Miracle-dz/UniFAR-FTUs huggingface dataset:
Beluga whale distribution, migration, and behavior in a changing Pacific Arctic

Beluga whale distribution, migration, and behavior in a changing Pacific Arctic Donna Dora Westley Hauser Chair of the Supervisory Committee: Assistant Professor Kristin L. Laidre School of Aquatic and Fishery Sciences and Applied Physics Laboratory Sea ice is disappearing at unprecedented rates in the Pacific Arctic with potential impacts to iceassociated marine predators that migrate to this seasonally accessible and productive ecosystem. In this dissertation I used satellite telemetry data spanning 1993-2012 collected from two migratory populations of beluga whales (Delphinapterus leucas) in the Pacific Arctic (i.e., Eastern Chukchi Sea and Eastern Beaufort Sea populations) to investigate how loss of sea ice and changes in other environmental factors affect distribution, movement, and behavior. I quantified fidelity to summer areas, sexual segregation, and migration timing as well as variations in diving behavior among regions. These analyses illustrate that population-scale patterns of philopatry, migration, and foraging are mediated by the combined effects of seasonal sea ice and oceanographic fluctuations, prey distribution, and social interactions. I also addressed the question of whether belugas would adjust their distribution, migration, and behavior to shifting sea ice conditions and to what extent matrilineally-learned behavior might supersede environmental forcing through the development of resource selection functions. Results indicate that sea ice is a contributing factor but not sole determinant of beluga habitat preferences. One population (Eastern Chukchi Sea) exhibits delayed fall migration in response to later sea ice freeze-up. Changing environmental conditions also seem to favor deeper, longer dives for this population. There were few overall differences in preferred habitat selection during 1990-2014, and summer distribution appears to be governed by philopatry rather than ice conditions. These results correspond to a conclusion that Eastern Chukchi Sea belugas are responding to a changing Pacific Arctic environment through behavioral plasticity in migration timing and foraging behavior. In contrast, there were few examples where migration timing or sea ice associations of Eastern Beaufort Sea belugas changed between the 1990s and 2000s. Taken as a whole, these results suggest population-specific responses by belugas in the face of fluctuating sea ice conditions. Across the circumpolar Arctic, some beluga populations may be more likely than others to adapt and persist in a changing climate.
Disease signatures in the gut metagenome of a prospective family cohort of inflammatory bowel disease

Inflammatory bowel disease (IBD) is associated with dysbiotic microbiomes. However, whether microbiomes of family members of IBD patients harbour microbial disease signatures of IBD is unknown. Here, we generate shotgun metagenomic data of an IBD family cohort and treatment-naive IBD cases, which we combine with published IBD metagenomes, to perform a meta-analysis of IBD-microbiome associations. Our study reveals microbial shifts that are specific to IBD or IBD subtypes (Crohns disease and ulcerative colitis). We find that IBD cohorts share signatures of association with disease irrespective of geography, and we report novel species-level identifications of microbial taxa that are universal markers of Crohns disease. We further demonstrate that the microbiome of healthy family members at high risk for IBD represent transitional states between the microbiome of unrelated healthy controls and IBD cases, and harbor diversity, compositional and ecological features that are also observed among IBD microbiomes. Overall, our study provides valuable insights into the intricate relationship between the gut microbiome and IBD, with implications for better predicting disease onset among individuals with high susceptibility for IBD.
Digital Trust and Peer-to-Peer Collaborative Consumption Platforms: A Mediation Analysis

This paper investigates the nature of digital trust in the context of P2P collaborative consumption platforms. We have analyzed data from the website Trustpilot (N=5,606), survey data from users of the online sharing platform Airbnb (N=232), and data retrieved from an online experiment conducted among current non-users of a fictitious online sharing platform (N=462). The findings reveal that trust in P2P collaborative consumption platforms (Airbnb, Lyft, and Uber) is lower than trust in P2P exchange first generation platforms (Ebay), as well as large online retail services and non-P2P platforms (Walmart, Zappos, Amazon). Furthermore, we find that the three trust-building management measures: ‘reliable insurance cover’, ‘simultaneous reviews’, and a ‘large network: many offers worldwide’ had a positive effect on ‘trust in the platform provider’. The findings confirm the hierarchical nature of the two-fold trust construct, meaning that ‘trust in the platform provider’ has a positive effect on the ‘trust in peers’ sharing on this platform. A mediation analysis reveals that ‘trust in the platform’ fully mediates all statistically significant effects of trust-building measures on the ‘trust in peers’ variable.
AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic information from a style effectively or suffer from high computational costs and inefficiencies in feature disentanglement due to using pre-trained models. This work proposes a lightweight but effective model, AesFA---Aesthetic Feature-Aware NST. The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely. To improve the network's ability to extract more distinct representations and further enhance the stylization quality, this work introduces a new aesthetic feature: contrastive loss. Extensive experiments and ablations show the approach not only outperforms recent NST methods in terms of stylization quality, but it also achieves faster inference. Codes are available at https://github.com/Sooyyoungg/AesFA.
Learning Naturally Aggregated Appearance for Efficient 3D Editing

Neural radiance fields, which represent a 3D scene as a color field and a density field, have demonstrated great progress in novel view synthesis yet are unfavorable for editing due to the implicitness. In view of such a deficiency, we propose to replace the color field with an explicit 2D appearance aggregation, also called canonical image, with which users can easily customize their 3D editing via 2D image processing. To avoid the distortion effect and facilitate convenient editing, we complement the canonical image with a projection field that maps 3D points onto 2D pixels for texture lookup. This field is carefully initialized with a pseudo canonical camera model and optimized with offset regularity to ensure naturalness of the aggregated appearance. Extensive experimental results on three datasets suggest that our representation, dubbed AGAP, well supports various ways of 3D editing (e.g., stylization, interactive drawing, and content extraction) with no need of re-optimization for each case, demonstrating its generalizability and efficiency. Project page is available at https://felixcheng97.github. io/AGAP/.
Adversarial Phase Retrieval via Nonlinear Least Absolute Deviation

We investigate the phase retrieval problem perturbed by dense bounded noise and sparse outliers that can change an adversarially chosen $s$-fraction of the measurement vector. The adversarial sparse outliers may exhibit dependence on both the observation and the measurement. We demonstrate that the nonlinear least absolute deviation based on amplitude measurement can tolerate adversarial outliers at a fraction of $s^{*,1}\approx0.2043$, while the intensity-based model can tolerate a fraction of $s^{*,2}\approx0.1185$. Furthermore, we construct adaptive counterexamples to show that the thresholds are theoretically sharp, thereby showing the presentation of phase transition in the adversarial phase retrieval problem when the corruption fraction exceeds the sharp thresholds. This implies that the amplitude-based model exhibits superior adversarial robustness in comparison with the intensity-based model. Corresponding experimental results are presented to further illustrate our theoretical findings. To the best of our knowledge, our results provide the first theoretical examination of the distinction in robustness performance between amplitude and intensity measurement. A crucial point of our analysis is that we explore the exact distribution of some combination of two non-independent Gaussian random variables and present the novel probability density functions to derive the sharp thresholds.
SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models

A new method called the Survival Beran-based Neural Importance Model (SurvBeNIM) is proposed. It aims to explain predictions of machine learning survival models, which are in the form of survival or cumulative hazard functions. The main idea behind SurvBeNIM is to extend the Beran estimator by incorporating the importance functions into its kernels and by implementing these importance functions as a set of neural networks which are jointly trained in an end-to-end manner. Two strategies of using and training the whole neural network implementing SurvBeNIM are proposed. The first one explains a single instance, and the neural network is trained for each explained instance. According to the second strategy, the neural network only learns once on all instances from the dataset and on all generated instances. Then the neural network is used to explain any instance in a dataset domain. Various numerical experiments compare the method with different existing explanation methods. A code implementing the proposed method is publicly available.
Delaunay Triangulations in the Hilbert Metric

The Hilbert metric is a distance function defined for points lying within the interior of a convex body. It arises in the analysis and processing of convex bodies, machine learning, and quantum information theory. In this paper, we show how to adapt the Euclidean Delaunay triangulation to the Hilbert geometry defined by a convex polygon in the plane. We analyze the geometric properties of the Hilbert Delaunay triangulation, which has some notable differences with respect to the Euclidean case, including the fact that the triangulation does not necessarily cover the convex hull of the point set. We also introduce the notion of a Hilbert ball at infinity, which is a Hilbert metric ball centered on the boundary of the convex polygon. We present a simple randomized incremental algorithm that computes the Hilbert Delaunay triangulation for a set of $n$ points in the Hilbert geometry defined by a convex $m$-gon. The algorithm runs in $O(n (\log n + \log^3 m))$ expected time. In addition we introduce the notion of the Hilbert hull of a set of points, which we define to be the region covered by their Hilbert Delaunay triangulation. We present an algorithm for computing the Hilbert hull in time $O(n h \log^2 m)$, where $h$ is the number of points on the hull's boundary.
Topological conditions for impurity effects in graphene nanosystems

We consider electronic spectra of graphene nanotubes and their perturbation by impurity atoms absorbed at different positions on nanotube surfaces, within the framework of Anderson hybrid model. A special attention is given to the cases when Dirac-like 1D modes appear in the nanotube spectrum and their hybridization with localized impurity states produces, at growing impurity concentration $c$, onset of a mobility gap near the impurity level and even opening, at yet higher $c$, of some narrow delocalized range within this mobility gap. Such behaviors are compared with the similar effects in the previously studied 2D graphene and armchair type graphene nanoribbons. Some possible practical applications are discussed.
On the Thermal Transport Puzzles in $\alpha\mbox{-}RuCl_3$

Thermal transport has been used to probe the nature of $\alpha\mbox{-}RuCl_3$, an important candidate of Kitaev material. Two remarkable observations were made under applied magnetic fields at low temperatures, and have stimulated extensive discussions. One is a sizable thermal Hall effect, and the other is an apparent ``oscillation'' of the longitudinal thermal conductivity with the magnetic field. It has been proposed that the former is due to a bosonic Chern band. Meanwhile, the origin of the latter has largely remained obscure. This work aims to resolve this ``oscillation'' puzzle. By examining the thermal transport data as well as other measured properties of $\alpha\mbox{-}RuCl_3$, we argue that the most plausible scenario is that of phonons scattering with spin degrees of freedom across multiple phases. We substantiate this picture into a phenomenological theory, which reproduces the ``oscillation'' behavior in a simple manner and makes predictions that can be examined by future experiments. Moreover, our phenomenological theory and the aforementioned proposal for the thermal Hall effect support each other. We hope this work can thus help settle the physical mechanism behind the thermal transport puzzles in $\alpha\mbox{-}RuCl_3$.
No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learning

The large action space is one fundamental obstacle to deploying Reinforcement Learning methods in the real world. The numerous redundant actions will cause the agents to make repeated or invalid attempts, even leading to task failure. Although current algorithms conduct some initial explorations for this issue, they either suffer from rule-based systems or depend on expert demonstrations, which significantly limits their applicability in many real-world settings. In this work, we examine the theoretical analysis of what action can be eliminated in policy optimization and propose a novel redundant action filtering mechanism. Unlike other works, our method constructs the similarity factor by estimating the distance between the state distributions, which requires no prior knowledge. In addition, we combine the modified inverse model to avoid extensive computation in high-dimensional state space. We reveal the underlying structure of action spaces and propose a simple yet efficient redundant action filtering mechanism named No Prior Mask (NPM) based on the above techniques. We show the superior performance of our method by conducting extensive experiments on high-dimensional, pixel-input, and stochastic problems with various action redundancy tasks. Our code is public online at https://github.com/zhongdy15/npm.
Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction

Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms. While continuous improvements have been made in domain-specific LSM, limited work has been done in cross-domain LSM, which considers modeling of lifelong sequences of both target domain and source domain. In this paper, we propose Lifelong Cross Network (LCN) to incorporate cross-domain LSM to improve the click-through rate (CTR) prediction in the target domain. The proposed LCN contains a LifeLong Attention Pyramid (LAP) module that comprises of three levels of cascaded attentions to effectively extract interest representations with respect to the candidate item from lifelong sequences. We also propose Cross Representation Production (CRP) module to enforce additional supervision on the learning and alignment of cross-domain representations so that they can be better reused on learning of the CTR prediction in the target domain. We conducted extensive experiments on WeChat Channels industrial dataset as well as on benchmark dataset. Results have revealed that the proposed LCN outperforms existing work in terms of both prediction accuracy and online performance.
VAE-IF: Deep feature extraction with averaging for unsupervised artifact detection in routine acquired ICU time-series

Artifacts are a common problem in physiological time-series data collected from intensive care units (ICU) and other settings. They affect the quality and reliability of clinical research and patient care. Manual annotation of artifacts is costly and time-consuming, rendering it impractical. Automated methods are desired. Here, we propose a novel unsupervised approach to detect artifacts in clinical-standard minute-by-minute resolution ICU data without any prior labeling or signal-specific knowledge. Our approach combines a variational autoencoder (VAE) and an isolation forest (iForest) model to learn features and identify anomalies in different types of vital signs, such as blood pressure, heart rate, and intracranial pressure. We evaluate our approach on a real-world ICU dataset and compare it with supervised models based on long short-term memory (LSTM) and XGBoost. We show that our approach achieves comparable sensitivity and generalizes well to an external dataset. We also visualize the latent space learned by the VAE and demonstrate its ability to disentangle clean and noisy samples. Our approach offers a promising solution for cleaning ICU data in clinical research and practice without the need for any labels whatsoever.
Stoch BiRo: Design and Control of a low cost bipedal robot

This paper introduces the Stoch BiRo, a cost-effective bipedal robot designed with a modular mechanical structure having point feet to navigate uneven and unfamiliar terrains. The robot employs proprioceptive actuation in abduction, hips, and knees, leveraging a Raspberry Pi4 for control. Overcoming computational limitations, a Learning-based Linear Policy controller manages balance and locomotion with only 3 degrees of freedom (DoF) per leg, distinct from the typical 5DoF in bipedal systems. Integrated within a modular control architecture, these controllers enable autonomous handling of unforeseen terrain disturbances without external sensors or prior environment knowledge. The robot's policies are trained and simulated using MuJoCo, transferring learned behaviors to the Stoch BiRo hardware for initial walking validations. This work highlights the Stoch BiRo's adaptability and cost-effectiveness in mechanical design, control strategies, and autonomous navigation, promising diverse applications in real-world robotics scenarios.
Precipitation Downscaling with Spatiotemporal Video Diffusion

In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statistical downscaling, or super-resolution, is a common workaround where a low-resolution prediction is improved using statistical approaches. Unlike traditional computer vision tasks, weather and climate applications require capturing the accurate conditional distribution of high-resolution given low-resolution patterns to assure reliable ensemble averages and unbiased estimates of extreme events, such as heavy rain. This work extends recent video diffusion models to precipitation super-resolution, employing a deterministic downscaler followed by a temporally-conditioned diffusion model to capture noise characteristics and high-frequency patterns. We test our approach on FV3GFS output, an established large-scale global atmosphere model, and compare it against five state-of-the-art baselines. Our analysis, capturing CRPS, MSE, precipitation distributions, and qualitative aspects using California and the Himalayas as examples, establishes our method as a new standard for data-driven precipitation downscaling.
Voice Activity Detection (VAD) in Noisy Environments

In the realm of digital audio processing, Voice Activity Detection (VAD) plays a pivotal role in distinguishing speech from non-speech elements, a task that becomes increasingly complex in noisy environments. This paper details the development and implementation of a VAD system, specifically engineered to maintain high accuracy in the presence of various ambient noises. We introduce a novel algorithm enhanced with a specially designed filtering technique, effectively isolating speech even amidst diverse background sounds. Our comprehensive testing and validation demonstrate the system's robustness, highlighting its capability to discern speech from noise with remarkable precision. The exploration delves into: (1) the core principles underpinning VAD and its crucial role in modern audio processing; (2) the methodologies we employed to filter ambient noise; and (3) a presentation of evidence affirming our system's superior performance in noisy conditions.
Commutativity and Null Spaces of Unbounded Operators on Hilbert Space

Based on the success of a well-known method for solving higher order linear differential equations, a study of two of the most important mathematical features of that method, viz. the null spaces and commutativity of the product of unbounded linear operators on a Hilbert space is carried out. A principle is proved describing solutions for the product of such operators in terms of the solutions for each of the factors when the null spaces of those factors satisfy a certain geometric relation to one another. Another geometric principle equating commutativity of a closed densely defined operator and a projection to stability of the range of the projection under the closed operator is proved.
Flow Symmetrization for Parameterized Constrained Diffeomorphisms

Diffeomorphisms play a crucial role while searching for shapes with fixed topological properties, allowing for smooth deformation of template shapes. Several approaches use diffeomorphism for shape search. However, these approaches employ only unconstrained diffeomorphisms. In this work, we develop Flow Symmetrization - a method to represent a parametric family of constrained diffeomorphisms that contain additional symmetry constraints such as periodicity, rotation equivariance, and transflection equivariance. Our representation is differentiable in nature, making it suitable for gradient-based optimization approaches for shape search. As these symmetry constraints naturally arise in tiling classes, our method is ideal for representing tile shapes belonging to any tiling class. To demonstrate the efficacy of our method, we design two frameworks for addressing the challenging problems of Escherization and Density Estimation. The first framework is dedicated to the Escherization problem, where we parameterize tile shapes belonging to different isohedral classes. Given a target shape, the template tile is deformed using gradient-based optimization to resemble the target shape. The second framework focuses on density estimation in identification spaces. By leveraging the inherent link between tiling theory and identification topology, we design constrained diffeomorphisms for the plane that result in unconstrained diffeomorphisms on the identification spaces. Specifically, we perform density estimation on identification spaces such as torus, sphere, Klein bottle, and projective plane. Through results and experiments, we demonstrate that our method obtains impressive results for Escherization on the Euclidean plane and density estimation on non-Euclidean identification spaces.