Miracle-dz/UniFAR-FTUs huggingface dataset:
Ladder Costs for Random Walks in L\'evy media

We consider a random walk $Y$ moving on a \emph{L\'evy random medium}, namely a one-dimensional renewal point process with inter-distances between points that are in the domain of attraction of a stable law. The focus is on the characterization of the law of the first-ladder height $Y_{\mathcal{T}}$ and length $L_{\mathcal{T}}(Y)$, where $\mathcal{T}$ is the first-passage time of $Y$ in $\mathbb{R}^+$. The study relies on the construction of a broader class of processes, denoted \emph{Random Walks in Random Scenery on Bonds} (RWRSB) that we briefly describe. The scenery is constructed by associating two random variables with each bond of $\mathbb{Z}$, corresponding to the two possible crossing directions of that bond. A random walk $S$ on $\mathbb{Z}$ with i.i. d increments collects the scenery values of the bond it traverses: we denote this composite process the RWRSB. Under suitable assumptions, we characterize the tail distribution of the sum of scenery values collected up to the first exit time $\mathcal{T}$. This setting will be applied to obtain results for the laws of the first-ladder length and height of $Y$. The main tools of investigation are a generalized Spitzer-Baxter identity, that we derive along the proof, and a suitable representation of the RWRSB in terms of local times of the random walk $S$. All these results are easily generalized to the entire sequence of ladder variables.
Car-Sharing Subscription Preferences: The Case of Copenhagen, Munich, and Tel Aviv-Yafo

Car-sharing services have been providing short-term car access to their users, contributing to sustainable urban mobility and generating positive societal and often environmental impacts. As car-sharing business models vary, it is important to understand what features drive the attraction and retention of its members in different contexts. For that, it is essential to examine individuals’ preferences for subscriptions to different business models and what they perceive as most relevant, as well as understand what could be attractive incentives. This study aims precisely to examine individuals’ preferences for the subscription of different car-sharing services in different cities. We designed a stated preference experiment and collected data from three different urban car-sharing settings, namely Copenhagen, Munich, and Tel Aviv-Yafo. Then a mixed logit model was estimated to uncover car-sharing plan subscription and incentives preferences. The results improve our understanding of how both the features of the car-sharing business model and the provision of incentives can maintain and attract members to the system. The achieved insights pave the road for the actual design of car-sharing business models and incentives that can be offered by existing and future car-sharing companies in the studied or similar cities. The specification of attributes and levels of the SP experiment was made through an iterative process with representatives of all cities. The attributes included were one-time subscription cost, usage cost, walking time to access the vehicle, walking time from the parking location to the destination, the probability of getting a shared vehicle, car-sharing vehicle types, and car-sharing vehicle engine type. The incentives offered were guaranteed child car seat availability, a family/friends account with discounted rates, a business account with discounted rates, the
Robust Topological Inference in the Presence of Outliers

The distance function to a compact set plays a crucial role in the paradigm of topological data analysis. In particular, the sublevel sets of the distance function are used in the computation of persistent homology -- a backbone of the topological data analysis pipeline. Despite its stability to perturbations in the Hausdorff distance, persistent homology is highly sensitive to outliers. In this work, we develop a framework of statistical inference for persistent homology in the presence of outliers. Drawing inspiration from recent developments in robust statistics, we propose a $\textit{median-of-means}$ variant of the distance function ($\textsf{MoM Dist}$), and establish its statistical properties. In particular, we show that, even in the presence of outliers, the sublevel filtrations and weighted filtrations induced by $\textsf{MoM Dist}$ are both consistent estimators of the true underlying population counterpart, and their rates of convergence in the bottleneck metric are controlled by the fraction of outliers in the data. Finally, we demonstrate the advantages of the proposed methodology through simulations and applications.
A review of machine learning approaches, challenges and prospects for computational tumor pathology

Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative approaches to patient prognosis. In the past decade, rapid advances in artificial intelligence, chip design and manufacturing, and mobile computing have facilitated research in computational pathology and have the potential to provide better-integrated solutions for whole-slide images, multi-omics data, and clinical informatics. However, tumor computational pathology now brings some challenges to the application of tumour screening, diagnosis and prognosis in terms of data integration, hardware processing, network sharing bandwidth and machine learning technology. This review investigates image preprocessing methods in computational pathology from a pathological and technical perspective, machine learning-based methods, and applications of computational pathology in breast, colon, prostate, lung, and various tumour disease scenarios. Finally, the challenges and prospects of machine learning in computational pathology applications are discussed.
Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction

Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game do not have the ball for about 87 minutes on average. However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates. In this paper, we evaluate players who create off-ball scoring opportunities by comparing actual movements with the reference movements generated via trajectory prediction. First, we predict the trajectories of players using a graph variational recurrent neural network that can accurately model the relationship between players and predict the long-term trajectory. Next, based on the difference in the modified off-ball evaluation index between the actual and the predicted trajectory as a reference, we evaluate how the actual movement contributes to scoring opportunity compared to the predicted movement. For verification, we examined the relationship with the annual salary, the goals, and the rating in the game by experts for all games of a team in a professional soccer league in a year. The results show that the annual salary and the proposed indicator correlated significantly, which could not be explained by the existing indicators and goals. Our results suggest the effectiveness of the proposed method as an indicator for a player without the ball to create a scoring chance for teammates.
Contextualization for the Organization of Text Documents Streams

There has been a signiﬁcant effort by the research community to address the problem of providing methods to organize documentation with the help of information Retrieval methods. In this report paper, we present several experiments with some stream analysis methods to explore streams of text documents. We use only dynamic algorithms to explore, analyze, and organize the ﬂux of text documents. This document shows a case study with developed architectures of a Text Document Stream Organization, using incremental algorithms like Incremental TextRank, and IS-TFIDF. Both these algorithms are based on the assumption that the mapping of text documents and their document-term matrix in lower-dimensional evolving networks provides faster processing when compared to batch algorithms. With this architecture, and by using FastText Embedding to retrieve similarity between documents, we compare methods with large text datasets and ground truth evaluation of clustering capacities. The datasets used were Reuters and COVID-19 emotions. The results provide a new view for the contextualization of similarity when approaching ﬂux of documents organization tasks, based on the similarity between documents in the ﬂux, and by using mentioned algorithms.
Recurrent Video Restoration Transformer with Guided Deformable Attention

Video restoration aims at restoring multiple high-quality frames from multiple low-quality frames. Existing video restoration methods generally fall into two extreme cases, i.e., they either restore all frames in parallel or restore the video frame by frame in a recurrent way, which would result in different merits and drawbacks. Typically, the former has the advantage of temporal information fusion. However, it suffers from large model size and intensive memory consumption; the latter has a relatively small model size as it shares parameters across frames; however, it lacks long-range dependency modeling ability and parallelizability. In this paper, we attempt to integrate the advantages of the two cases by proposing a recurrent video restoration transformer, namely RVRT. RVRT processes local neighboring frames in parallel within a globally recurrent framework which can achieve a good trade-off between model size, effectiveness, and efficiency. Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature. Within each clip, different frame features are jointly updated with implicit feature aggregation. Across different clips, the guided deformable attention is designed for clip-to-clip alignment, which predicts multiple relevant locations from the whole inferred clip and aggregates their features by the attention mechanism. Extensive experiments on video super-resolution, deblurring, and denoising show that the proposed RVRT achieves state-of-the-art performance on benchmark datasets with balanced model size, testing memory and runtime.
Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning

The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical successes in model-based reinforcement learning with function approximation, however, eschew the true model in favor of a surrogate that, while ignoring various facets of the environment, still facilitates effective planning over behaviors. Recently formalized as the value equivalence principle, this algorithmic technique is perhaps unavoidable as real-world reinforcement learning demands consideration of a simple, computationally-bounded agent interacting with an overwhelmingly complex environment. In this work, we entertain an extreme scenario wherein some combination of immense environment complexity and limited agent capacity entirely precludes identifying an exactly value-equivalent model. In light of this, we embrace a notion of approximate value equivalence and introduce an algorithm for incrementally synthesizing simple and useful approximations of the environment from which an agent might still recover near-optimal behavior. Crucially, we recognize the information-theoretic nature of this lossy environment compression problem and use the appropriate tools of rate-distortion theory to make mathematically precise how value equivalence can lend tractability to otherwise intractable sequential decision-making problems.
Passive Photoacoustic Effect

Photoacoustic effect refers to the acoustic generation induced by laser irradiation, where nanosecond pulsed laser source is normally used to provide instantaneous heating and thermoelastic expansion of the sample. More generally, photoacoustic generation requires active intensity modulation of laser source to produce sharp temperature gradient, which is key to generate acoustic wave. In this paper, we propose a novel photoacoustic effect from moving droplet, which generates photoacoustic wave by a non-modulated continuous-wave laser. When the droplet moves through the laser spot area, it can be heated up and cooled down instantaneously, generating photoacoustic waves. We name it passive photoacoustic effect. Theoretical analysis and simulation study validated the existence of passive photoacoustic effect. This phenomenon may find potential application in high-throughput photoacoustic cytometry.
VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images

—In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT). VPIT is the ﬁrst method that uses voxel pseudo images for 3D SOT. The input point cloud is structured by pillar-based voxelization, and the resulting pseudo image is used as an input to a 2D-like Siamese SOT method. The pseudo image is created in the Bird’s-eye View (BEV) coordinates, and therefore the objects in it have constant size. Thus, only the object rotation can change in the new coordinate system and not the object scale. For this reason, we replace multi-scale search with a multi-rotation search, where differently rotated search regions are compared against a single target representation to predict both position and rotation of the object. Experiments on KITTI Tracking dataset show that VPIT is the fastest 3D SOT method and maintains competitive Success and Precision values. Application of a SOT method in a real-world scenario meets with limitations such as lower computational capabilities of embedded devices and a latency-unforgiving environment, where the method is forced to skip certain data frames if the inference speed is not high enough. We implement a real-time evaluation protocol and show that other methods lose most of their performance on embedded devices, while VPIT maintains its ability to track the object.
Seamless Interaction Design with Coexistence and Cooperation Modes for Robust Human-Robot Collaboration

A robot needs multiple interaction modes to robustly collaborate with a human in complicated industrial tasks. We develop a Coexistence-and-Cooperation (CoCo) human-robot collaboration system. Coexistence mode enables the robot to work with the human on different sub-tasks independently in a shared space. Cooperation mode enables the robot to follow human guidance and recover failures. A human intention tracking algorithm takes in both human and robot motion measurements as input and provides a switch on the interaction modes. We demonstrate the effectiveness of CoCo system in a use case analogous to a real world multi-step assembly task.
Subspace Phase Retrieval

In recent years, phase retrieval has received much attention in many fields including statistics, applied mathematics and optical engineering. In this paper, we propose an efficient algorithm, termed Subspace Phase Retrieval (SPR), which can accurately recover a $n$-dimensional $k$-sparse complex-valued signal given its $\mathcal O(k\log^3 n)$ magnitude-only Gaussian samples. This offers a significant improvement over many existing methods that require $\mathcal O(k^2 \log n)$ or more samples. Also, our sampling complexity is nearly optimal as it is very close to the fundamental limit $\mathcal O(k \log \frac{n}{k})$ for sparse phase retrieval.
A Level-Set Immersed Boundary Method for Incompressible Flows at Subcritical Reynolds Numbers

In this work, a numerical scheme based on a level-set immersed boundary method is employed for the numerical simulation of the ﬂow around two tandem circular cylinders in the subcritical ﬂow regimes. Three diﬀerent spacing ratios (cid:96)/ D (where (cid:96) is the center-to-center distance between the two cylinders with D being the diameter of the cylinders) from 2 to 4 is considered. The instantaneous ﬂow structures, pressure distributions and hydro-dynamic forces on two tandem cylinders are analyzed at a Reynolds number of R e = 2 . 2 × 10 4 . The strategy is based on a combination of a narrow-band accurate conservative level set method and ghost-ﬂuid framework. In this strategy, the interface is deﬁned as the isocontour of a hyperbolic tangent function, which is advected by the ﬂuid, and then periodically reshaped to enforce the degraded level set function being a signed distance function using a reinitialization equation based on an improved form. The latter approach takes advantage of a mapping onto a classical distance level set while much better preserving the interface shape.
Evaluating the V-band Photometric Metallicity with Fundamental Mode RR Lyrae in the Kepler Field

The aim of this work is to evaluate the performance of photometric metallicity [Fe/H], determined based on V-band light curves photometrically transformed from the gr-band light curves. We tested this by using a set of homogeneous samples of fundamental mode RR Lyrae located in the Kepler field. It was found that the color term is necessary in such photometric transformation. We demonstrated that when including the color term the determined photometric [Fe/H] are in good agreement with the spectroscopic [Fe/H], either based on the calibrated or the transformed V-band light curves. We also tested the impact of Blazhko RR Lyrae in determining the photometric [Fe/H], and found that Blazhko RR Lyrae can give consistent photometric [Fe/H]. Finally, we derived independent gVr-band [Fe/H]–ϕ 31–P relations (where ϕ 31 and P are the Fourier parameter and pulsation period, respectively) using our light curves. The V-band relation is in good agreement with the most recent determination given in the literature.
ARC - Actor Residual Critic for Adversarial Imitation Learning

Adversarial Imitation Learning (AIL) is a class of popular state-of-the-art Imitation Learning algorithms commonly used in robotics. In AIL, an artificial adversary's misclassification is used as a reward signal that is optimized by any standard Reinforcement Learning (RL) algorithm. Unlike most RL settings, the reward in AIL is $differentiable$ but current model-free RL algorithms do not make use of this property to train a policy. The reward is AIL is also shaped since it comes from an adversary. We leverage the differentiability property of the shaped AIL reward function and formulate a class of Actor Residual Critic (ARC) RL algorithms. ARC algorithms draw a parallel to the standard Actor-Critic (AC) algorithms in RL literature and uses a residual critic, $C$ function (instead of the standard $Q$ function) to approximate only the discounted future return (excluding the immediate reward). ARC algorithms have similar convergence properties as the standard AC algorithms with the additional advantage that the gradient through the immediate reward is exact. For the discrete (tabular) case with finite states, actions, and known dynamics, we prove that policy iteration with $C$ function converges to an optimal policy. In the continuous case with function approximation and unknown dynamics, we experimentally show that ARC aided AIL outperforms standard AIL in simulated continuous-control and real robotic manipulation tasks. ARC algorithms are simple to implement and can be incorporated into any existing AIL implementation with an AC algorithm. Video and link to code are available at: https://sites.google. com/view/actor-residual-critic.
MeerKAT Observations of the Reversing Drifting Subpulses in PSR J1750-3503

We present an analysis of the subpulse drift in PSR J1750−3503, which is characterized by abrupt transitions of drift direction. As the pulsar does not exhibit other mode changes or clear nulling, it is an ideal candidate system for studying the phenomenon of drift direction change. For ∼80% of the time, the subpulses are characterized by positive drift—from early to later longitudes—while the drift direction is negative in the other ∼20%. The subpulse separation for single pulses with positive drift, P 2 = (18.8 ± 0.1)°, is higher than for single pulses with negative drift, P 2 = (17.5 ± 0.2)°. When the drift is stable, the measured repetition time of the drift pattern is P 3 obs = (43.5 ± 0.4)P, where P is the pulsar period. We show that the observed data can be reproduced by a carousel models with subpulse rotation around the magnetic axis using a purely dipolar configuration of the surface magnetic field. The observed drift characteristics can be modeled assuming that the actual repetition time P 3 < 2P, such that we observe its aliased value. A small variation in P 3, of the order of 6% (or less assuming higher alias orders), is enough to reproduce the characteristic drift direction changes we observe.
NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation

We propose a method for Saimaa ringed seal ( Pusa hispida saimensis ) re-identiﬁcation. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal monitoring and conservation and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identiﬁcation by Pelage Pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal’s pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based aﬃne invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to ﬁnd the best match from a database of known individuals. We perform extensive experiments of various modiﬁcations of the method on a new challenging Saimaa ringed seals re-identiﬁcation dataset. The proposed method is shown to produce the best re-identiﬁcation accuracy on our dataset in comparisons with alternative approaches.
UTTS: Unsupervised TTS with Conditional Disentangled Sequential Variational Auto-encoder

In this paper, we propose a novel unsupervised text-to-speech (UTTS) framework which does not require text-audio pairs for the TTS acoustic modeling (AM). UTTS is a multi-speaker speech synthesizer that supports zero-shot voice cloning, it is developed from a perspective of disentangled speech representation learning. The framework offers a flexible choice of a speaker's duration model, timbre feature (identity) and content for TTS inference. We leverage recent advancements in self-supervised speech representation learning as well as speech synthesis front-end techniques for system development. Specifically, we employ our recently formulated Conditional Disentangled Sequential Variational Auto-encoder (C-DSVAE) as the backbone UTTS AM, which offers well-structured content representations given unsupervised alignment (UA) as condition during training. For UTTS inference, we utilize a lexicon to map input text to the phoneme sequence, which is expanded to the frame-level forced alignment (FA) with a speaker-dependent duration model. Then, we develop an alignment mapping module that converts FA to UA. Finally, the C-DSVAE, serving as the self-supervised TTS AM, takes the predicted UA and a target speaker embedding to generate the mel spectrogram, which is ultimately converted to waveform with a neural vocoder. We show how our method enables speech synthesis without using a paired TTS corpus. Experiments demonstrate that UTTS can synthesize speech of high naturalness and intelligibility measured by human and objective evaluations. Audio samples are available at our demo page https://neurtts.github. io/utts_demo.