spatial extent and spectral energy distribution suggest that the most luminous z ≈ 6 galaxies are undergoing merger-induced starbursts. The luminosity function of z = 5.9 star-forming galaxies is derived. This agrees well with previous work and shows strong evidence for an exponential decline at the bright end, indicating that the feedback processes that govern the shape of the bright end are occurring effectively at this epoch.
Predictive event segmentation and representation with neural networks: A self-supervised model assessed by psychological experiments

People segment complex, ever-changing and continuous experience into basic, stable and discrete spatio-temporal experience units, called events. Event segmentation literature investigates the mechanisms that allow people to extract events. Event segmentation theory points out that people predict ongoing activities and observe prediction error signals to find event boundaries that keep events apart. In this study, we investigated the mechanism giving rise to this ability by a computational model and accompanying psychological experiments. Inspired from event segmentation theory and predictive processing, we introduced a self-supervised model of event segmentation. This model consists of neural networks that predict the sensory signal in the next time-step to represent different events, and a cognitive model that regulates these networks on the basis of their prediction errors. In order to verify the ability of our model in segmenting events, learning them during passive observation, and representing them in its internal representational space, we prepared a video that depicts human behaviors represented by point-light displays. We compared event segmentation behaviors of participants and our model with this video in two hierarchical event segmentation levels. By using point-biserial correlation technique, we demonstrated that event segmentation decisions of our model correlated with the responses of participants. Moreover, by approximating representation space of participants by a similarity-based technique, we showed that our model formed a similar representation space with those of participants. The result suggests that our model that tracks the prediction error signals can produce human-like event boundaries and event representations. Finally, we discussed our contribution to the literature of event cognition and our understanding of how event segmentation is implemented in the brain.
Non-Smooth, H\"older-Smooth, and Robust Submodular Maximization

We study the problem of maximizing a continuous DR-submodular function that is not necessarily smooth. We prove that the continuous greedy algorithm achieves an $[(1-1/e)\OPT-\epsilon]$ guarantee when the function is monotone and H\"older-smooth, meaning that it admits a H\"older-continuous gradient. For functions that are non-differentiable or non-smooth, we propose a variant of the mirror-prox algorithm that attains an $[(1/2)\OPT-\epsilon]$ guarantee. We apply our algorithmic frameworks to robust submodular maximization and distributionally robust submodular maximization under Wasserstein ambiguity. In particular, the mirror-prox method applies to robust submodular maximization to obtain a single feasible solution whose value is at least $(1/2)\OPT-\epsilon$. For distributionally robust maximization under Wasserstein ambiguity, we deduce and work over a submodular-convex maximin reformulation whose objective function is H\"older-smooth, for which we may apply both the continuous greedy and the mirror-prox algorithms.
Entanglement transitions with free fermions

We use Majorana operators to study entanglement dynamics under random free fermion unitary evolution and projective measurements in one dimension. For certain choices of unitary evolution, namely those which swap neighboring Majorana operators, and measurements of neighboring Majorana bilinears, one can map the evolution to the statistical model of completely packed loops with crossings (CPLC) and study the corresponding phase diagram. We generalize this model using the language of fermionic Gaussian states to a general free fermion unitary evolution acting on neighboring Majorana operators, and numerically compute its phase diagram. We find that both the Goldstone and area law phases persist in this new phase diagram, but with a shifted phase boundary. One important qualitative aspect of the new phase boundary is that even for the case of commuting measurements, the Goldstone phase persists up to a finite non-zero measurement rate. This is in contrast with the CPLC, in which non-commuting measurements are necessary for realizing the Goldstone phase. We also numerically compute the correlation length critical exponent at the transition, which we find to be near to that of the CPLC, and give a tentative symmetry based explanation for some differences in the phase transition line between the CPLC and generalized models.
Interaction of half-quantized vortices in two-component Bose-Einstein condensates

We study the asymptotic interaction between two half-quantized vortices in two-component Bose-Einstein condensates. When two vortices in different components are placed at distance 2R, the leading order of the force between them is found to be (lnR/{xi}-1/2)/R{sup 3}, in contrast to 1/R between vortices placed in the same component. We derive it analytically using the Abrikosov ansatz and the profile functions of the vortices, confirmed numerically with the Gross-Pitaevskii model. We also find that the short-range cutoff of the intervortex potential linearly depends on the healing length.
3D Brain and Heart Volume Generative Models: A Survey

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This paper provides a comprehensive survey of generative models for three-dimensional (3D) volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditional and conditional generative models is proposed to cover diverse medical tasks for the brain and heart: unconditional synthesis, classification, conditional synthesis, segmentation, denoising, detection, and registration. We provide relevant background, examine each task and also suggest potential future directions. A list of the latest publications will be updated on GitHub to keep up with the rapid influx of papers at https://github.com/csyanbin/3D-Medical-Generative-Survey.
Lyapunov exponent for small particles in smooth one-dimensional flows

This paper discusses the Lyapunov exponent λ for small particles in a spatially and temporally smooth flow in one dimension. The Lyapunov exponent is obtained as a series expansion in the Stokes number, St, which is a dimensionless measure of the importance of inertial effects. The approach described here can be extended to calculations of the Lyapunov exponents and of the correlation dimension for inertial particles in higher dimensions. It is shown that there is a correction to this theory which arises because the particles do not sample the velocity field ergodically. Using this non-ergodic correction, it is found that (contrary to expectations) the first-order term in the expansion of λ does not vanish.
A Generalist Framework for Panoptic Segmentation of Images and Videos

Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image. As permutations of instance IDs are also valid solutions, the task requires learning of high-dimensional one-to-many mapping. As a result, state-of-the-art approaches use customized architectures and task-specific loss functions. We formulate panoptic segmentation as a discrete data generation problem, without relying on inductive bias of the task. A diffusion model is proposed to model panoptic masks, with a simple architecture and generic loss function. By simply adding past predictions as a conditioning signal, our method is capable of modeling video (in a streaming setting) and thereby learns to track object instances automatically. With extensive experiments, we demonstrate that our simple approach can perform competitively to state-of-the-art specialist methods in similar settings. 1
Probing high-redshift quasars with ALMA. I. Expected observables and potential number of sources

Aims. We explore how ALMA observations can probe high-redshift galaxies in unprecedented detail. We discuss the main observables that are excited by the large-scale starburst, and formulate expectations for the chemistry and the fluxes in the center of active galaxies, in which chemistry may be driven by the absorption of X-rays. We estimate the expected number of sources at high redshift in an ALMA deep field. As a specific example for the complex interpretation of sub-mm line observations, we analyze the recently detected z = 6.42 quasar, for which a number of different line fluxes is already available. We note that our diagnostics may also be valuable for future observations in the local universe with space-borne instruments like on SPICA or FIRI. Methods. To estimate the observables from the starburst, we check which emission from the starburst ring of the nearby Seyfert 2 galaxy NGC 1068 falls into the ALMA bands if the galaxy were placed at z = 8. We estimate the sizes of the central X-ray dominated region based on a semi-analytic model, and employ a detailed 1D approach for the chemistry in X-ray irradiated molecular clouds to evaluate the chemistry and the expected line emission under these conditions. We make use of pre-existing chemistry calculations in X-ray dominated regions to show the dependence of different line fluxes on X-ray luminosity, cloud density and cloud column density. We use theoretical models for the high-z black hole population and the local SMBH density to estimate the number of sources at higher redshift. Results. We show that a number of different fine-structure lines may be used to probe the starburst component of high-redshift quasars in considerable detail, providing specific information on the structure of these galaxies by several independent means. We show that the size of the central X-ray dominated region is of the order of a few hundred parsec, and we provide detailed predictions for the expected fluxes in CO, [CII] and [OI]. While the latter fine-structure lines quickly become optically thick and depend mostly on the strength of the X-ray source, the rotational CO lines have a non-trivial dependence on these parameters. We compare our models to XDRs observed in NGC 1068 and APM 08279 and find that the observed emission can indeed be explained with these models. Depending on the amount of X-ray flux, the CO line intensities may rise continuously up to the (17-16) transition. A measurement of such high-J lines allows one to distinguish observationally between XDRs and PDRs. For the recently observed z = 6.42 quasar, we show that the collected fluxes cannot be interpreted in terms of a single gas component. We find indications for the presence of a dense warm component in active star forming regions and a low-density component in more quiescent areas. Near z = 6, an ALMA deep field may find roughly one source per arcmin 2 . At higher redshift, one likely has to rely on other surveys like JWST to find appropriate sources.
Excursions through KK modes

In this article we study Kaluza-Klein (KK) dimensional reduction of massive Abelian gauge theories with charged matter fields on a circle. Since local gauge transformations change position dependence of the charged fields, the decomposition of the charged matter fields into KK modes is gauge dependent. While whole KK mass spectrum is independent of the gauge choice, the mode number depends on the gauge. The masses of the KK modes also depend on the field value of the zero-mode of the extra dimensional component of the gauge field. In particular, one of the KK modes in the KK tower of each massless 5D charged field becomes massless at particular values of the extra-dimensional component of the gauge field. When the extra-dimensional component of the gauge field is identified with the inflaton, this structure leads to recursive cosmological particle productions.
C-Mixup: Improving Generalization in Regression

Improving the generalization of deep networks is an important open challenge, particularly in domains without plentiful data. The mixup algorithm improves generalization by linearly interpolating a pair of examples and their corresponding labels. These interpolated examples augment the original training set. Mixup has shown promising results in various classification tasks, but systematic analysis of mixup in regression remains underexplored. Using mixup directly on regression labels can result in arbitrarily incorrect labels. In this paper, we propose a simple yet powerful algorithm, C-Mixup, to improve generalization on regression tasks. In contrast with vanilla mixup, which picks training examples for mixing with uniform probability, C-Mixup adjusts the sampling probability based on the similarity of the labels. Our theoretical analysis confirms that C-Mixup with label similarity obtains a smaller mean square error in supervised regression and meta-regression than vanilla mixup and using feature similarity. Another benefit of C-Mixup is that it can improve out-of-distribution robustness, where the test distribution is different from the training distribution. By selectively interpolating examples with similar labels, it mitigates the effects of domain-associated information and yields domain-invariant representations. We evaluate C-Mixup on eleven datasets, ranging from tabular to video data. Compared to the best prior approach, C-Mixup achieves 6.56%, 4.76%, 5.82% improvements in in-distribution generalization, task generalization, and out-of-distribution robustness, respectively. Code is released at https://github.com/huaxiuyao/C-Mixup.
Wellposedness for a (1+1)-dimensional wave equation with quasilinear boundary condition

We consider the linear wave equation V(x)utt(x,t)−uxx(x,t)=0 on [0,∞)×[0,∞) with initial conditions and a nonlinear Neumann boundary condition ux(0,t)=(f(ut(0,t)))t at x = 0. This problem is an exact reduction of a nonlinear Maxwell problem in electrodynamics. In the case where f:R→R is an increasing homeomorphism we study global existence, uniqueness and wellposedness of the initial value problem by the method of characteristics and fixed point methods. We also prove conservation of energy and momentum and discuss why there is no wellposedness in the case where f is a decreasing homeomorphism. Finally we show that previously known time-periodic, spatially localized solutions (breathers) of the wave equation with the nonlinear Neumann boundary condition at x = 0 have enough regularity to solve the initial value problem with their own initial data.
Matern Cluster Process with Holes at the Cluster Centers

Inspired by recent applications of point processes to biological nanonetworks, this paper presents a novel variant of a Mat\'ern cluster process (MCP) in which the points located within a certain distance from the cluster centers are removed. We term this new process the MCP with holes at the cluster center (MCP-H, in short). Focusing on the three-dimensional (3D) space, we first characterize the conditional distribution of the distance between an arbitrary point of a given cluster to the origin, conditioned on the location of that cluster, for both MCP and MCP-H. These distributions are shown to admit remarkably simple closed forms in the 3D space, which is not even possible in the simpler two-dimensional (2D) case. Using these distributions, the contact distance distribution and the probability generating functional (PGFL) are characterized for both MCP and MCP-H.
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning

We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized), but the agents do not know beforehand which communication level they will encounter at execution time. To formalize our setting, we define a new class of multi-agent partially observable Markov decision processes (POMDPs) that we name hybrid-POMDPs, which explicitly model a communication process between the agents. We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents' observations at execution time. We evaluate MARO on standard scenarios and extensions of previous benchmarks tailored to emphasize the negative impact of partial observability in MARL. Experimental results show that our method consistently outperforms relevant baselines, allowing agents to act with faulty communication while successfully exploiting shared information.