Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic Neural Fields: Learning Functions on Manifolds

Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative modeling. Some of their advantages are a sound theoretic foundation and an easy implementation in current deep learning frameworks. While neural fields have been applied to signals on manifolds, e.g., for texture reconstruction, their representation has been limited to extrinsically embedding the shape into Euclidean space. The extrinsic embedding ignores known intrinsic manifold properties and is inflexible wrt. transfer of the learned function. To overcome these limitations, this work introduces intrinsic neural fields, a novel and versatile representation for neural fields on manifolds. Intrinsic neural fields combine the advantages of neural fields with the spectral properties of the Laplace-Beltrami operator. We show theoretically that intrinsic neural fields inherit many desirable properties of the extrinsic neural field framework but exhibit additional intrinsic qualities, like isometry invariance. In experiments, we show intrinsic neural fields can reconstruct high-fidelity textures from images with state-of-the-art quality and are robust to the discretization of the underlying manifold. We demonstrate the versatility of intrinsic neural fields by tackling various applications: texture transfer between deformed shapes&different shapes, texture reconstruction from real-world images with view dependence, and discretization-agnostic learning on meshes and point clouds.
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization

The expected improvement (EI) algorithm is one of the most popular strategies for optimization under uncertainty due to its simplicity and efficiency. Despite its popularity, the theoretical aspects of this algorithm have not been properly analyzed. In particular, whether in the noisy setting, the EI strategy with a standard incumbent converges is still an open question of the Gaussian process bandit optimization problem. We aim to answer this question by proposing a variant of EI with a standard incumbent defined via the GP predictive mean. We prove that our algorithm converges, and achieves a cumulative regret bound of $\mathcal O(\gamma_T\sqrt{T})$, where $\gamma_T$ is the maximum information gain between $T$ observations and the Gaussian process model. Based on this variant of EI, we further propose an algorithm called Improved GP-EI that converges faster than previous counterparts. In particular, our proposed variants of EI do not require the knowledge of the RKHS norm and the noise's sub-Gaussianity parameter as in previous works. Empirical validation in our paper demonstrates the effectiveness of our algorithms compared to several baselines.
Radio Analysis of SN2004C Reveals an Unusual CSM Density Profile as a Harbinger of Core Collapse

We present extensive multifrequency Karl G. Jansky Very Large Array (VLA) and Very Long Baseline Array (VLBA) observations of the radio-bright supernova (SN) IIb SN 2004C that span ∼40–2793 days post-explosion. We interpret the temporal evolution of the radio spectral energy distribution in the context of synchrotron self-absorbed emission from the explosion’s forward shock as it expands in the circumstellar medium (CSM) previously sculpted by the mass-loss history of the stellar progenitor. VLBA observations and modeling of the VLA data point to a blastwave with average velocity ∼0.06 c that carries an energy of ≈1049 erg. Our modeling further reveals a flat CSM density profile ρ CSM ∝ R −0.03±0.22 up to a break radius R br ≈ (1.96 ± 0.10) × 1016 cm, with a steep density gradient following ρ CSM ∝ R −2.3±0.5 at larger radii. We infer that the flat part of the density profile corresponds to a CSM shell with mass ∼0.021 M ☉, and that the progenitor’s effective mass-loss rate varied with time over the range (50–500) × 10−5 M ☉ yr−1 for an adopted wind velocity v w = 1000 km s−1 and shock microphysical parameters ϵ e = 0.1, ϵ B = 0.01. These results add to the mounting observational evidence for departures from the traditional single-wind mass-loss scenarios in evolved, massive stars in the centuries leading up to core collapse. Potentially viable scenarios include mass loss powered by gravity waves and/or interaction with a binary companion.
Reinforcement Learning for Optimal Control of a District Cooling Energy Plant

District cooling energy plants (DCEPs) consisting of chillers, cooling towers, and thermal energy storage (TES) systems consume a considerable amount of electricity. Optimizing the scheduling of the TES and chillers to take advantage of time-varying electricity price is a challenging optimal control problem. The classical method, model predictive control (MPC), requires solving a high dimensional mixed-integer nonlinear program (MINLP) because of the on/off actuation of the chillers and charge/discharge of TES, which are computationally challenging. RL is an attractive alternative: the real time control computation is a low-dimensional optimization problem that can be easily solved. However, the performance of an RL controller depends on many design choices.In this paper, we propose a Q-learning based reinforcement learning (RL) controller for this problem. Numerical simulation results show that the proposed RL controller is able to reduce energy cost over a rule-based baseline controller by approximately 8%, comparable to savings reported in the literature with MPC for similar DCEPs. We describe the design choices in the RL controller, including basis functions, reward function shaping, and learning algorithm parameters. Compared to existing work on RL for DCEPs, the proposed controller is designed for continuous state and actions spaces.
A negative binomial approximation in group testing

We consider the problem of group testing (pooled testing), first introduced by Dorfman. For nonadaptive testing strategies, we refer to a nondefective item as “intruding” if it only appears in positive tests. Such items cause misclassification errors in the well-known COMP algorithm and can make other algorithms produce an error. It is therefore of interest to understand the distribution of the number of intruding items. We show that, under Bernoulli matrix designs, this distribution is well approximated in a variety of senses by a negative binomial distribution, allowing us to understand the performance of the two-stage conservative group testing algorithm of Aldridge.
Practical data monitoring in the internet-services domain

Large-scale monitoring, anomaly detection, and root cause analysis of metrics are essential requirements of the internet-services industry. To address the need to continuously monitor millions of metrics, many anomaly detection approaches are being used on a daily basis by large internet-based companies. However, in spite of the significant progress made to accurately and efficiently detect anomalies in metrics, the sheer scale of the number of metrics has meant there are still a large number of false alarms that need to be investigated. This paper presents a framework for reliable large-scale anomaly detection. It is significantly more accurate than existing approaches and allows for easy interpretation of models, thus enabling practical data monitoring in the internet-services domain.
ON NONMONOGENIC NUMBER FIELDS DEFINED BY TRINOMIALS OF TYPE xn+axm+b

Let $K=\Q(\theta)$ be a number field generated by a complex root $\th$ of a monic irreducible trinomial $F(x) = x^n+ax^{m}+b \in \Z[x]$. In this paper, we deal with the problem of the non-monogenity of $K$. More precisely, we provide some explicit conditions on $a$, $b$, $n$, and $m$ for which $K$ is not monogenic. As application, we show that there are infinite families of non-monogenic number fields defined by trinomials of degree $n=2^r\cdot3^k$ with $r$ and $k$ are positive integers. We also give two infinite families of non-monogenic number fields defined by trinomials of degree $6$. Finally, we illustrate our results by giving some examples.
Incomplete Knowledge Graph Alignment

Knowledge graph (KG) alignment - the task of recognizing entities referring to the same thing in different KGs - is recognized as one of the most important operations in the field of KG construction and completion. However, existing alignment techniques often assume that the input KGs are complete and isomorphic, which is not true due to the real-world heterogeneity in the domain, size, and sparsity. In this work, we address the problem of aligning incomplete KGs with representation learning. Our KG embedding framework exploits two feature channels: transitivity-based and proximity-based. The former captures the consistency constraints between entities via translation paths, while the latter captures the neighbourhood structure of KGs via attention guided relation-aware graph neural network. The two feature channels are jointly learned to exchange important features between the input KGs while enforcing the output representations of the input KGs in the same embedding space. Also, we develop a missing links detector that discovers and recovers the missing links in the input KGs during the training process, which helps mitigate the incompleteness issue and thus improve the compatibility of the learned representations. The embeddings then are fused to generate the alignment result, and the high-confidence matched node pairs are updated to the pre-aligned supervision data to improve the embeddings gradually. Empirical results show that our model is more accurate than the SOTA and is robust against different levels of incompleteness.
Development of simplified design techniques for estimating undrained bearing loads of piled-raft in soft clay

Many structures founded on pile-raft foundations on soft clay in Singapore has been conservatively designed without considering the contribution from the raft due to the lack of knowledge (and thus design confidence) on the soil-structure interaction, bearing capacity of foundation against settlement. This generally resulted in very expensive foundation costs particularly in the soft clay conditions. In view of the increasingly high prices of construction materials globally, it is timely to look into the development of an optimum design that provides safe, lasting and leaner design, allows use of resources effectively, economically and yet performing pile-raft foundation system efficiently in the soft clay condition. To achieve these, a thorough understanding of the soil-structure interaction, bearing capacity and settlement behaviours of piled-raft foundations in soft clay are of paramount importance. This research is identified as being of importance to practical civil and structure engineers in providing them with simplified techniques to perform a quick preliminary assessment and design of piled-raft foundation, before carrying out complicated and time-consuming 3-D finite element analyses. This is particularly useful when a quick assessment of the feasibility and construction costs of piled-raft foundation is required during the preliminary design stage.
Scientometric Analysis of Interdisciplinary Collaboration and Gender Trends in 30 Years of IEEE VIS Publications

We present the results of a scientometric analysis of 30 years of IEEE VIS publications between 1990-2020, in which we conducted a multifaceted analysis of interdisciplinary collaboration and gender composition among authors. To this end, we curated BiblioVIS, a bibliometric dataset that contains rich metadata about IEEE VIS publications, including 3032 papers and 6113 authors. One of the main factors differentiating BiblioVIS from similar datasets is the authors' gender and discipline data, which we inferred through iterative rounds of computational and manual processes. Our analysis shows that, by and large, inter-institutional and interdisciplinary collaboration has been steadily growing over the past 30 years. However, interdisciplinary research was mainly between a few fields, including Computer Science, Engineering and Technology, and Medicine and Health disciplines. Our analysis of gender shows steady growth in women's authorship. Despite this growth, the gender distribution is still highly skewed, with men dominating (~75%) of this space. Our predictive analysis of gender balance shows that if the current trends continue, gender parity in the visualization field will not be reached before the third quarter of the century (~2070). Our primary goal in this work is to call the visualization community's attention to the critical topics of collaboration, diversity, and gender. Our research offers critical insights through the lens of diversity and gender to help accelerate progress towards a more diverse and representative research community.
Improving the photoresponse performance of monolayer MoS2 photodetector via local flexoelectric effect

Strain engineering is an effective means of modulating the optical and electrical properties of two-dimensional materials. The flexoelectric effect caused by inhomogeneous strain exists in most dielectric materials, which breaks the limit of the materials’ non-centrosymmetric structure for piezoelectric effect. However, there is a lack of understanding of the impact on optoelectronic behaviour of monolayer MoS2 photodetector via local flexoelectric effect triggered by biaxial strain. In this paper, we develop a probe tip (Pt)-MoS2-Au asymmetric Schottky barrier photodetector based on conductive atomic force microscopy to investigate the impact of flexoelectric effect on the photoresponse performance. Consequently, when the probe force increases from 24 nN to 720 nN, the photocurrent, responsivity and detectivity increase 28.5 times, 29.6 times and 5.3 times at forward bias under 365 nm light illumination, respectively. These results indicate that local flexoelectric effect plays a critical role to improve the photoresponse performance of photodetector. Our approach suggests a new route to improve the performance of photodetectors by introducing local flexoelectric polarization field, offering the potential for the application of strain modulated photoelectric devices.
A systematic review of sociodemographic reporting and representation in eating disorder psychotherapy treatment trials in the United States

Abstract Objective Eating disorders (EDs) were once conceptualized as primarily affecting affluent, White women, a misconception that informed research and practice for many years. Abundant evidence now discredits this stereotype, but it is unclear if prevailing “evidence‐based” treatments have been evaluated in samples representative of the diversity of individuals affected by EDs. Our goal was to evaluate the reporting, inclusion, and analysis of sociodemographic variables in ED psychotherapeutic treatment randomized controlled trials (RCTs) in the US through 2020. Methods We conducted a systematic review of ED psychotherapeutic treatment RCTs in the US and examined the reporting and inclusion of gender identity, age, race/ethnicity, sexual orientation, and socioeconomic status (SES) of enrolled participants, as well as recruitment methods, power analyses, and discussion of limitations and generalizability. Results Our search yielded 58 studies meeting inclusion criteria dating back to 1985. Reporting was at times incomplete, absent, or centered on the racial/gender majority group. No studies reported gender diverse participants, and men and people of color were underrepresented generally, with differences noted across diagnoses. A minority of papers considered sociodemographic variables in analyses or acknowledged limitations related to sample characteristics. Some progress was made across the decades, with studies increasingly providing full racial and ethnic data, and more men included over time. Although racial and ethnic diversity improved somewhat, progress appeared to stall in the last decade. Discussion We summarize findings, consider context and challenges for RCT researchers, and offer suggestions for researchers, journal editors, and reviewers on improving representation, reporting, and analytic practices. Public Significance Randomized controlled trials of eating disorder psychotherapeutic treatment in the US are increasingly reporting full race/ethnicity data, but information on SES is inconsistent and sexual orientation absent. White women still comprise the overwhelming majority of participants, with few men and people of color, and no gender‐diverse individuals. Findings underscore the need to improve reporting and increase representation to ensure evidence‐based treatments are effective across and within diverse groups.
A Simulation-Based Transfer Function Modeling Approach for Responsive Production Planning

A Simulation-Based Transfer Function Modeling Approach for Responsive Production Planning Jingang Liu In this thesis, a novel statistical metamodeling method is proposed to study the inputoutput dynamic relations of general queueing system, especially when the system going through transient state. This metamodeling approach incorporates discrete even simulation, statistical inference, analytical queueing analysis to estimate a set of transfer function models (TFMs), which fully describe the system dynamic in terms of outputs that are interested : first and second moment of work-in-process (WIP) and first moment of departure rate. Empirical queueing examples with non-Markov property are studied, including single station-single server system, single-station multi-server with failure system and multi-station multi-server with reentrant flow system and failure, following the proposed approach. TFMs for such systems are obtained. Predictions of the system dynamics is estimated from these TFMs under given system. Cross validation of the predicted outputs with simulation results show that the proposed approach is very accurate in describing the system evolution under both transient and steady states of general queueing system. Also, the proposed TFMs has been applied to the general Jackson-network models, cross validation result shows that the estimation results are promising as well. The above proposed TFMs is integrated into a production planning model for a 5 station in-tandem system with reentrant flow and machine failures. The demands are independent and random with mean and distribution known. The production planning model estimates both mean and variance of the expected total cost based on TFMs. Genetic algorithm (GA) is employed to find the Pareto Front of such a production planning model. Objectives from these Pareto Front points are compared with simulation results and proved the accuracy of the production planning model based on TFMs.
Concurrent and longitudinal contributions of phonological awareness to early adolescent Chinese reading acquisition

Abstract This study investigated universal and language-specific phonological awareness in reading development among Chinese early adolescent students. Seventy-six children participated in this study and completed a series of reading tasks at two data collection points across Grades 5 and 6. In Grade 5, universal phonological awareness (syllable, onset, rhyme, and phoneme awareness), language-specific phonological awareness (tone awareness) as well as character recognition and production measurements were administered to the participants. Lexical inferencing ability was measured in Grade 6. Character recognition and lexical inference were coded as the outcome variables. Subsequent multiple regression analyses showed that Time 1 (Grade 5) language-universal onset and phoneme awareness predicted character recognition and production at Time 1. More strikingly, the study demonstrated that language-specific tone awareness exerted a longitudinal effect on later lexical inferencing ability after controlling for age and nonverbal intelligence. Results underscored both the universality and language specificity of phonological awareness and provided empirical evidence to substantiate the facilitative role of early language-specific psycholinguistic grain size in later reading performance.
Benefit from successful recanalization in an Italian cohort of stroke patients receiving endovascular treatments according to the DIRECT-MT trial criteria

Introduction To identify predictors of 3-month mRS score and to estimate the benefit from successful recanalization across baseline subgroups of Italian Registry of Endovascular Treatment in Acute Stroke (IRETAS) cohort of stroke patients receiving endovascular treatments according to the DIRECT-MT criteria. Methods Using a model of propensity score matching, we retrospectively identified an IRETAS cohort of 137 patients receiving bridging who were matched with 137 patients receiving MT alone according to the DIRECT-MT criteria. Results Differences were found between DIRECT-MT and IRETAS cohorts for 3-month mRS score 0 to 1 (23.5% vs. 33.1%) and 0 to 2 (36.7% vs. 47.1%), successful (82% vs. 76.7%) and complete recanalization (32.3% vs. 58.8%). Among unfavorable predictors for 3-month mRS shift, diabetes mellitus (18.9% vs. 13.9%) and asymptomatic intracerebral hemorhage (ICH) (34.8% vs. 25.5%) were more frequent in the DIRECT-MT, whereas age ≥80 years (23.7% vs. 15.3%) and pre-stroke mRS score >0 (16.1% vs. 7.8%) were more frequent in the IRETAS. The direction of effect on the 3-month mRS shift (6 to 0) favored successful recanalization across all strata. Greatest benefit from successful recanalization was observed in patients with most severe strokes (NIHSS ≥20, OR:4.002; 16–19, OR:3.292; 2–5, OR:2.470) and most proximal occlusion site (intra-cranial ICA, OR:4.092; M1-MCA, OR:3.705; M2-MCA, OR:2.001), in younger patients (18–59 years, OR:3.677; 60–79, OR:3.267; ≥80, OR:1.993), and in patients who started the treatment earlier (onset-to-groin time ≤205 min, OR:4.361; onset-to-groin time >205, OR:2.326). Conclusions The benefit from successful recanalization for 3-month mRS shift in the direction of favorable outcome was different across baseline subgroups.
Assessment of a Crisis Standards of Care Scoring System for Resource Prioritization and Estimated Excess Mortality by Race, Ethnicity, and Socially Vulnerable Area During a Regional Surge in COVID-19

Key Points Question Is a crisis standards of care scoring system designed to allocate scarce resources in the COVID-19 pandemic associated with inequities in resource allocation by race? Findings In this cohort study of 498 adults admitted to the intensive care unit and preemptively scored during a COVID-19 surge, nearly twice the proportion of Black patients were scored in the lowest priority group compared with all other patients, a significant difference. Meaning These findings suggest that a scoring system designed to maximize lives and life-years saved in the setting of resource scarcity during the COVID-19 pandemic may result in racial inequities in prioritization.