Miracle-dz/UniFAR-FTUs huggingface dataset:
A Computing-in-Memory-based One-Class Hyperdimensional Computing Model for Outlier Detection

In this work, we present ODHD, an algorithm for outlier detection based on hyperdimensional computing (HDC), a non-classical learning paradigm. Along with the HDC-based algorithm, we propose IM-ODHD, a computing-in-memory (CiM) implementation based on hardware/software (HW/SW) codesign for improved latency and energy efficiency. The training and testing phases of ODHD may be performed with conventional CPU/GPU hardware or our IM-ODHD, SRAM-based CiM architecture using the proposed HW/SW codesign techniques. We evaluate the performance of ODHD on six datasets from different application domains using three metrics, namely accuracy, F1 score, and ROC-AUC, and compare it with multiple baseline methods such as OCSVM, isolation forest, and autoencoder. The experimental results indicate that ODHD outperforms all the baseline methods in terms of these three metrics on every dataset for both CPU/GPU and CiM implementations. Furthermore, we perform an extensive design space exploration to demonstrate the tradeoff between delay, energy efficiency, and performance of ODHD. We demonstrate that the HW/SW codesign implementation of the outlier detection on IM-ODHD is able to outperform the GPU-based implementation of ODHD by at least 331.5x/889x in terms of training/testing latency (and on average 14.0x/36.9x in terms of training/testing energy consumption.
Did a Kilonova Set Off in Our Galactic Backyard 3.5 Myr ago?

The recent detection of the live isotopes 60Fe and 244Pu in deep ocean sediments dating back to the past 3–4 Myr poses a serious challenge to the identification of their production site(s). While 60Fe is usually attributed to standard core-collapse supernovae, actinides are r-process nucleosynthesis yields, which are believed to be synthesized in rare events, such as special classes of supernovae or binary mergers involving at least one neutron star. Previous works concluded that a single binary neutron star merger cannot explain the observed isotopic ratio. In this work, we consider a set of numerical simulations of binary neutron star mergers producing long-lived massive remnants expelling both dynamical and spiral-wave wind ejecta. The latter, due to a stronger neutrino irradiation, also produce iron-group elements. Assuming that large-scale mixing is inefficient before the fading of the kilonova remnant and that the spiral-wave wind is sustained over a 100–200 ms timescale, the ejecta emitted at mid-high latitudes provide a 244Pu over 60Fe ratio compatible with observations. The merger could have happened 80–150 pc away from the Earth and between 3.5 and 4.5 Myr ago. We also compute expected isotopic ratios for eight other live radioactive nuclides showing that the proposed binary neutron star merger scenario is distinguishable from other scenarios proposed in the literature.
THInImg: Cross-modal Steganography for Presenting Talking Heads in Images

Cross-modal Steganography is the practice of concealing secret signals in publicly available cover signals (distinct from the modality of the secret signals) unobtrusively. While previous approaches primarily concentrated on concealing a relatively small amount of information, we propose THInImg, which manages to hide lengthy audio data (and subsequently decode talking head video) inside an identity image by leveraging the properties of human face, which can be effectively utilized for covert communication, transmission and copyright protection. THInImg consists of two parts: the encoder and decoder. Inside the encoder-decoder pipeline, we introduce a novel architecture that substantially increase the capacity of hiding audio in images. Moreover, our framework can be extended to iteratively hide multiple audio clips into an identity image, offering multiple levels of control over permissions. We conduct extensive experiments to prove the effectiveness of our method, demonstrating that THInImg can present up to 80 seconds of high quality talking-head video (including audio) in an identity image with 160x160 resolution.
Existence solutions for a weighted equation of p-biharmonic type in the unit ball of $\mathbb{R}^{N}$ with critical exponential growth

We study a weighted $\frac{N}{2}$ biharmonic equation involving a positive continuous potential in $\overline{B}$. The non-linearity is assumed to have critical exponential growth in view of logarithmic weighted Adams' type inequalities in the unit ball of $\mathbb{R}^{N}$. It is proved that there is a nontrivial weak solution to this problem by the mountain Pass Theorem. We avoid the loss of compactness by proving a concentration compactness result and by a suitable asymptotic condition.
Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

In autonomous driving, predicting future events in advance and evaluating the foreseeable risks empowers autonomous vehicles to better plan their actions, enhancing safety and efficiency on the road. To this end, we propose Drive-WM, the first driving world model compatible with existing end-to-end planning models. Through a joint spatial-temporal modeling facilitated by view factorization, our model generates high-fidelity multiview videos in driving scenes. Building on its powerful generation ability, we showcase the potential of applying the world model for safe driving planning for the first time. Particularly, our Drive-WM enables driving into multiple futures based on distinct driving maneuvers, and determines the optimal trajectory according to the image-based rewards. Evaluation on real-world driving datasets verifies that our method could generate high-quality, consistent, and controllable multiview videos, opening up possibilities for real-world simulations and safe planning.
A Class of Directed Acyclic Graphs with Mixed Data Types in Mediation Analysis

We propose a unified class of generalized structural equation models (GSEMs) with data of mixed types in mediation analysis, including continuous, categorical, and count variables. Such models extend substantially the classical linear structural equation model to accommodate many data types arising from the application of mediation analysis. Invoking the hierarchical modeling approach, we specify GSEMs by a copula joint distribution of outcome variable, mediator and exposure variable, in which marginal distributions are built upon generalized linear models (GLMs) with confounding factors. We discuss the identifiability conditions for the causal mediation effects in the counterfactual paradigm as well as the issue of mediation leakage, and develop an asymptotically efficient profile maximum likelihood estimation and inference for two key mediation estimands, natural direct effect and natural indirect effect, in different scenarios of mixed data types. The proposed new methodology is illustrated by a motivating epidemiological study that aims to investigate whether the tempo of reaching infancy BMI peak (delay or on time), an important early life growth milestone, may mediate the association between prenatal exposure to phthalates and pubertal health outcomes.
Parity-check Codes from Disjunct Matrices

The matrix representations of linear codes have been well-studied for use as disjunct matrices. However, no connection has previously been made between the properties of disjunct matrices and the parity-check codes obtained from them. This paper makes this connection for the first time. We provide some fundamental results on parity-check codes from general disjunct matrices (in particular, a minimum distance bound). We then consider three specific constructions of disjunct matrices and provide parameters of their corresponding parity-check codes including rate, distance, girth, and density. We show that, by choosing the correct parameters, the codes we construct have the best possible error-correction performance after one round of bit-flipping decoding with regard to a modified version of Gallager's bit-flipping decoding algorithm.
Invariance assumptions for class distribution estimation

We study the problem of class distribution estimation under dataset shift. On the training dataset, both features and class labels are observed while on the test dataset only the features can be observed. The task then is the estimation of the distribution of the class labels, i.e. the estimation of the class prior probabilities, in the test dataset. Assumptions of invariance between the training joint distribution of features and labels and the test distribution can considerably facilitate this task. We discuss the assumptions of covariate shift, factorizable joint shift, and sparse joint shift and their implications for class distribution estimation.
In Search of a Data Transformation That Accelerates Neural Field Training

Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal. A key obstacle that prevents its widespread adoption is the encoding speed-generating neural fields requires an overfitting of a neural network, which can take a significant number of SGD steps to reach the desired fidelity level. In this paper, we delve into the impacts of data transformations on the speed of neural field training, specifically focusing on how permuting pixel locations affect the convergence speed of SGD. Counterintuitively, we find that randomly permuting the pixel locations can considerably accelerate the training. To explain this phenomenon, we examine the neural field training through the lens of PSNR curves, loss landscapes, and error patterns. Our analyses suggest that the random pixel permutations remove the easy-to-fit patterns, which facilitate easy optimization in the early stage but hinder capturing fine details of the signal.
Translational and reorientational dynamics in carboxylic acid-based deep eutectic solvents.

The glass formation and the dipolar reorientational motions in deep eutectic solvents (DESs) are frequently overlooked, despite their crucial role in defining the room-temperature physiochemical properties. To understand the effects of these dynamics on the ionic conductivity and their relation to the mechanical properties of the DES, we conducted broadband dielectric and rheological spectroscopy over a wide temperature range on three well-established carboxylic acid-based natural DESs. These are the eutectic mixtures of choline chloride with oxalic acid (oxaline), malonic acid (maline), and phenylacetic acid (phenylaceline). In all three DESs, we observe signs of a glass transition in the temperature dependence of their dipolar reorientational and structural dynamics, as well as varying degrees of motional decoupling between the different observed dynamics. Maline and oxaline display a breaking of the Walden rule near the glass-transition temperature, while the relation between the dc conductivity and dipolar relaxation time in both maline and phenylaceline is best described by a power law. The glass-forming properties of the investigated systems not only govern the orientational dipolar motions and rheological properties, which are of interest from a fundamental point of view, but they also affect the dc conductivity, even at room temperature, which is of high technical relevance.
Pseudomonas aeruginosa shows between and within strains heterogeneity in virulence phenotype after passive exposure to zebrafish (Danio rerio) and nematodes (Caenorhabditis elegans)

The rapid emergence of diseases and parasites in aquatic wildlife requires improved methodologies to identify and characterize new and future pathogens. While microinjection of pathogens directly into a sentinel organism such as zebrafish enables the exploration of infection and immune response in the host, such methodology focuses primarily on identifying causative agents in events of aquatic wildlife mortality due to acute infection. Here, we present an updated protocol of infection by static immersion in larval zebrafish to investigate the possible effect of prolonged environmental exposure to an opportunistic pathogen. By controlling microbial growth and monitoring mortality over five days, we show that static immersion can detect minute differences in virulence profiles between and within different strains of Pseudomonas aeruginosa, an important opportunistic pathogen of animals and humans. We then conducted two sets of passive exposure virulence assays in Caenorhabditis elegans, an alternative model. We demonstrated the virulence phenotype, while showing slight differences between experimental models, showed similar trends. We believe that passive exposure thus offers a practical host-pathogen model that simulates opportunistic infection occurring in the environment and enables the detection of minute changes in virulence between and within bacterial strains.
Nuevos conceptos en la transmisión sexual del HIV

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TRIBAL: Tree Inference of B cell Clonal Lineages

B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies. Availability TRIBAL is available at https://github.com/elkebir-group/tribal
Anti- and pro-inflammatory milieu differentially regulate differentiation and immune functions of oligodendrocyte progenitor cells

Oligodendrocyte progenitor cells (OPC) were regarded for years solely for their regenerative role; however, their immune-modulatory roles have gained much attention recently, particularly in the context of multiple sclerosis (MS). Despite extensive studies on OPCs, there are limited data elucidating the interactions between their intrinsic regenerative and immune functions, as well as their relationship with the inflamed central nervous system (CNS) environment, a key factor in MS pathology. We examined the effects of pro-inflammatory cytokines, represented by interferon (IFN)-γ and tumor necrosis factor (TNF)-α, as well as anti-inflammatory cytokines, represented by interleukin(IL)-4 and IL-10, on OPC differentiation and immune characteristics. Using primary cultures, ELISA, and immunofluorescence stainings, we assessed differentiation capacity, phagocytic activity, major histocompatibility complex (MHC)-II expression, and cytokine secretion. We observed that the anti-inflammatory milieu (IL4 and IL10) reduced both OPC differentiation and immune functions. Conversely, exposure to TNF-α led to intact differentiation, increased phagocytic activity, high levels of MHC-II expression, and cytokines secretion. Those effects were attributed to signaling via TNF-receptor-2 (TNFR2) and counteracted the detrimental effects of IFNγ on OPC differentiation. Our findings suggest that a pro-regenerative, permissive inflammatory environment is needed for OPCs to execute both regenerative and immune-modulatory functions.
Terminally differentiated effector memory T cells associate with cognitive and AD-related biomarkers in an aging-based community cohort

Background and Purpose The immune response changes during aging and the progression of Alzheimer’s disease (AD) and related dementia (ADRD). Terminally differentiated effector memory T cells (called TEMRA) are important during aging and AD due to their cytotoxic phenotype and association with cognitive decline. However, it is not clear if the changes seen in TEMRAs are specific to AD-related cognitive decline specifically or are more generally correlated with cognitive decline. This study aimed to examine whether TEMRAs are associated with cognition and plasma biomarkers of AD, neurodegeneration, and neuroinflammation in a community-based cohort of older adults. Methods Study participants from a University of Kentucky Alzheimer’s Disease Research Center (UK-ADRC) community-based cohort of aging and dementia were used to test our hypothesis. There were 84 participants, 44 women and 40 men. Participants underwent physical examination, neurological examination, medical history, cognitive testing, and blood collection to determine plasma biomarker levels (Aβ42/Aβ40 ratio, total tau, Neurofilament Light chain (Nf-L), Glial Fibrillary Acidic Protein (GFAP)) and to isolate peripheral blood mononuclear cells (PBMCs). Flow cytometry was used to analyze PBMCs from study participants for effector and memory T cell populations, including CD4+ and CD8+ central memory T cells (TCM), Naïve T cells, effector memory T cells (TEM), and effector memory CD45RA+ T cells (TEMRA) immune cell markers. Results CD8+ TEMRAs were positively correlated with Nf-L and GFAP. We found no significant difference in CD8+ TEMRAs based on cognitive scores and no associations between CD8+ TEMRAs and AD-related biomarkers. CD4+ TEMRAs were associated with cognitive impairment on the MMSE. Gender was not associated with TEMRAs, but it did show an association with other T cell populations. Conclusion These findings suggest that the accumulation of CD8+ TEMRAs may be a response to neuronal injury (Nf-L) and neuroinflammation (GFAP) during aging or the progression of AD and ADRD. As our findings in a community-based cohort were not clinically- defined AD participants but included all ADRDs, this suggests that TEMRAs may be associated with changes in systemic immune T cell subsets associated with the onset of pathology.
Spindle-slow oscillation coupling during sleep predicts sequence-based language learning

Language is one of the most defining human capabilities, involving the coordination of brain networks that generalise the meaning of linguistic units of varying complexity. On a neural level, neocortical slow oscillations and thalamic spindles during sleep facilitate the reactivation of newly encoded memory traces, manifesting in distinct oscillatory activity during retrieval. However, it is currently unknown if the effect of sleep on memory extends to the generalisation of the mechanisms that subserve sentence comprehension. We address this question by analysing electroencephalogram data recorded from 35 participants during an artificial language learning task and an 8hr nocturnal sleep period. We found that a period of sleep was associated with increased alpha/beta power and improved behavioural performance. Phase amplitude coupling analyses also revealed that spindle-slow oscillation coupling predicted the consolidation of sequence-based word orders, which was associated with distinct patterns of oscillatory activity during sentence processing. Taken together, this study presents converging behavioural and neurophysiological evidence for a role of sleep in the consolidation of higher order language learning and associated oscillatory neural activity.
The Minimax Risk in Testing the Histogram of Discrete Distributions for Uniformity under Missing Ball Alternatives

We study the problem of testing the goodness of fit of a discrete sample from many categories to the uniform distribution over the categories. As a class of alternative hypotheses, we consider the removal of an ℓp ball of radius ϵ around the uniform rate sequence for p≤ 2. When the number of samples n and number of categories N go to infinity while ϵ is small, the minimax risk $R_ \in ^{\ast}$ in testing based on the sample’s histogram (number of absent categories, singletons, collisions, …) asymptotes to $2\Phi \left( { - n{N^{2 - 2/p}}{ \in ^2}/\sqrt {8N} } \right)$, with Φ(x) the normal CDF. This characterization allows the comparison of the many estimators previously proposed for this problem at the constant level, rather than at the rate of convergence of the risk or the scaling order of the sample complexity. The minimax test mostly relies on collisions in the very small sample limit but otherwise behaves like the chisquared test. Empirical studies over a range of problem parameters show that our estimate is accurate in finite samples and that the minimax test is significantly better than the chisquared test or a test that only uses collisions. Our analysis relies on the asymptotic normality of histogram ordinates, the equivalence between the minimax setting and a Bayesian setting, and the characterization of the least favorable prior by reducing a multi-dimensional optimization problem to a one-dimensional problem.
Scaled Agile: Toolgestützte Echtzeitplausibilisierung des PI-Planning

: Agiles Projektvorgehen mit seinen fortschrittlichen Methoden hat sich mittlerweile weit-gehend durchgesetzt. So schlank einzelne agile Teams agieren, so komplex wird das Zusammenspiel mehrerer oder gar vieler solcher Teams - insbesondere im Rahmen der auch hier erforderlichen Pla-nung. Aktuelle skalierende Ansätze wie SAFe (Scaled Agile Framework) bieten dafür zwar grund-sätzliche Verfahren, jedoch zeigt die Praxis, dass man auf der Meta-Ebene mit zunehmender Anzahl der beteiligten Teams meist zu optimistisch unterwegs ist: Spätestens nach der Auflösung Team-übergreifender Abhängigkeiten ist beim PI-Planning die Komplexität in der Regel so hoch, dass eine realisitische Gesamteinschätzung der Leistbarkeit über alle Teams und das gesamte Program Increment (PI) nicht mehr möglich ist.