Miracle-dz/UniFAR-FTUs huggingface dataset:
DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction

Neuromorphic event cameras are useful for dynamic vision problems under difficult lighting conditions. To enable studies of using event cameras in automobile driving applications, this paper reports a new end-to-end driving dataset called DDD20. The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames. DDD20 is the longest event camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera and vehicle human control data collected from 4000km of highway and urban driving under a variety of lighting conditions. Using DDD20, we report the first study of fusing brightness change events and intensity frame data using a deep learning approach to predict the instantaneous human steering wheel angle. Over all day and night conditions, the explained variance for human steering prediction from a Resnet-32 is significantly better from the fused DVS+APS frames (0.88) than using either DVS (0.67) or APS (0.77) data alone.
A Non-local Model of the Propagation of Action Potentials in Myelinated Neurons

Myelinated neurons are characterized by the presence of myelin, a multilaminated wrapping around the axons formed by specialized neuroglial cells. Myelin acts as an electrical insulator and therefore, in myelinated neurons, the action potentials do not propagate within the axons but happen only at the nodes of Ranvier which are gaps in the axonal myelination. Recent advancements in brain science have shown that the shapes, timings, and propagation speeds of these so-called saltatory action potentials are controlled by various biochemical interactions among neurons, glial cells and the extracellular space. Given the complexity of brain's structure and processes, the work hypothesis made in this paper is that non-local effects are involved in the optimal propagation of action potentials. A non-local model of the action potentials propagation in myelinated neurons is proposed that involves spatial derivatives of fractional order. The effects of non-locality on the distribution of the membrane potential are investigated using numerical simulations.
Structure/property relationship of semi-crystalline polymer during tensile deformation: A molecular dynamics approach

A coarse-grained molecular dynamics model of linear polyethylene-like polymer chain system was built to investigate the responds of structure and mechanical properties during uniaxial deformation. The influence of chain length, temperature, and strain rate were studied. The molecular dynamic tests showed that yielding may governed by different mechanisms at temperatures above and below Tg. Melt-recrystallization was observed at higher temperature, and destruction of crystal structures was observed at lower temperatures beyond yield point. While the higher temperature and lower strain rate have similar effects on mechanical properties. The correlated influences of time and temperature in the microscopic structures are more complicated. The evolution of microscopic characteristics such as the orientation parameter, the bond length, and the content of trans-trans conformation were calculated from the simulation. The results showed that the temperature have double effects on polymer chains. Higher temperature on one hand makes the chains more flexible, while on the other hand shortens the relaxation time of polymers. It is the interaction of these two aspects that determine the orientation parameter. During deformation, the trans conformation has experienced a rising process after the first drop process. And these microscopic structure parameters exhibit critical transaction, which are closely related to the yield point. A hypothetical model was thus proposed to describe the micro-structure and property relations based the investigations of this study.
Decoder Modulation for Indoor Depth Completion

Depth completion recovers a dense depth map from sensor measurements. Current methods are mostly tailored for very sparse depth measurements from LiDARs in outdoor settings, while for indoor scenes Time-of-Flight (ToF) or structured light sensors are mostly used. These sensors provide semi-dense maps, with dense measurements in some regions and almost empty in others. We propose a new model that takes into account the statistical difference between such regions. Our main contribution is a new decoder modulation branch added to the encoder-decoder architecture. The encoder extracts features from the concatenated RGB image and raw depth. Given the mask of missing values as input, the proposed modulation branch controls the decoding of a dense depth map from these features differently for different regions. This is implemented by modifying the spatial distribution of output signals inside the decoder via Spatially-Adaptive Denormalization (SPADE) blocks. Our second contribution is a novel on-the- y sensor simulation strategy that allows us to train on a semi-dense sensor data when the ground truth depth map is not available. Our model achieves the state of the art results on indoor Matterport3D dataset [1]. Being designed for semi-dense input depth, our model is still competitive with LiDAR-oriented approaches on the KITTI dataset [2]. Our sensor simulation strategy significantly improves prediction quality with no dense ground truth available, as validated on the NYUv2 dataset [3].
Conformal Prediction: a Unified Review of Theory and New Challenges

In this work we provide a review of basic ideas and novel developments about Conformal Prediction -- an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions -- that is able to yield in a very straightforward way predictions sets that are valid in a statistical sense also in in the finite sample case. The in-depth discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea.
Sorting probability for large Young diagrams

For a finite poset $P=(X,\prec)$, let $\mathcal{L}_P$ denote the set of linear extensions of $P$. The sorting probability $\delta(P)$ is defined as \[\delta(P) \, := \, \min_{x,y\in X} \, \bigl| \mathbf{P} \, [L(x)\leq L(y) ] \ - \ \mathbf{P} \, [L(y)\leq L(x) ] \bigr|\,, \] where $L \in \mathcal{L}_P$ is a uniform linear extension of $P$. We give asymptotic upper bounds on sorting probabilities for posets associated with large Young diagrams and large skew Young diagrams, with bounded number of rows.
Causal Modeling of Twitter Activity During COVID-19

Understanding the characteristics of public attention and perception is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and perception. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.
Benchmarking the Gerchberg-Saxton Algorithm

Due to the proliferation of spatial light modulators, digital holography is finding wide-spread use in fields from augmented reality to medical imaging to additive manufacturing to lithography to optical tweezing to telecommunications. There are numerous types of SLM available with a multitude of algorithms for generating holograms. Each algorithm has limitations in terms of convergence speed, power efficiency, accuracy and data storage requirement. Here, we consider probably the most common algorithm for computer generated holography - Gerchberg-Saxton - and examine the trade-off in convergent quality, performance and efficiency. In particular, we focus on measuring and understanding the factors that control runtime and convergence.
Learning and Optimization with Seasonal Patterns

Decision Making in a Nonstationary Environment with Periodic Rewards Multiarmed bandit (MAB) is a powerful tool in sequential decision making. Traditional MAB models assume constant mean rewards over time, an assumption often too restrictive for real-world applications in which rewards can vary seasonally. In “Learning and Optimization with Seasonal Patterns,” Chen, Wang, and Wang challenge the standard assumption and study a nonstationary MAB model with periodic rewards. They introduce a two-stage policy that combines Fourier analysis with a confidence bound–based learning procedure. This innovative approach allows the algorithm to adapt to time-varying mean rewards that follow a periodic pattern. The first stage estimates the periods of all decision-making arms, whereas the second stage exploits this information to optimize long-term rewards. The study proves that the learning policy is near optimal, achieving a regret upper bound that scales with the square root of the time horizon and the periods of the arms. This work opens new avenues for more adaptive and efficient decision making in many applications that face seasonality, such as the fashion industry and service systems.
Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP

Iran, along with China, South Korea, and Italy was among the countries that were hit hard in the first wave of the COVID-19 spread. Twitter is one of the widely-used online platforms by Iranians inside and abroad for sharing their opinion, thoughts, and feelings about a wide range of issues. In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time. We applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics. We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point. We additionally categorized broader content of tweets that shows satire, followed by news, is the dominant tweet type among the Iranian users. While this framework and methodology can be used to track public response to ongoing developments related to COVID-19, a generalization of this framework can become a useful framework to gauge Iranian public reaction to ongoing policy measures or events locally and internationally.
Iterative and doubling algorithms for Riccati‐type matrix equations: A comparative introduction

We review a family of algorithms for Lyapunov‐ and Riccati‐type equations which are all related to each other by the idea of doubling: they construct the iterate Qk=X2k of another naturally‐arising fixed‐point iteration (Xh) via a sort of repeated squaring. The equations we consider are Stein equations X − A∗ X A = Q, Lyapunov equations A∗ X + X A + Q = 0, discrete‐time algebraic Riccati equations X = Q + A∗ X(I + G X)−1A, continuous‐time algebraic Riccati equations Q + A∗ X + X A − X G X = 0, palindromic quadratic matrix equations A + Q Y + A∗Y2 = 0, and nonlinear matrix equations X + A∗ X−1A = Q. We draw comparisons among these algorithms, highlight the connections between them and to other algorithms such as subspace iteration, and discuss open issues in their theory.
Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent Dilated Convolution Model for Parametric Speech Generation

In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a quasi-periodic (QP) architecture to improve the pitch controllability of PWG. PWG is a compact non-autoregressive (non-AR) speech generation model, whose generative speed is much faster than real time. While utilizing PWG as a vocoder to generate speech on the basis of acoustic features such as spectral and prosodic features, PWG generates high-fidelity speech. However, when the input acoustic features include unseen pitches, the pitch accuracy of PWG-generated speech degrades because of the fixed and generic network of PWG without prior knowledge of speech periodicity. The proposed QPPWG adopts a pitch-dependent dilated convolution network (PDCNN) module, which introduces the pitch information into PWG via the dynamically changed network architecture, to improve the pitch controllability and speech modeling capability of vanilla PWG. Both objective and subjective evaluation results show the higher pitch accuracy and comparable speech quality of QPPWG-generated speech when the QPPWG model size is only 70 % of that of vanilla PWG.
Delay-Aware Scheduling over mmWave/sub-6 Dual Interfaces: A Reinforcement Learning Approach

We consider a transmitter with mmWave/sub6 dual interfaces. Due to the intermittency of mmWave channel, the transmitter must schedule packets wisely across the interfaces to minimize the average delay by observing the system state. We use the well-known dynamic programming methods and Q-learning to find the optimal scheduling policy and investigate the influence of observing CSI on the optimal policy under different levels of knowledge of the environment. We find that only when the channel state transition model is not available, the instantaneous CSI can help in reducing system delay.
The aquatic Lystrosaurus: A palaeontological myth

Evidence previously cited for the aquatic nature of the Triassic mammal‐like reptile Lystrosaurus is reviewed. It is concluded that this evidence does not constitute a convincing case. General adaptations of aquatic tetrapods are reviewed and applied to the morphology of Lystrosaurus, in order to determine whether aquatic adaptations not previously sought in Lystrosaurus are present It is concluded that few or any such adaptations are present. Such as are, for example a widened knee and flared scapula blade, are as compatible with a digging function, as with swimming. Nevertheless, the distinctive nature of the skull and to a lesser extent the postcranial skeleton of Lystrosaurus stands out, and several features require functional interpretation. These include: a downwardly‐directed and elongated snout, a short and high temporal region, a pear‐shaped external nasal opening bounded by a rugose ridge, slight dorsal flaring of the scapula, a powerful forelimb and wide knee joint These are dealt with elsewher.
COVI White Paper

The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public health institutions around the world. Contact tracing is an essential tool to change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile apps has the potential to shift the paradigm. Some countries have deployed centralized tracking systems, but more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or for-profit corporations. Machine learning methods can circumvent some of the limitations of standard digital tracing by incorporating many clues and their uncertainty into a more graded and precise estimation of infection risk. The estimated risk can provide early risk awareness, personalized recommendations and relevant information to the user. Finally, non-identifying risk data can inform epidemiological models trained jointly with the machine learning predictor. These models can provide statistical evidence for the importance of factors involved in disease transmission. They can also be used to monitor, evaluate and optimize health policy and (de)confinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
The origin and evolution of Indomalesian, Australasian and Pacific island biotas: insights from Aglaieae (Meliaceae, Sapindales)

Aim The role of long‐distance dispersal in the Indomalesian, Australasian and Pacific flora is currently hotly debated. The lack of well‐resolved phylogenetic trees for Pacific plants has been a major limitation for biogeographical analysis. Here, we present a well‐resolved phylogenetic tree for the tribe Aglaieae in the mahogany family, Meliaceae, and use it to investigate the origin, evolution and dispersal history of biotas in this area. The subfamily Melioideae, including the tribe Aglaieae (Meliaceae, Sapindales), is a plant group with good representation in the region in terms of biomass and species numbers, wide ecological attributes and known animal vectors. The family has a good fossil record (especially from North America and Europe). Genera and species in the tribe Aglaieae therefore provide an excellent model group for addressing this debate.
Designing Just-in-Time Detection for Gamified Fitness Frameworks

This paper presents our findings from a multi-year effort to detect motion events early using inertial sensors in real-world settings. We believe early event detection is the next step in advancing motion tracking, and can enable just-in-time interventions, particularly for mHealth applications. Our system targets strength training workouts in the fitness domain, where users perform well-defined movements for each exercise, while wearing an inertial sensor. We collect data for 20 exercises across 12 users over 26 months. We propose an algorithm to detect repetitions before they end, to allow a user to visualize movement derived metrics in real-time. We further develop a gamified approach to display this information to the user and encourage them to perform consistent movements. Participants in a feasibility study find the gamified feedback useful in improving their form. Our system can detect repetition events as early as 500 ms before it ends, which is 2x faster and more accurate than state-of-the-art trackers. We believe our approach will open exciting avenues for tracking, detection, and gamification for fitness frameworks.
Nonsingular black holes in nonlinear gravity coupled to Euler-Heisenberg electrodynamics

We study static, spherically symmetric black holes supported by Euler-Heisenberg theory of electrodynamics and coupled to two different modified theories of gravity. Such theories are the quadratic $f(R)$ model and Eddington-inspired Born-Infeld gravity, both formulated in metric-affine spaces, where metric and affine connection are independent fields. We find exact solutions of the corresponding field equations in both cases, characterized by mass, charge, the Euler-Heisenberg coupling parameter and the modified gravity one. For each such family of solutions, we characterize its horizon structure and the modifications in the innermost region, finding that some subclasses are geodesically complete. The singularity regularization is achieved under two different mechanisms: either the boundary of the manifold is pushed to an infinite affine distance, not being able to be reached in finite time by any geodesic, or the presence of a wormhole structure allows for the smooth extension of all geodesics overcoming the maximum of the potential barrier.
An analysis of the periodically forced PP04 climate model, using the theory of non-smooth dynamical systems

In this paper we perform a careful analysis of the forced PP04 model for climate change, in particular the behaviour of the ice-ages. This system models the transition from a glacial to an inter-glacial state through a sudden release of oceanic Carbon Dioxide into the atmosphere. This process can be cast in terms of a Filippov dynamical system, with a discontinuous change in its dynamics related to the Carbon Dioxide release. By using techniques from the theory of non-smooth dynamical systems, we give an analysis of this model in the cases of both no insolation forcing and also periodic insolation forcing. This reveals a rich, and novel, dynamical structure to the solutions of the PP04 model. In particular we see synchronised periodic solutions with subtle regions of existence which depend on the amplitude and frequency of the forcing. The orbits can be created/destroyed in both smooth and discontinuity induced bifurcations. We study both the orbits and the transitions between them and make comparisons with actual climate dynamics.
Halting SARS-CoV-2 by Targeting High-Contact Individuals

Network scientists have proposed that infectious diseases involving person-to-person transmission may be effectively halted by targeting interventions at a minority of highly connected individuals. Can this strategy be effective in combating a virus partly transmitted in close-range contact, as many believe SARS-CoV-2 to be? Effectiveness critically depends on high between-person variability in the number of close-range contacts. We analyze population survey data showing that indeed the distribution of close-range contacts across individuals is characterized by a small fraction of individuals reporting very high frequencies. Strikingly, we find that the average duration of contact is mostly invariant in the number of contacts, reinforcing the criticality of hubs. We simulate a population embedded in a network with empirically observed contact frequencies. Simulations show that targeting hubs robustly improves containment.
On the Mammalia Obtained by the Naturalist Exploring Expedition to Southern Brazil

A large part of the North American strata which have been by various authors referred to the Permian have no valid claim to be either so considered, or as being separate from the upper Coalmeasures. But a part of them may be reasonably assumed to be homotaxially equivalent with at least a part of the European Permiars; although their delimitation from the Coal-measures may in most cases be difficult or impracticable. The evidence upon which the Texan strata have been referred to the Permian is fuller than that which has been adduced with regard to any other North American strata, that have been so referred. That is, the evidence of both vertebrate and invertebrate fossils is in favor of such reference, and the difference in the character of the strata from those of the underlying Coal-measures, although not great, is conveniently distinguishable. Still, it is true that the Texan Permian strata bear many Coal-measure invertebrate species; and its flora is at present unknown.