Miracle-dz/UniFAR-FTUs huggingface dataset:
A Phase-space View of Cold-gas Properties of Virgo Cluster Galaxies: Multiple Quenching Processes at Work?

We investigate the cold-gas properties of massive Virgo galaxies (>109 M ⊙) at <3R 200 (R 200 is the radius where the mean interior density is 200 times the critical density) on the projected phase-space diagram with the largest archival data set to date to understand the environmental effects on galaxy evolution in the Virgo cluster. We find lower H i and H2 mass fractions and higher star formation efficiencies (SFEs) from H i and H2 in the Virgo galaxies than in the field galaxies for matched stellar masses; the Virgo galaxies generally follow the field relationships between the offset from the main sequence of the star-forming galaxies [Δ(MS)] and the gas fractions and SFEs, to the slight offset to lower gas fractions or higher SFEs compared to field galaxies at Δ(MS) < 0; lower gas fractions in galaxies with smaller clustocentric distance and velocity; and lower gas fractions in the galaxies in the W cloud, a substructure of the Virgo cluster. Our results suggest the cold-gas properties of some Virgo galaxies are affected by their environment at least at 3R 200 maybe via strangulation and/or preprocesses, and H i and H2 in some galaxies are removed by ram pressure at <1.5R 200. Our data cannot rule out the possibility of other processes such as strangulation and galaxy harassment accounting for gas reduction in some galaxies at <1.5R 200. Future dedicated observations of a mass-limited complete sample are required for definitive conclusions.
Capturing Omni-Range Context for Omnidirectional Segmentation

Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving. Enabling an all-encompassing view of street-scenes, omnidirectional cameras present themselves as a perfect fit in such systems. Most segmentation models for parsing urban environments operate on common, narrow Field of View (FoV) images. Transferring these models from the domain they were designed for to 360° perception, their performance drops dramatically, e.g., by an absolute 30.0% (mIoU) on established test-beds. To bridge the gap in terms of FoV and structural distribution between the imaging domains, we introduce Efficient Concurrent Attention Networks (ECANets), directly capturing the inherent long-range dependencies in omnidirectional imagery. In addition to the learned attention-based contextual priors that can stretch across 360° images, we upgrade model training by leveraging multi-source and omni-supervised learning, taking advantage of both: Densely labeled and unlabeled data originating from multiple datasets. To foster progress in panoramic image segmentation, we put forward and extensively evaluate models on Wild PAnoramic Semantic Segmentation (WildPASS), a dataset designed to capture diverse scenes from all around the globe. Our novel model, training regimen and multi-source prediction fusion elevate the performance (mIoU) to new state-of-the-art results on the public PASS (60.2%) and the fresh WildPASS (69.0%) benchmarks.1
"This Browser is Lightning Fast": The Effects of Message Content on Perceived Performance

Perceived performance refers to how quickly software appears to perform a given task and is an integral element of building user trust and holding attention [4, 6]. Specifically, web browser performance specs are a primary metric deployed in software benchmarks, technology media news stories, and related marketing copy for comparing web browsers. With technical performance being very similar across various web browsers, investigating other factors that contribute to user perceived performance is integral to understanding and improving holistic perceptions of browser quality. With technical performance being similar for various web browsers, improving user perceived performance is integral to optimizing browser quality. We investigated the importance of priming, which has a well-documented ability to affect people’s beliefs, on users’ perceptions of web browser performance. We studied 1495 participants who read either an article about performance improvements to Mozilla Firefox, an article about user interface updates to Firefox, or an article about self-driving cars, and then watched video clips of browser tasks. As the priming effect would suggest, we found that reading articles about Firefox increased participants’ perceived performance of Firefox over the most widely used web browser, Google Chrome. In addition, we found that article content mattered, as the article about performance improvements led to higher performance ratings than the article about UI updates. Our findings demonstrate how perceived performance can be improved without making technical improvements and that designers and developers must consider a wider picture when trying to improve user attitudes about technology. A variety of factors affect user perceived performance. Previous research on search engine results and social media has illustrated the importance of popularity and brand on usage and perception of performance, respectively [17, 28]. Additionally, multiple studies [15, 38] have illustrated the effect that priming users with information from credible sources can have on their opinions. This paper builds on these findings through a large-scale survey investigating whether priming can be used to improve users’ perceived performance of a less well-known browser over the most widely-used web browser. In doing so, this paper makes several contributions: Author
Time-Ordered Recent Event (TORE) Volumes for Event Cameras

Event cameras are an exciting, new sensor modality enabling high-speed imaging with extremely low-latency and wide dynamic range. Unfortunately, most machine learning architectures are not designed to directly handle sparse data, like that generated from event cameras. Many state-of-the-art algorithms for event cameras rely on interpolated event representations—obscuring crucial timing information, increasing the data volume, and limiting overall network performance. This paper details an event representation called Time-Ordered Recent Event (TORE) volumes. TORE volumes are designed to compactly store raw spike timing information with minimal information loss. This bio-inspired design is memory efficient, computationally fast, avoids time-blocking (i.e., fixed and predefined frame rates), and contains “local memory” from past data. The design is evaluated on a wide range of challenging tasks (e.g., event denoising, image reconstruction, classification, and human pose estimation) and is shown to dramatically improve state-of-the-art performance. TORE volumes are an easy-to-implement replacement for any algorithm currently utilizing event representations.
Homogenization theory of ion transportation in multicellular tissue

Ion transport in biological tissues is crucial in the study of many biological and pathological problems. Some multi-cellular structures, like smooth muscles on the vessel walls, could be treated as periodic bi-domain structures, which consist of intracellular space and extracellular space with semipermeable membranes in between. With the aid of two-scale homogenization theory, macro-scale models are proposed based on an electro-neutral (EN) microscale model with nonlinear interface conditions, where membranes are treated as combinations of capacitors and resistors. The connectivity of intracellular space is also taken into consideration. If the intracellular space is fully connected and forms a syncytium, then the macroscale model is a bidomain nonlinear coupled partial differential equations system. Otherwise, when the intracellular cells are not connected, the macroscale model for intracellular space is an ordinary differential system with source/sink terms from the connected extracellular space.
Stochastic Electron Acceleration by Temperature Anisotropy Instabilities under Solar Flare Plasma Conditions

Using 2D particle-in-cell plasma simulations, we study electron acceleration by temperature anisotropy instabilities, assuming conditions typical of above-the-loop-top sources in solar flares. We focus on the long-term effect of T e,⊥ > T e,∥ instabilities by driving the anisotropy growth during the entire simulation time through imposing a shearing or a compressing plasma velocity (T e,⊥ and T e,∥ are the temperatures perpendicular and parallel to the magnetic field). This magnetic growth makes T e,⊥/T e,∥ grow due to electron magnetic moment conservation, and amplifies the ratio ω ce/ω pe from ∼0.53 to ∼2 (ω ce and ω pe are the electron cyclotron and plasma frequencies, respectively). In the regime ω ce/ω pe ≲ 1.2–1.7, the instability is dominated by oblique, quasi-electrostatic modes, and the acceleration is inefficient. When ω ce/ω pe has grown to ω ce/ω pe ≳ 1.2–1.7, electrons are efficiently accelerated by the inelastic scattering provided by unstable parallel, electromagnetic z modes. After ω ce/ω pe reaches ∼2, the electron energy spectra show nonthermal tails that differ between the shearing and compressing cases. In the shearing case, the tail resembles a power law of index α s ∼ 2.9 plus a high-energy bump reaching ∼300 keV. In the compressing runs, α s ∼ 3.7 with a spectral break above ∼500 keV. This difference can be explained by the different temperature evolutions in these two types of simulations, suggesting that a critical role is played by the type of anisotropy driving, ω ce/ω pe, and the electron temperature in the efficiency of the acceleration.
Combining Learning from Demonstration with Learning by Exploration to Facilitate Contact-Rich Tasks

Collaborative robots are expected to work alongside humans and directly replace human workers in some cases, thus effectively responding to rapid changes in assembly lines. Current methods for programming contact-rich tasks, particularly in heavily constrained spaces, tend to be fairly inefficient. Therefore, faster and more intuitive approaches are urgently required for robot teaching. This study focuses on combining visual servoing-based learning from demonstration (LfD) and force-based learning by exploration (LbE) to enable the fast and intuitive programming of contact-rich tasks with minimal user efforts. Two learning approaches were developed and integrated into a framework, one relying on human-to-robot motion mapping (visual servoing approach) and the other relying on force-based reinforcement learning. The developed framework implements the noncontact demonstration teaching method based on the visual servoing approach and optimizes the demonstrated robot target positions according to the detected contact state. The developed framework is compared with two most commonly used baseline techniques, i.e., teach pendant-based teaching and hand-guiding teaching. Furthermore, the efficiency and reliability of the framework are validated via comparison experiments involving the teaching and execution of contact-rich tasks. The proposed framework shows the best performance in terms of the teaching time, execution success rate, risk of damage, and ease of use.
Real-time Approximate Routing for Smart Transit Systems

We study real-time routing policies in smart transit systems, where the platform has a combination of cars and high-capacity vehicles (e.g., buses or shuttles) and seeks to serve a set of incoming trip requests. The platform can use its fleet of cars as a feeder to connect passengers to its high-capacity fleet, which operates on fixed routes. Our goal is to find the optimal set of (bus) routes and corresponding frequencies to maximize the social welfare of the system in a given time window. This generalizes the Line Planning Problem, a widely studied topic in the transportation literature, for which existing solutions are either heuristic (with no performance guarantees), or require extensive computation time (and hence are impractical for real-time use). To this end, we develop a 1-1/e-ε approximation algorithm for the Real-Time Line Planning Problem, using ideas from randomized rounding and the Generalized Assignment Problem. Our guarantee holds under two assumptions: (i) no inter-bus transfers and (ii) access to a pre-specified set of feasible bus lines. We moreover show that these two assumptions are crucial by proving that, if either assumption is relaxed, the łineplanningproblem does not admit any constant-factor approximation. Finally, we demonstrate the practicality of our algorithm via numerical experiments on real-world and synthetic datasets, in which we show that, given a fixed time budget, our algorithm outperforms Integer Linear Programming-based exact methods.
Valley relaxation of resident electrons and holes in a monolayer semiconductor: Dependence on carrier density and the role of substrate-induced disorder

Using time-resolved optical Kerr rotation, we measure the low temperature valley dynamics of resident electrons and holes in exfoliated WSe$_2$ monolayers as a systematic function of carrier density. In an effort to reconcile the many disparate timescales of carrier valley dynamics in monolayer semiconductors reported to date, we directly compare the doping-dependent valley relaxation in two electrostatically-gated WSe$_2$ monolayers having different dielectric environments. In a fully-encapsulated structure (hBN/WSe$_2$/hBN, where hBN is hexagonal boron nitride), valley relaxation is found to be monoexponential. The valley relaxation time $\tau_v$ is quite long ($\sim$10~$\mu$s) at low carrier densities, but decreases rapidly to less than 100~ns at high electron or hole densities $\gtrsim$2 $\times 10^{12}$~cm$^{-2}$. In contrast, in a partially-encapsulated WSe$_2$ monolayer placed directly on silicon dioxide (hBN/WSe$_2$/SiO$_2$), carrier valley relaxation is multi-exponential at low carrier densities. The difference is attributed to environmental disorder from the SiO$_2$ substrate. Unexpectedly, very small out-of-plane magnetic fields can increase $\tau_v$, especially in the hBN/WSe$_2$/SiO$_2$ structure, suggesting that localized states induced by disorder can play an important role in depolarizing spins and mediating the valley relaxation of resident carriers in monolayer transition metal-dichalcogenide semiconductors.
Self-Learning for Zero Shot Neural Machine Translation

Neural Machine Translation (NMT) approaches employing monolingual data are showing steady improvements in resource rich conditions. However, evaluations using real-world low-resource languages still result in unsatisfactory performance. This work proposes a novel zero-shot NMT modeling approach that learns without the now-standard assumption of a pivot language sharing parallel data with the zero-shot source and target languages. Our approach is based on three stages: initialization from any pre-trained NMT model observing at least the target language, augmentation of source sides leveraging target monolingual data, and learning to optimize the initial model to the zero-shot pair, where the latter two constitute a self-learning cycle. Empirical findings involving four diverse (in terms of a language family, script and relatedness) zero-shot pairs show the effectiveness of our approach with up to +5.93 BLEU improvement against a supervised bilingual baseline. Compared to unsupervised NMT, consistent improvements are observed even in a domain-mismatch setting, attesting to the usability of our method.
A data-driven epidemic model with social structure for understanding the COVID-19 infection on a heavily affected Italian province

In this work, using a detailed dataset furnished by National Health Authorities concerning the Province of Pavia (Lombardy, Italy), we propose to determine the essential features of the ongoing COVID-19 pandemic in terms of contact dynamics. Our contribution is devoted to provide a possible planning of the needs of medical infrastructures in the Pavia Province and to suggest different scenarios about the vaccination campaign which possibly help in reducing the fatalities and/or reducing the number of infected in the population. The proposed research combines a new mathematical description of the spread of an infectious diseases which takes into account both age and average daily social contacts with a detailed analysis of the dataset of all traced infected individuals in the Province of Pavia. These information are used to develop a data-driven model in which calibration and feeding of the model are extensively used. The epidemiological evolution is obtained by relying on an approach based on statistical mechanics. This leads to study the evolution over time of a system of probability distributions characterizing the age and social contacts of the population. One of the main outcomes shows that, as expected, the spread of the disease is closely related to the mean number of contacts of individuals. The model permits to forecast thanks to an uncertainty quantification approach and in the short time horizon, the average number and the confidence bands of expected hospitalized classified by age and to test different options for an effective vaccination campaign with age-decreasing priority.
Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation

The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data. In these representations, data are encoded with various visual attributes depending on the needs of the representation itself. To make coherent design choices about visual attributes, the visual search field proposes guidelines based on the human brain perception of features. However, information visualization representations frequently need to depict more data than the amount these guidelines have been validated on. Since, the information visualization community has extended these guidelines to a wider parameter space. This paper contributes to this theme by extending visual search theories to an information visualization context. We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractor. Stimuli are defined by color and shape features for the purpose of visually encoding categorical data. The experimental protocol is made of a parameters space reduction step (i.e., sub-sampling) based on a machine learning model, and a user evaluation to measure capacity limits and validate hypotheses. The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier. When redundantly encoded, the display heterogeneity has no effect on the task. When encoded with one attribute, the difficulty depends on that attribute heterogeneity until its capacity limit (7 for color, 5 for shape) is reached. Finally, when encoded with two attributes simultaneously, performances drop drastically even with minor heterogeneity.
Event-Triggered Distributed Estimation With Decaying Communication Rate

We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Different from existing time-triggered communication schemes, an event-triggered asynchronous scheme is investigated in order to reduce communication while preserving estimation convergence. A distributed estimation algorithm with a single step size is first proposed based on an event-triggered communication scheme with a time-dependent decaying threshold. With the event-triggered scheme, each sensor sends its estimate to neighbor sensors only when the difference between the current estimate and the last sent-out estimate is larger than the triggering threshold. We prove that the proposed algorithm has mean-square and almost-sure convergence respectively, under an integrated condition of sensor network topology and sensor measurement matrices. The condition is satisfied if the topology is a balanced digraph containing a spanning tree and the system is collectively observable. Moreover, we provide estimates for the convergence rates, which are related to the step size as well as the triggering threshold. Furthermore, as an essential metric of sensor communication intensity in the event-triggered distributed algorithms, the communication rate is proved to decay to zero with a certain speed almost surely as time goes to infinity. We show that given the step size, adjusting the decay speed of the triggering threshold can lead to a tradeoff between the convergence rate of the estimation error and the decay speed of the communication rate. Specifically, increasing the decay speed of the threshold would make the communication rate decay faster, but reduce the convergence rate of the estimation error. Numerical simulations are provided to illustrate the developed results.
Maximum Entropy RL (Provably) Solves Some Robust RL Problems

Many potential applications of reinforcement learning (RL) require guarantees that the agent will perform well in the face of disturbances to the dynamics or reward function. In this paper, we prove theoretically that standard maximum entropy RL is robust to some disturbances in the dynamics and the reward function. While this capability of MaxEnt RL has been observed empirically in prior work, to the best of our knowledge our work provides the first rigorous proof and theoretical characterization of the MaxEnt RL robust set. While a number of prior robust RL algorithms have been designed to handle similar disturbances to the reward function or dynamics, these methods typically require adding additional moving parts and hyperparameters on top of a base RL algorithm. In contrast, our theoretical results suggest that MaxEnt RL by itself is robust to certain disturbances, without requiring any additional modifications. While this does not imply that MaxEnt RL is the best available robust RL method, MaxEnt RL does possess a striking simplicity and appealing formal guarantees.
Delaunay triangulations of generalized Bolza surfaces

The Bolza surface can be seen as the quotient of the hyperbolic plane, represented by the Poincaré disk model, under the action of the group generated by the hyperbolic isometries identifying opposite sides of a regular octagon centered at the origin. We consider generalized Bolza surfaces Mg, where the octagon is replaced by a regular 4g-gon, leading to a genus g surface. We propose an extension of Bowyer’s algorithm to these surfaces. In particular, we compute the value of the systole of Mg. We also propose algorithms computing small sets of points on Mg that are used to initialize Bowyer’s algorithm.
Design, fabrication and characterisation of a micro-fabricated double-junction segmented ion trap

We describe the implementation of a three-dimensional Paul ion trap fabricated from a stack of precision-machined silica glass wafers, which incorporates a pair of junctions for 2-dimensional ion transport. The trap has 142 dedicated electrodes which can be used to define multiple potential wells in which strings of ions can be held. By supplying time-varying potentials, this also allows for transport and re-configuration of ion strings. We describe the design, simulation, fabrication and packaging of the trap, including explorations of different parameter regimes and possible optimizations and design choices. We give results of initial testing of the trap, including measurements of heating rates and junction transport.