Miracle-dz/UniFAR-FTUs huggingface dataset:
Amodal Optical Flow

Optical flow estimation is very challenging in situations with transparent or occluded objects. In this work, we address these challenges at the task level by introducing Amodal Optical Flow, which integrates optical flow with amodal perception. Instead of only representing the visible regions, we define amodal optical flow as a multi-layered pixel-level motion field that encompasses both visible and occluded regions of the scene. To facilitate research on this new task, we extend the AmodalSynthDrive dataset to include pixel-level labels for amodal optical flow estimation. We present several strong baselines, along with the Amodal Flow Quality metric to quantify the performance in an interpretable manner. Furthermore, we propose the novel AmodalFlowNet as an initial step toward addressing this task. AmodalFlowNet consists of a transformer-based cost-volume encoder paired with a recurrent transformer decoder which facilitates recurrent hierarchical feature propagation and amodal semantic grounding. We demonstrate the tractability of amodal optical flow in extensive experiments and show its utility for downstream tasks such as panoptic tracking. We make the dataset, code, and trained models publicly available at http://amodal-flow.cs. uni-freiburg.de.
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework

Vast amounts of astronomical photometric data are generated from various projects, requiring significant efforts to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework would simplify the task of designing custom classifiers. We present a novel deep learning framework for classifying light curves using a weakly supervised object detection model. Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data. This allows for automatic feature extraction from both time and frequency domains, enabling our model to handle data across different scales and sampling intervals. We train our model on datasets obtained from both space-based and ground-based multi-band observations of variable stars and transients. We achieve an accuracy of 87% for combined variables and transient events, which is comparable to the performance of previous feature-based models. Our trained model can be utilized directly to other missions, such as ASAS-SN, without requiring any retraining or fine-tuning. To address known issues with miscalibrated predictive probabilities, we apply conformal prediction to generate robust predictive sets that guarantee true label coverage with a given probability. Additionally, we incorporate various anomaly detection algorithms to empower our model with the ability to identify out-of-distribution objects. Our framework is implemented in the Deep-LC toolkit, which is an open-source Python package hosted on Github and PyPI.
PEMS: Pre-trained Epidemic Time-series Models

Providing accurate and reliable predictions about the future of an epidemic is an important problem for enabling informed public health decisions. Recent works have shown that leveraging data-driven solutions that utilize advances in deep learning methods to learn from past data of an epidemic often outperform traditional mechanistic models. However, in many cases, the past data is sparse and may not sufficiently capture the underlying dynamics. While there exists a large amount of data from past epidemics, leveraging prior knowledge from time-series data of other diseases is a non-trivial challenge. Motivated by the success of pre-trained models in language and vision tasks, we tackle the problem of pre-training epidemic time-series models to learn from multiple datasets from different diseases and epidemics. We introduce Pre-trained Epidemic Time-Series Models (PEMS) that learn from diverse time-series datasets of a variety of diseases by formulating pre-training as a set of self-supervised learning (SSL) tasks. We tackle various important challenges specific to pre-training for epidemic time-series such as dealing with heterogeneous dynamics and efficiently capturing useful patterns from multiple epidemic datasets by carefully designing the SSL tasks to learn important priors about the epidemic dynamics that can be leveraged for fine-tuning to multiple downstream tasks. The resultant PEM outperforms previous state-of-the-art methods in various downstream time-series tasks across datasets of varying seasonal patterns, geography, and mechanism of contagion including the novel Covid-19 pandemic unseen in pre-trained data with better efficiency using smaller fraction of datasets.
Ensemble sampling for linear bandits: small ensembles suffice

We provide the first useful and rigorous analysis of ensemble sampling for the stochastic linear bandit setting. In particular, we show that, under standard assumptions, for a $d$-dimensional stochastic linear bandit with an interaction horizon $T$, ensemble sampling with an ensemble of size of order $\smash{d \log T}$ incurs regret at most of the order $\smash{(d \log T)^{5/2} \sqrt{T}}$. Ours is the first result in any structured setting not to require the size of the ensemble to scale linearly with $T$ -- which defeats the purpose of ensemble sampling -- while obtaining near $\smash{\sqrt{T}}$ order regret. Ours is also the first result that allows infinite action sets.
Leveraging Large Language Models to Detect Influence Campaigns in Social Media

Social media influence campaigns pose significant challenges to public discourse and democracy. Traditional detection methods fall short due to the complexity and dynamic nature of social media. Addressing this, we propose a novel detection method using Large Language Models (LLMs) that incorporates both user metadata and network structures. By converting these elements into a text format, our approach effectively processes multilingual content and adapts to the shifting tactics of malicious campaign actors. We validate our model through rigorous testing on multiple datasets, showcasing its superior performance in identifying influence efforts. This research not only offers a powerful tool for detecting campaigns, but also sets the stage for future enhancements to keep up with the fast-paced evolution of social media-based influence tactics.
How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions

While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition? This study seeks to explore this question through the lens of syntax, a crucial component of sentence comprehension. Adopting a natural language question-answering (Q&A) scheme, we craft questions targeting nine syntactic knowledge points that are most closely related to sentence comprehension. Experiments conducted on 24 LLMs suggest that most have a limited grasp of syntactic knowledge, exhibiting notable discrepancies across different syntactic knowledge points. In particular, questions involving prepositional phrase attachment pose the greatest challenge, whereas those concerning adjectival modifier and indirect object are relatively easier for LLMs to handle. Furthermore, a case study on the training dynamics of the LLMs reveals that the majority of syntactic knowledge is learned during the initial stages of training, hinting that simply increasing the number of training tokens may not be the `silver bullet' for improving the comprehension ability of LLMs.
On the properties of the density matrix of the $\mathfrak{sl}_{n+1}$-invariant model

We present an ansatz of generalizing the construction of recursion relations for the correlation functions of the $\mathfrak{sl}_2$-invariant fundamental exchange model in the thermodynamic limit by Jimbo, Miwa, Smirnov, Takeyama and one of our present authors in 2004 for higher rank. Due to the structure of the correlators as functions of their inhomogeneity parameters, a recursion formula for the reduced density matrix was proven. In the case of $\mathfrak{sl}_3$ we use the explicit results of Kl\"umper and Ribeiro and Nirov, Hutsalyuk and one of our present authors for the reduced density matrix of up to operator length three to verify whether it is possible to relate the residues of the density matrix of length $n$ to the density matrix of length smaller than $n$ as in $\mathfrak{sl}_2$. This is unclear, since the reduced quantum Knizhnik--Zamolodchikov equation splits into two parts for higher rank. In fact, we show two relations, one of which is analogous to the $\mathfrak{sl}_2$ case and one which is new. This allows us to construct an analogue of the operator $X_k$, which we call Snail Operator. In the $\mathfrak{sl}_2$ case this operator has many nice properties including in particular the fact that only one irreducible representation of the Yangian $Y(\mathfrak{sl}_2)$, the Kirillov--Reshetikhin module $W_k$, contributed the residue at $\lambda_i-\lambda_j=-(k+1)$.
Conditions for recurrence and transience for time-inhomogeneous random walks

The present paper extends the earlier results by [Abramov, V. M. Conditions for recurrence and transience for time-inhomogeneous birth-and-death processes, \emph{Bull. Aust. Math. Soc. } (to appear)] for the case of time-inhomogeneous random walks, the increments of which take values in $\mathbb{R}$. By this, we give a full solution of the open problem formulated by [Menshikov, M. and Volkov, S. Urn-related random walk with drift $\rho x^\alpha/t^\beta$, \emph{Electron. J. Probab. }, \textbf{13} (2008), paper No. 31, 944--960] that was partially solved in the aforementioned paper by Abramov.
Protecting the Future of Information: LOCO Coding With Error Detection for DNA Data Storage

DNA strands serve as a storage medium for $4$-ary data over the alphabet $\{A,T,G,C\}$. DNA data storage promises formidable information density, long-term durability, and ease of replicability. However, information in this intriguing storage technology might be corrupted. Experiments have revealed that DNA sequences with long homopolymers and/or with low $GC$-content are notably more subject to errors upon storage. This paper investigates the utilization of the recently-introduced method for designing lexicographically-ordered constrained (LOCO) codes in DNA data storage. This paper introduces DNA LOCO (D-LOCO) codes, over the alphabet $\{A,T,G,C\}$ with limited runs of identical symbols. These codes come with an encoding-decoding rule we derive, which provides affordable encoding-decoding algorithms. In terms of storage overhead, the proposed encoding-decoding algorithms outperform those in the existing literature. Our algorithms are readily reconfigurable. D-LOCO codes are intrinsically balanced, which allows us to achieve balancing over the entire DNA strand with minimal rate penalty. Moreover, we propose four schemes to bridge consecutive codewords, three of which guarantee single substitution error detection per codeword. We examine the probability of undetecting errors. We also show that D-LOCO codes are capacity-achieving and that they offer remarkably high rates at moderate lengths.
Topology Management in Rooftop Wireless Networks

The lack of access to broadband Internet services in rural areas of New Zealand has been identified as a significant problem for rural businesses and households. The existing wired telephone network which is being used for broadband delivery in urban areas is around fifty years old in most rural areas and is not capable of sustaining the speeds required for broadband access. This problem has led to the idea of using wireless technologies to provide broadband access to rural areas. This report investigates existing wireless network technologies and mesh implementations and describes why they are unsuitable for use in a rural environment. Drawing on the experiences of these existing networks a protocol to create a wireless mesh network for use in rural areas is designed. The mesh network created by this protocol is different from existing mesh networks in a number of ways. Each node in the mesh network has multiple wireless interfaces and utilises directional antenna to reduce interference and increase communication range. Additionally the protocol creates a mesh network composed of many layer 2 wireless Ethernet links rather than a single link. The successful implementation of this protocol within a framework that enables future research to be easily performed is a significant achievement of this project. The results of evaluating this framework and the mesh protocol in two test bed networks are also presented.
Estimating Conditional Value-at-Risk with Nonstationary Quantile Predictive Regression Models

This paper develops an asymptotic distribution theory for a two-stage instrumentation estimation approach in quantile predictive regressions when both generated covariates and persistent predictors are used. The generated covariates are obtained from an auxiliary quantile regression model and our main interest is the robust estimation and inference of the primary quantile predictive regression in which this generated covariate is added to the set of nonstationary regressors. We find that the proposed doubly IVX estimator is robust to the abstract degree of persistence regardless of the presence of generated regressor obtained from the first stage procedure. The asymptotic properties of the two-stage IVX estimator such as mixed Gaussianity are established while the asymptotic covariance matrix is adjusted to account for the first-step estimation error.
Optimising non-Newtonian fluids for impact protection of laminates

Non-Newtonian fluids can be used for the protection of flexible laminates. Understanding the coupling between the flow of the protecting fluid and the deformation of the protected solids is necessary in order to optimise this functionality. We present a scaling analysis of the problem based on a single coupling variable, the effective width of a squeeze flow between flat rigid plates, and predict that impact protection for laminates is optimised by using shear-thinning, and not shear-thickening, fluids. The prediction is verified experimentally by measuring the velocity and pressure in impact experiments. Our scaling analysis should be generically applicable for non-Newtonian fluid-solid interactions in diverse applications.
Boundary null controllability of degenerate wave equation as the limit of internal controllability

This work is concerned with the possibility of proving the boundary null controllability for the degenerate wave equation, developing the asymptotic analysis of a suitable family of state-control pairs $((u_\varepsilon , v_\varepsilon))_{\varepsilon>0}$, solving related internal null controllability problems. The passage to the limit argument will be rigorously performed through the obtainment of a refined observalitity type inequality, with a constant explicitly given in terms of $\varepsilon>0$. This represents an essential point, since will allow us to achieve our required weak convergence results.
A seismic refraction investigation of crustal structure beneath the Lesser Antilles island arc

Summary. A seismic refraction experiment along am across the Lesser Antilles island arc in the eastern Caribbean has shown that the crust beneath the arc contains two refractors with average seismic velocities of 6.2 and 6.9 km/s. The upper, 6.2 km/s refractor varies in depth and shows large fluctuations in its seismic velocity. The layer betweeen the refractors has an average thickness of about 10 km, and varies between 2 and 20 km. The total thickness of the crust of the arc is about 30 km beneath Martinique and about 35 km beneath St Vincent. The approximate depths of the Moho beneath the centres of Grenada and Tobago Troughs either side of the arc are 21 and 25 km respectively. Both these troughs, although of different origin, are underlain by crust of modified oceanic type.
ReIDTracker Sea: the technical report of BoaTrack and SeaDronesSee-MOT challenge at MaCVi of WACV24

Multi-Object Tracking is one of the most important technologies in maritime computer vision. Our solution tries to explore Multi-Object Tracking in maritime Unmanned Aerial vehicles (UAVs) and Unmanned Surface Vehicles (USVs) usage scenarios. Most of the current Multi-Object Tracking algorithms require complex association strategies and association information (2D location and motion, 3D motion, 3D depth, 2D appearance) to achieve better performance, which makes the entire tracking system extremely complex and heavy. At the same time, most of the current Multi-Object Tracking algorithms still require video annotation data which is costly to obtain for training. Our solution tries to explore Multi-Object Tracking in a completely unsupervised way. The scheme accomplishes instance representation learning by using self-supervision on ImageNet. Then, by cooperating with high-quality detectors, the multi-target tracking task can be completed simply and efficiently. The scheme achieved top 3 performance on both UAV-based Multi-Object Tracking with Reidentification and USV-based Multi-Object Tracking benchmarks and the solution won the championship in many multiple Multi-Object Tracking competitions. such as BDD100K MOT,MOTS, Waymo 2D MOT
Spectral Line Analysis/Modeling (SLAM) I: pvanalysis

Line observations of young stellar objects (YSOs) at (sub)millimeter wavelengths provide essential information of gas kinematics in star and planet forming environments. For Class 0 and I YSOs, identification of Keplerian rotation is of particular interest, because it reveals presence of rotationally-supported disks that are still being embedded in infalling envelopes and enables us to dynamically measure the protostellar mass. We have developed a python library SLAM (Spectral Line Analysis/Modeling) with a primary focus on analyses of emission line data at (sub)millimeter wavelengths. Here, we present an overview of the pvanalysis tool from SLAM, which is designed to identify Keplerian rotation of a disk and measure the dynamical mass of a central object using a position-velocity (PV) diagram of emission line data. The advantage of this tool is that it analyzes observational features of given data and thus requires few computational time and parameter assumptions, in contrast to detailed radiative transfer modelings. In this article, we introduce the basic concept and usage of this tool, present an application to observational data, and discuss remaining caveats.
Discretized Distributed Optimization over Dynamic Digraphs

We consider a discrete-time model of continuous-time distributed optimization over dynamic directed-graphs (digraphs) with applications to distributed learning. Our optimization algorithm works over general strongly connected dynamic networks under switching topologies, e.g., in mobile multi-agent systems and volatile networks due to link failures. Compared to many existing lines of work, there is no need for bi-stochastic weight designs on the links. The existing literature mostly needs the link weights to be stochastic using specific weight-design algorithms needed both at the initialization and at all times when the topology of the network changes. This paper eliminates the need for such algorithms and paves the way for distributed optimization over time-varying digraphs. We derive the bound on the gradient-tracking step-size and discrete time-step for convergence and prove dynamic stability using arguments from consensus algorithms, matrix perturbation theory, and Lyapunov theory. This work, particularly, is an improvement over existing stochastic-weight undirected networks in case of link removal or packet drops. This is because the existing literature may need to rerun time-consuming and computationally complex algorithms for stochastic design, while the proposed strategy works as long as the underlying network is weight-symmetric and balanced. The proposed optimization framework finds applications to distributed classification and learning.
Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized method

Analogical reasoning derives information from known relations and generalizes this information to similar yet unfamiliar situations. One of the first generalized ways in which deep learning models were able to solve verbal analogies was through vector arithmetic of word embeddings, essentially relating words that were mapped to a vector space (e.g., king - man + woman = __? ). In comparison, most attempts to solve visual analogies are still predominantly task-specific and less generalizable. This project focuses on visual analogical reasoning and applies the initial generalized mechanism used to solve verbal analogies to the visual realm. Taking the Abstraction and Reasoning Corpus (ARC) as an example to investigate visual analogy solving, we use a variational autoencoder (VAE) to transform ARC items into low-dimensional latent vectors, analogous to the word embeddings used in the verbal approaches. Through simple vector arithmetic, underlying rules of ARC items are discovered and used to solve them. Results indicate that the approach works well on simple items with fewer dimensions (i.e., few colors used, uniform shapes), similar input-to-output examples, and high reconstruction accuracy on the VAE. Predictions on more complex items showed stronger deviations from expected outputs, although, predictions still often approximated parts of the item's rule set. Error patterns indicated that the model works as intended. On the official ARC paradigm, the model achieved a score of 2% (cf. current world record is 21%) and on ConceptARC it scored 8.8%. Although the methodology proposed involves basic dimensionality reduction techniques and standard vector arithmetic, this approach demonstrates promising outcomes on ARC and can easily be generalized to other abstract visual reasoning tasks.
Local Limit of Nonlocal Gravity: Cosmological Perturbations

We explore the cosmological implications of the local limit of nonlocal gravity, which is a classical generalization of Einstein's theory of gravitation within the framework of teleparallelism. An appropriate solution of this theory is the modified Cartesian flat cosmological model. The main purpose of this paper is to study linear perturbations about the orthonormal tetrad frame field adapted to the standard comoving observers in this model. The observational viability of the perturbed model is examined using all available data regarding the cosmic microwave background. The implications of the linearly perturbed modified Cartesian flat model are examined and it is shown that the model is capable of alleviating the $H_0$ tension.
Spectral properties of random graphs with fixed equitable partition

We define a graph to be $S$-regular if it contains an equitable partition given by a matrix $S$. These graphs are generalizations of both regular and bipartite, biregular graphs. An $S$-regular matrix is defined then as a matrix on an $S$-regular graph consistent with the graph's equitable partition. In this paper we derive the limiting spectral density for large, random $S$-regular matrices as well as limiting functions of certain statistics for their eigenvector coordinates as a function of eigenvalue. These limiting functions are defined in terms of spectral measures on $S$-regular trees. In general, these spectral measures do not have a closed-form expression; however, we provide a defining system of polynomials for them. Finally, we explore eigenvalue bounds of $S$-regular graph, proving an expander mixing lemma, Alon-Bopana bound, and other eigenvalue inequalities in terms of the eigenvalues of the matrix $S$.