Miracle-dz/UniFAR-FTUs huggingface dataset:
Deadline Aware Two-Timescale Resource Allocation for VR Video Streaming

In this paper, we investigate resource allocation problem in the context of multiple virtual reality (VR) video flows sharing a certain link, considering specific deadline of each video frame and the impact of different frames on video quality. Firstly, we establish a queuing delay bound estimation model, enabling link node to proactively discard frames that will exceed the deadline. Secondly, we model the importance of different frames based on viewport feature of VR video and encoding method. Accordingly, the frames of each flow are sorted. Then we formulate a problem of minimizing long-term quality loss caused by frame dropping subject to per-flow quality guarantee and bandwidth constraints. Since the frequency of frame dropping and network fluctuation are not on the same time scale, we propose a two-timescale resource allocation scheme. On the long timescale, a queuing theory based resource allocation method is proposed to satisfy quality requirement, utilizing frame queuing delay bound to obtain minimum resource demand for each flow. On the short timescale, in order to quickly fine-tune allocation results to cope with the unstable network state, we propose a low-complexity heuristic algorithm, scheduling available resources based on the importance of frames in each flow. Extensive experimental results demonstrate that the proposed scheme can efficiently improve quality and fairness of VR video flows under various network conditions.
Circulating PACAP levels are associated with altered imaging measures of entorhinal cortex neurite density in posttraumatic stress disorder

Background. Pituitary adenylate cyclase-activating polypeptide (PACAP) regulates plasticity in brain systems underlying arousal and memory and is associated with posttraumatic stress disorder (PTSD). Research in animal models suggests that PACAP modulates entorhinal cortex (EC) input to the hippocampus, contributing to impaired contextual fear conditioning. In PTSD, PACAP is associated with higher activity of the amygdala to threat stimuli and lower functional connectivity of the amygdala and hippocampus. However, PACAP-affiliated structural alterations of these regions have not been reported. Here, we examined whether peripheral PACAP levels were associated with neuronal morphology of the amygdala and hippocampus (primary analysis), and EC (secondary analysis) using Neurite Orientation Dispersion and Density Imaging. Methods. Sixty-four (44 female) adults (19 to 54 years old) with DSM-5 Criterion A trauma exposure completed the Clinician-Administered PTSD Scale (CAPS-5), a blood draw, and magnetic resonance imaging. PACAP38 radioimmunoassay was performed and T1-weighted and multi-shell diffusion-weighted images were acquired. Neurite Density Index (NDI) and Orientation Dispersion Index (ODI) were quantified in the amygdala, hippocampus, and EC. CAPS-5 total score and anxious arousal score were used to test for clinical associations with brain structure. Results. Higher PACAP levels in blood were associated with greater EC NDI ({beta}=0.31, q=0.034) and lower EC ODI ({beta}=-0.30, q=0.042) and not hippocampal or amygdala measures. Neither EC NDI nor ODI was associated with clinical measures. Conclusions. Circulating PACAP levels were associated with altered neuronal density of the EC but not hippocampus or amygdala. These findings strengthen evidence that PACAP may impact arousal-associated memory circuits.
Enumerating minimal vertex covers and dominating sets with capacity and/or connectivity constraints

In this paper, we consider the problems of enumerating minimal vertex covers and minimal dominating sets with capacity and/or connectivity constraints. We develop polynomial-delay enumeration algorithms for these problems on bounded-degree graphs. For the case of minimal connected vertex cover, our algorithm runs in polynomial delay even on the class of $d$-claw free graphs, which extends the result on bounded-degree graphs. To complement these algorithmic results, we show that the problems of enumerating minimal connected vertex covers and minimal capacitated vertex covers in bipartite graphs are at least as hard as enumerating minimal transversals in hypergraphs.
Conditional automatic complexity and its metrics

Li, Chen, Li, Ma, and Vit\'anyi (2004) introduced a similarity metric based on Kolmogorov complexity. It followed work by Shannon in the 1950s on a metric based on entropy. We define two computable similarity metrics, analogous to the Jaccard distance and Normalized Information Distance, based on conditional automatic complexity and show that they satisfy all axioms of metric spaces.
SMAD4: A critical regulator of cardiac neural crest cell fate and vascular smooth muscle development

During embryogenesis, cardiac neural crest‐derived cells (NCs) migrate into the pharyngeal arches and give rise to the vascular smooth muscle cells (vSMCs) of the pharyngeal arch arteries (PAAs). vSMCs are critical for the remodeling of the PAAs into their final adult configuration, giving rise to the aortic arch and its arteries (AAAs).
SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation

Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, when examining environments which will host AR experiences, it is challenging to determine where those instability artifacts will occur; we rarely have access to ground truth pose to measure pose error, and even if pose error is available, traditional visualizations do not connect that data with the real environment, limiting their usefulness. To address these issues we present SiTAR (Situated Trajectory Analysis for Augmented Reality), the first situated trajectory analysis system for AR that incorporates estimates of pose tracking error. We start by developing the first uncertainty-based pose error estimation method for visual-inertial simultaneous localization and mapping (VI-SLAM), which allows us to obtain pose error estimates without ground truth; we achieve an average accuracy of up to 96.1% and an average FI score of up to 0.77 in our evaluations on four VI-SLAM datasets. Next, we present our SiTAR system, implemented for ARCore devices, combining a backend that supplies uncertainty-based pose error estimates with a frontend that generates situated trajectory visualizations. Finally, we evaluate the efficacy of SiTAR in realistic conditions by testing three visualization techniques in an in-the-wild study with 15 users and 13 diverse environments; this study reveals the impact both environment scale and the properties of surfaces present can have on user experience and task performance.
Quantized thermal and spin transports of dirty planar topological superconductors

Nontrivial bulk topological invariants of quantum materials can leave their signatures on charge, thermal and spin transports. In two dimensions, their imprints can be experimentally measured from well-developed multi-terminal Hall bar arrangements. Here, we numerically compute the low temperature ($T$) thermal ($\kappa_{xy}$) and zero temperature spin ($\sigma^{sp}_{xy}$) Hall conductivities, and longitudinal thermal conductance ($G^{th}_{xx}$) of various paradigmatic two-dimensional fully gapped topological superconductors, belonging to distinct Altland-Zirnbauer symmetry classes, namely $p+ip$ (class D), $d+id$ (class C) and $p \pm ip$ (class DIII) paired states, in mesoscopic six-terminal Hall bar setups from the scattering matrix formalism using Kwant. In both clean and weak disorder limits, the time-reversal symmetry breaking $p+ip$ and $d+id$ pairings show half-quantized and quantized $\kappa_{xy}$ [in units of $\kappa_0=\pi^2 k^2_B T/(3h)$], respectively, while the latter one in addition accommodates a quantized $\sigma^{sp}_{xy}$ [in units of $\sigma^{sp}_0=\hbar/(8 \pi)$]. By contrast, the time-reversal invariant $p \pm ip$ pairing only displays a quantized $G^{th}_{xx}$ at low $T$ up to a moderate strength of disorder. In the strong disorder regime, all these topological responses ($\kappa_{xy}$, $\sigma^{sp}_{xy}$ and $G^{th}_{xx}$) vanish. Possible material platforms hosting such paired states and manifesting these robust topological thermal and spin responses are highlighted.
Efficacy of two diode lasers in the removal of calculus from the root surface: An in vitro study

Scaling and root planning (SRP) is still the gold standard of nonsurgical periodontal therapy, and it has been accompanied by several supportive therapies in recent years. One of the most studied methods is the diode laser, thanks to its thermal and bactericidal properties. Our trial intended to verify whether it could influence the chemical bond between calculus and the root surface.
Inference of dynamic hypergraph representations in temporal interaction data

A range of systems across the social and natural sciences generate datasets consisting of interactions between two distinct categories of items at various instances in time. Online shopping, for example, generates purchasing events of the form (user, product, time of purchase), and mutualistic interactions in plant-pollinator systems generate pollination events of the form (insect, plant, time of pollination). These data sets can be meaningfully modeled as temporal hypergraph snapshots in which multiple items within one category (i.e. online shoppers) share a hyperedge if they interacted with a common item in the other category (i.e. purchased the same product) within a given time window, allowing for the application of hypergraph analysis techniques. However, it is often unclear how to choose the number and duration of these temporal snapshots, which have a strong influence on the final hypergraph representations. Here we propose a principled nonparametric solution to this problem by extracting temporal hypergraph snapshots that optimally capture structural regularities in temporal event data according to the minimum description length principle. We demonstrate our methods on real and synthetic datasets, finding that they can recover planted artificial hypergraph structure in the presence of considerable noise and reveal meaningful activity fluctuations in human mobility data.
Why Thermodynamic Entropy And Statistical Entropy Are Two Different Physical Quantities

Historically, the coincidence of classical thermodynamic entropy and statistical entropy is based on an assumption (the so-called Boltzmann-Planck relation). The study shows that the Boltzmann-Planck relation is not valid in general. The study demonstrates that thermodynamic entropy is a physical entity distinct from statistical entropy. Initially, the entropy of ideal gases was examined and demonstrated to be independent of the gas volume. The ideal gas's true entropy was then expressed correctly. The Boltzmann-Planck entropy equation is demonstrated to be incapable of describing the thermodynamic entropy of ideal gases. In general, the Boltzmann-Planck entropy cannot be considered a statistical interpretation of the thermodynamic entropy. The two entropies represent two different physical quantities. The physical quantity that enters the second law of thermodynamics is the thermodynamic entropy, not the statistical entropy. The study advocates for a fundamental rethinking of today's statistical understanding of thermodynamics.
Multiple imputation for non-monotone missing not at random data using the no self-censoring model

Although approaches for handling missing data from longitudinal studies are well-developed when the patterns of missingness are monotone, fewer methods are available for non-monotone missingness. Moreover, the conventional missing at random assumption—a natural benchmark for monotone missingness—does not model realistic beliefs about the non-monotone missingness processes (Robins and Gill, 1997). This has provided the impetus for alternative non-monotone missing not at random mechanisms. The “no self-censoring” model is such a mechanism and assumes the probability an outcome variable is missing is independent of its value when conditioning on all other possibly missing outcome variables and their missingness indicators. As an alternative to “weighting” methods that become computationally demanding with increasing number of outcome variables, we propose a multiple imputation approach under no self-censoring. We focus on the case of binary outcomes and present results of simulation and asymptotic studies to investigate the performance of the proposed imputation approach. We describe a related approach to sensitivity analysis to departure from no self-censoring. We discuss the relationship between missing at random and no self-censoring and prove that one is not a special case of the other. Finally, we discuss extensions to non-binary data settings. The proposed methods are illustrated with application to a substance use disorder clinical trial.
Special geometry, quasi-modularity and attractor flow for BPS structures

We study mathematical structures on the moduli spaces of BPS structures of $\mathcal{N}=2$ theories. Guided by the realization of BPS structures within type IIB string theory on non-compact Calabi-Yau threefolds, we develop a notion of BPS variation of Hodge structure which gives rise to special K\"ahler geometry as well as to Picard-Fuchs equations governing the central charges of the BPS structure. We focus our study on cases with complex one dimensional moduli spaces and charge lattices of rank two including Argyres-Douglas $A_2$ as well as Seiberg-Witten $SU(2)$ theories. In these cases the moduli spaces are identified with modular curves and we determine the expressions of the central charges in terms of quasi-modular forms of the corresponding duality groups. We furthermore determine the curves of marginal stability and study the attractor flow in these examples, showing that it provides another way of determining the complete BPS spectrum in these cases.
Inter-finger Small Object Manipulation With DenseTact Optical Tactile Sensor

The ability to grasp and manipulate small objects in cluttered environments remains a significant challenge. This letter introduces a novel approach that utilizes a tactile sensor-equipped gripper with eight degrees of freedom to overcome these limitations. We employ DenseTact 2.0 for the gripper, enabling precise control and improved grasp success rates, particularly for small objects ranging from 5 mm to 25 mm. Our integrated strategy incorporates the robot arm, gripper, and sensor to manipulate and orient small objects for subsequent classification, effectively. We contribute a specialized dataset designed for classifying these objects based on tactile sensor output and a new control algorithm for in-hand orientation tasks. Our system demonstrates 88% of successful grasp and successfully classified small objects in cluttered scenarios.
Subscriptions and external links help drive resentful users to alternative and extremist YouTube channels

Do online platforms facilitate the consumption of potentially harmful content? Using paired behavioral and survey data provided by participants recruited from a representative sample in 2020 (n = 1181), we show that exposure to alternative and extremist channel videos on YouTube is heavily concentrated among a small group of people with high prior levels of gender and racial resentment. These viewers often subscribe to these channels (prompting recommendations to their videos) and follow external links to them. In contrast, nonsubscribers rarely see or follow recommendations to videos from these channels. Our findings suggest that YouTube’s algorithms were not sending people down “rabbit holes” during our observation window in 2020, possibly due to changes that the company made to its recommender system in 2019. However, the platform continues to play a key role in facilitating exposure to content from alternative and extremist channels among dedicated audiences.
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks

Our study reveals new theoretical insights into over-smoothing and feature over-correlation in deep graph neural networks. We show the prevalence of invariant subspaces, demonstrating a fixed relative behavior that is unaffected by feature transformations. Our work clarifies recent observations related to convergence to a constant state and a potential over-separation of node states, as the amplification of subspaces only depends on the spectrum of the aggregation function. In linear scenarios, this leads to node representations being dominated by a low-dimensional subspace with an asymptotic convergence rate independent of the feature transformations. This causes a rank collapse of the node representations, resulting in over-smoothing when smooth vectors span this subspace, and over-correlation even when over-smoothing is avoided. Guided by our theory, we propose a sum of Kronecker products as a beneficial property that can provably prevent over-smoothing, over-correlation, and rank collapse. We empirically extend our insights to the non-linear case, demonstrating the inability of existing models to capture linearly independent features.
DCAlign v1.0: aligning biological sequences using co-evolution models and informed priors

Abstract Summary DCAlign is a new alignment method able to cope with the conservation and the co-evolution signals that characterize the columns of multiple sequence alignments of homologous sequences. However, the pre-processing steps required to align a candidate sequence are computationally demanding. We show in v1.0 how to dramatically reduce the overall computing time by including an empirical prior over an informative set of variables mirroring the presence of insertions and deletions. Availability and implementation DCAlign v1.0 is implemented in Julia and it is fully available at https://github.com/infernet-h2020/DCAlign.
Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents

Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current policy research has insufficiently assessed urban form effects on car travel at three levels: (1) Causality -- Can causality be established beyond theoretical and correlation-based analyses? (2) Generalizability -- Do relationships hold across different cities and world regions? (3) Context specificity -- How do relationships vary across neighborhoods of a city? Here, we address all three gaps via causal graph discovery and explainable machine learning to detect urban form effects on intra-city car travel, based on mobility data of six cities across three continents. We find significant causal effects of urban form on trip emissions and inter-feature effects, which had been neglected in previous work. Our results demonstrate that destination accessibility matters most overall, while low density and low connectivity also sharply increase CO$_2$ emissions. These general trends are similar across cities but we find idiosyncratic effects that can lead to substantially different recommendations. In more monocentric cities, we identify spatial corridors -- about 10--50 km from the city center -- where subcenter-oriented development is more relevant than increased access to the main center. Our work demonstrates a novel application of machine learning that enables new research addressing the needs of causality, generalizability, and contextual specificity for scaling evidence-based urban climate solutions.
Shedding light on emerging therapeutic targets for chordoma

ABSTRACT Introduction Despite encouraging advances in radiation and surgical treatment, chordomas remain resistant to chemotherapy and local recurrence is common. Although the primary mechanism of recurrence is local, metastatic disease occurs in a small subset of patients. Recurrence may also occur along the surgical trajectory if care is not taken to fully excise the open biopsy pathway. There is increasing morbidity with reoperation upon disease recurrence, and radiation is an option for cytoreduction in primary disease or for recurrent disease, although toxicity may be observed with high-dose therapies. Given these challenges, targeted chemotherapeutic agents for postoperative adjuvant treatment are needed. Areas covered In this review, we summarize the genetic drivers of chordoma and the state of the current research in chordoma immunotherapy and epigenetics. Expert opinion Chordoma is a heterogenous tumor that should be targeted from different angles and the study of its characteristics, from molecular to immunological to epigenetic, is necessary. Combining different approaches, such as studying noninvasive patient methylation patterns with tissue-based molecular and drug screening, can transform patient care by guiding treatment decisions based on prognostic mechanisms from different sources, while helping individualize surgical planning and treatment.
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization

This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization. Although the current versions of ADMM algorithm provide promising numerical results in producing solutions that are close to optimal for many convex and non-convex optimization problems, it remains unclear if they can converge to a stationary point for weakly convex and locally non-smooth functions. Through our analysis using the Moreau envelope function, we demonstrate that MADM can indeed converge to a stationary point under mild conditions. Our analysis also includes computing the bounds on the amount of change in the dual variable update step by relating the gradient of the Moreau envelope function to the proximal function. Furthermore, the results of our numerical experiments indicate that our method is faster and more robust than widely-used approaches.
Hydrothermal enrichment of lithium in intracaldera illite-bearing claystones

Developing a sustainable supply chain for the global proliferation of lithium ion batteries in electric vehicles and grid storage necessitates the extraction of lithium resources that minimize local environmental impacts. Volcano sedimentary lithium resources have the potential to meet this requirement, as they tend to be shallow, high-tonnage deposits with low waste:ore strip ratios. Illite-bearing Miocene lacustrine sediments within the southern portion of McDermitt caldera (USA) at Thacker Pass contain extremely high lithium grades (up to ~1 weight % of Li), more than double the whole-rock concentration of lithium in smectite-rich claystones in the caldera and other known claystone lithium resources globally (<0.4 weight % of Li). Illite concentrations measured in situ range from ~1.3 to 2.4 weight % of Li within fluorine-rich illitic claystones. The unique lithium enrichment of illite at Thacker Pass resulted from secondary lithium- and fluorine-bearing hydrothermal alteration of primary neoformed smectite-bearing sediments, a phenomenon not previously identified.
Learning to Taste: A Multimodal Wine Dataset

We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor. The dataset encompasses 897k images of wine labels and 824k reviews of wines curated from the Vivino platform. It has over 350k unique bottlings, annotated with year, region, rating, alcohol percentage, price, and grape composition. We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment with 256 participants who were asked to rank wines based on their similarity in flavor, resulting in more than 5k pairwise flavor distances. We propose a low-dimensional concept embedding algorithm that combines human experience with automatic machine similarity kernels. We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and aligns with the intricate human perception of flavor.