Miracle-dz/UniFAR-FTUs huggingface dataset:
On The Range Of a Random Walk In A Torus

Let a simple random walk run inside a torus of dimension three or higher for a number of steps which is a constant proportion of the volume. We examine geometric properties of the range, the random subgraph induced by the set of vertices visited by the walk. Distance and mixing bounds for the typical range are proven that are a $k$-iterated log factor from those on the full torus for arbitrary $k$. The proof uses hierarchical renormalization and techniques that can possibly be applied to other random processes in the Euclidean lattice. We use the same technique to bound the heat kernel of a random walk on random interlacements.
Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation

As its core computation, a self-attention mechanism gauges pairwise correlations across the entire input sequence. Despite favorable performance, calculating pairwise correlations is prohibitively costly. While recent work has shown the benefits of runtime pruning of elements with low attention scores, the quadratic complexity of self-attention mechanisms and their on-chip memory capacity demands are overlooked. This work addresses these constraints by architecting an accelerator, called SPRINT1, which leverages the inherent parallelism of ReRAM crossbar arrays to compute attention scores in an approximate manner. Our design prunes the low attention scores using a lightweight analog thresholding circuitry within ReRAM, enabling SPRINT to fetch only a small subset of relevant data to on-chip memory. To mitigate potential negative repercussions for model accuracy, SPRINT re-computes the attention scores for the few fetched data in digital. The combined in-memory pruning and on-chip recompute of the relevant attention scores enables SPRINT to transform quadratic complexity to a merely linear one. In addition, we identify and leverage a dynamic spatial locality between the adjacent attention operations even after pruning, which eliminates costly yet redundant data fetches. We evaluate our proposed technique on a wide range of state-of-the-art transformer models. On average, SPRINT yields 7.5× speedup and 19.6× energy reduction when total l6KB on-chip memory is used, while virtually on par with iso-accuracy of the baseline models (on average 0.36% degradation).
The Fellowship of the Authors: Disambiguating Names from Social Network Context

Most NLP approaches to entity linking and coreference resolution focus on retrieving similar mentions using sparse or dense text representations. The common"Wikification"task, for instance, retrieves candidate Wikipedia articles for each entity mention. For many domains, such as bibliographic citations, authority lists with extensive textual descriptions for each entity are lacking and ambiguous named entities mostly occur in the context of other named entities. Unlike prior work, therefore, we seek to leverage the information that can be gained from looking at association networks of individuals derived from textual evidence in order to disambiguate names. We combine BERT-based mention representations with a variety of graph induction strategies and experiment with supervised and unsupervised cluster inference methods. We experiment with data consisting of lists of names from two domains: bibliographic citations from CrossRef and chains of transmission (isnads) from classical Arabic histories. We find that in-domain language model pretraining can significantly improve mention representations, especially for larger corpora, and that the availability of bibliographic information, such as publication venue or title, can also increase performance on this task. We also present a novel supervised cluster inference model which gives competitive performance for little computational effort, making it ideal for situations where individuals must be identified without relying on an exhaustive authority list.
Searching for Structure in Unfalsifiable Claims

Social media platforms give rise to an abundance of posts and comments on every topic imaginable. Many of these posts express opinions on various aspects of society, but their unfalsifiable nature makes them ill-suited to fact-checking pipelines. In this work, we aim to distill such posts into a small set of narratives that capture the essential claims related to a given topic. Understanding and visualizing these narratives can facilitate more informed debates on social media. As a first step towards systematically identifying the underlying narratives on social media, we introduce PAPYER, a fine-grained dataset of online comments related to hygiene in public restrooms, which contains a multitude of unfalsifiable claims. We present a human-in-the-loop pipeline that uses a combination of machine and human kernels to discover the prevailing narratives and show that this pipeline outperforms recent large transformer models and state-of-the-art unsupervised topic models.
SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketches

2D animation is a common factor in game development, used for characters, effects and background art. It involves work that takes both skill and time, but parts of which are repetitive and tedious. Automated animation approaches exist, but are designed without animators in mind. The focus is heavily on real-life video, which follows strict laws of how objects move, and does not account for the stylistic movement often present in 2D animation. We propose a problem formulation that more closely adheres to the standard workflow of animation. We also demonstrate a model, SketchBetween, which learns to map between keyframes and sketched in-betweens to rendered sprite animations. We demonstrate that our problem formulation provides the required information for the task and that our model outperforms an existing method. 1
Optimal Regularized Online Convex Allocation by Adaptive Re-Solving

This paper introduces a dual-based algorithm framework for solving the regularized online resource allocation problems, which have cumulative convex rewards, hard resource constraints, and a non-separable regularizer. Under a strategy of adaptively updating the resource constraints, the proposed framework only requests an approximate solution to the empirical dual problem up to a certain accuracy, and yet delivers an optimal logarithmic regret under a locally strongly convex assumption. Surprisingly, a delicate analysis of dual objective function enables us to eliminate the notorious loglog factor in regret bound. The flexible framework renders renowned and computationally fast algorithms immediately applicable, e.g., dual gradient descent and stochastic gradient descent. A worst-case square-root regret lower bound is established if the resource constraints are not adaptively updated during dual optimization, which underscores the critical role of adaptive dual variable update. Comprehensive numerical experiments and real data application demonstrate the merits of proposed algorithm framework.
MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment

The visual quality of point clouds has been greatly emphasized since the ever-increasing 3D vision applications are expected to provide cost-effective and high-quality experiences for users. Looking back on the development of point cloud quality assessment (PCQA), the visual quality is usually evaluated by utilizing single-modal information, i.e., either extracted from the 2D projections or 3D point cloud. The 2D projections contain rich texture and semantic information but are highly dependent on viewpoints, while the 3D point clouds are more sensitive to geometry distortions and invariant to viewpoints. Therefore, to leverage the advantages of both point cloud and projected image modalities, we propose a novel no-reference Multi-Modal Point Cloud Quality Assessment (MM-PCQA) metric. In specific, we split the point clouds into sub-models to represent local geometry distortions such as point shift and down-sampling. Then we render the point clouds into 2D image projections for texture feature extraction. To achieve the goals, the sub-models and projected images are encoded with point-based and image-based neural networks. Finally, symmetric cross-modal attention is employed to fuse multi-modal quality-aware information. Experimental results show that our approach outperforms all compared state-of-the-art methods and is far ahead of previous no-reference PCQA methods, which highlights the effectiveness of the proposed method. The code is available at https://github.com/zzc-1998/MM-PCQA.
Lip-to-Speech Synthesis for Arbitrary Speakers in the Wild

In this work, we address the problem of generating speech from silent lip videos for any speaker in the wild. In stark contrast to previous works, our method (i) is not restricted to a fixed number of speakers, (ii) does not explicitly impose constraints on the domain or the vocabulary and (iii) deals with videos that are recorded in the wild as opposed to within laboratory settings. The task presents a host of challenges, with the key one being that many features of the desired target speech, like voice, pitch and linguistic content, cannot be entirely inferred from the silent face video. In order to handle these stochastic variations, we propose a new VAE-GAN architecture that learns to associate the lip and speech sequences amidst the variations. With the help of multiple powerful discriminators that guide the training process, our generator learns to synthesize speech sequences in any voice for the lip movements of any person. Extensive experiments on multiple datasets show that we outperform all baselines by a large margin. Further, our network can be fine-tuned on videos of specific identities to achieve a performance comparable to single-speaker models that are trained on $4\times$ more data. We conduct numerous ablation studies to analyze the effect of different modules of our architecture. We also provide a demo video that demonstrates several qualitative results along with the code and trained models on our website http://cvit.iiit. ac.in/research/projects/cvit-projects/lip-to-speech-synthesis.
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking

The point cloud based 3D single object tracking has drawn increasing attention. Although many breakthroughs have been achieved, we also reveal two severe issues. By extensive analysis, we find the prediction manner of current approaches is non-robust, i.e., exposing a misalignment gap between prediction score and actually localization accuracy. Another issue is the sparse point returns will damage the feature matching procedure of the SOT task. Based on these insights, we introduce two novel modules, i.e., Adaptive Refine Prediction (ARP) and Target Knowledge Transfer (TKT), to tackle them, respectively. To this end, we first design a strong pipeline to extract discriminative features and conduct the matching with the attention mechanism. Then, ARP module is proposed to tackle the misalignment issue by aggregating all predicted candidates with valuable clues. Finally, TKT module is designed to effectively overcome incomplete point cloud due to sparse and occlusion issues. We call our overall framework PCET. By conducting extensive experiments on the KITTI and Waymo Open Dataset, our model achieves state-of-the-art performance while maintaining a lower computational cost.
Hidden Author Bias in Book Recommendation

Collaborative filtering algorithms have the advantage of not requiring sensitive user or item information to provide recommendations. However, they still suffer from fairness related issues, like popularity bias. In this work, we argue that popularity bias often leads to other biases that are not obvious when additional user or item information is not provided to the researcher. We examine our hypothesis in the book recommendation case on a commonly used dataset with book ratings. We enrich it with author information using publicly available external sources. We find that popular books are mainly written by US citizens in the dataset, and that these books tend to be recommended disproportionally by popular collaborative filtering algorithms compared to the users' profiles. We conclude that the societal implications of popularity bias should be further examined by the scholar community.
Inequality of Opportunity and Income Redistribution

We examine how redistribution decisions respond to the source of luck when there is uncertainty about its role in determining opportunities and outcomes. We elicit redistribution decisions from a representative U.S. sample who observe worker outcomes and whether luck could determine earnings directly (``lucky outcomes'') or indirectly by providing one of the workers with a relative advantage (``lucky opportunities''). We find that participants redistribute less and are less responsive to changes in the importance of luck in environments with lucky opportunities. We show that individuals rely on a simple heuristic when assessing the impact of unequal opportunities, which leads them to underappreciate the extent to which small differences in opportunities can have a large impact on outcomes. These findings have implications for models of redistribution attitudes and help explain the gap between lab evidence on support for redistribution and inequality trends.
Memory effects in multipartite systems coupled by nondiagonal dephasing mechanisms

The developing of (non-Markovian) memory effects strongly depends on the underlying system-environment dynamics. Here we study this problem in multipartite arrangements where all subsystems are coupled to each other by non-diagonal Markovian (Lindblad) dephasing mechanisms. Taking as system and environment arbitrary sets of complementary subsystems it is shown that both operational and non-operational approaches to quantum non-Markovianity can be characterized in an exact analytical way. Similarly to previous studies about dissipative-entanglement-generation in this kind of dynamics [Seif, Wang, and Clerk, Phys. Rev. Lett. 128, 070402 (2022)], we found that memory effects can only emerge when a time-reversal symmetry is broken. Nevertheless, it is also found that departures from Markovianity can equivalently be represented through a statistical mixture of Markovian dephasing dynamics, which does not involve any system-environment entanglement. Specific bipartite and multipartite dynamics exemplify the main general results.
Public Parking Spot Detection And Geo-localization Using Transfer Learning

In cities around the world, locating public parking lots with vacant parking spots is a major problem, costing commuters time and adding to traffic congestion. This work illustrates how a dataset of Geo-tagged images from a mobile phone camera, can be used in navigating to the most convenient public parking lot in Johannesburg with an available parking space, detected by a neural network powered-public camera. The images are used to fine-tune a Detectron2 model pre-trained on the ImageNet dataset to demonstrate detection and segmentation of vacant parking spots, we then add the parking lot's corresponding longitude and latitude coordinates to recommend the most convenient parking lot to the driver based on the Haversine distance and number of available parking spots. Using the VGG Image Annotation (VIA) we use images from an expanding dataset of images, and annotate these with polygon outlines of the four different types of objects of interest: cars, open parking spots, people, and car number plates. We use the segmentation model to ensure number plates can be occluded in production for car registration anonymity purposes. We get an 89% and 82% intersection over union cover score on cars and parking spaces respectively. This work has the potential to help reduce the amount of time commuters spend searching for free public parking, hence easing traffic congestion in and around shopping complexes and other public places, and maximize people's utility with respect to driving on public roads.
Combating Noisy Labels in Long-Tailed Image Classification

Most existing methods that cope with noisy labels usually assume that the class distributions are well balanced, which has insufficient capacity to deal with the practical scenarios where training samples have imbalanced distributions. To this end, this paper makes an early effort to tackle the image classification task with both long-tailed distribution and label noise. Existing noise-robust learning methods cannot work in this scenario as it is challenging to differentiate noisy samples from clean samples of tail classes. To deal with this problem, we propose a new learning paradigm based on matching between inferences on weak and strong data augmentations to screen out noisy samples and introduce a leave-noise-out regularization to eliminate the effect of the recognized noisy samples. Furthermore, we incorporate a novel prediction penalty based on online prior distribution to avoid bias towards head classes. This mechanism has superiority in capturing the class fitting degree in realtime compared to the existing long-tail classification methods. Exhaustive experiments demonstrate that the proposed method outperforms state-of-the-art algorithms that address the distribution imbalance problem in long-tailed classification under noisy labels.
Charged Particle Circular Orbits around Weakly Charged and Magnetized Kerr Black Holes

We study the circular orbits of charged particles around a weakly charged Kerr black hole immersed in a weak, axisymmetric magnetic field. First, we review the circular orbits of neutral particles. We then review the circular orbits of charged particles around a weakly charged Kerr black hole and weakly magnetized Kerr black hole. The case of a weakly magnetized and charged black hole is investigated thereafter. We investigate, in particular, the effect of the electromagnetic forces on the charged particles’ innermost stable circular orbits. We examine the conditions for the existence of negative-energy stable circular orbits and the possibility of the emergence of a gap or double orbit in thin accretion disks. Some of the interesting astrophysical consequences of our findings are discussed as well.
AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System

We propose AccoMontage2, a system capable of doing full-length song harmonization and accompaniment arrangement based on a lead melody. Following AccoMontage, this study focuses on generating piano arrangements for popular/folk songs and it carries on the generalized template-based retrieval method. The novelties of this study are twofold. First, we invent a harmonization module (which AccoMontage does not have). This module generates structured and coherent full-length chord progression by optimizing and balancing three loss terms: a micro-level loss for note-wise dissonance, a meso-level loss for phrase-template matching, and a macro-level loss for full piece coherency. Second, we develop a graphical user interface which allows users to select different styles of chord progression and piano texture. Currently, chord progression styles include Pop, R&B, and Dark, while piano texture styles include several levels of voicing density and rhythmic complexity. Experimental results show that both our harmonization and arrangement results significantly outperform the baselines. Lastly, we release AccoMontage2 as an online application as well as the organized chord progression templates as a public dataset.
Experimental verification of off-axis polarimetry with cadmium zinc telluride detectors of AstroSat-CZT Imager

Abstract. The cadmium zinc telluride imager (CZTI) on board AstroSat consists of an array of a large number of pixelated cadmium zinc telluride (CZT) detectors capable of measuring the polarization of incident hard x-rays. The polarization measurement capability of CZTI for on-axis sources was experimentally confirmed before the launch. CZTI has yielded tantalizing results on the x-ray polarization of the Crab nebula and pulsar in the energy range of 100 to 380 keV. CZTI has also contributed to the measurement of prompt emission polarization for several gamma-ray bursts (GRBs). However, polarization measurements of off-axis sources such as GRBs are challenging. It is vital to experimentally calibrate the CZTI sensitivity to off-axis sources to enhance the credence of the measurements. In this context, we report the verification of the off-axis polarimetric capability of pixelated CZT detectors through controlled experiments carried out with a CZT detector similar to that used in CZTI and extensive Geant4 simulations of the experimental setup. Our current results show that the CZT detectors can be used to measure the polarization of bright GRBs with off-axis angles of up to ∼60 deg. However, at incidence angles between 45 deg and 60 deg, there might be some systematic effects that need to be taken into account when interpreting the measured polarization fraction.
Computational Control of Networks of Dynamical Systems: Application to the National Airspace System

The research presented in this dissertation is motivated by the need for efficient analysis, automation, and optimization tools for the National Airspace System (NAS) and more generally for large scale networks of dynamical systems. A new modeling framework based on hybrid system theory is developed, which captures congestion propagation into the Air Traffic Control (ATC) system. This model is validated against Enhanced Traffic Management System (ETMS) data and used for analyzing low level actuation of the human Air Traffic Controller. This model enables us to quantify the capacity limit of the airspace in terms of geometry and traffic patterns, as well as the speed of propagation of congestion in the system. Once this setting is in place, maneuver assignment problems are posed as optimization programs, some of which can be reduced to Mixed Integer Linear Programs (MILPs). Problem specific algorithms are designed to show that certain MILPs can be solved exactly in polynomial time. These algorithms are shown to run faster than CPLEX (the leading commercial software to solve MILPs) when implemented on the same platforms. For other problems, approximation algorithms are designed, with guaranteed bounds on running time and performance. An architecture is proposed for the implementation of this method using a live ETMS data feed. Flow control problems in the NAS are modeled using an Eulerian framework. A Partial Differential Equation (PDE) model of high altitude traffic is derived, using a modified Lighthill-Whitham-Richards (LWR) PDE. High altitude traffic is modeled as a network of LWR PDEs linked through their boundary conditions. The model is validated against ETMS data. A new adjoint-based method is developed for controlling ATC network flow management problems and successfully applied to realistic scenarios for the airspace between Chicago and the east coast. Accurate numerical analysis schemes are used and run very
KoCHET: A Korean Cultural Heritage Corpus for Entity-related Tasks

As digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential. To achieve this, we propose KoCHET - a Korean cultural heritage corpus for the typical entity-related tasks, i.e., named entity recognition (NER), relation extraction (RE), and entity typing (ET). Advised by cultural heritage experts based on the data construction guidelines of government-affiliated organizations, KoCHET consists of respectively 112,362, 38,765, 113,198 examples for NER, RE, and ET tasks, covering all entity types related to Korean cultural heritage. Moreover, unlike the existing public corpora, modified redistribution can be allowed both domestic and foreign researchers. Our experimental results make the practical usability of KoCHET more valuable in terms of cultural heritage. We also provide practical insights of KoCHET in terms of statistical and linguistic analysis. Our corpus is freely available at https://github.com/Gyeongmin47/KoCHET.