Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Fully Dynamic k-Centers Clustering

We present the first algorithm for fully dynamic $k$-centers clustering in an arbitrary metric space that maintains an optimal $2+\epsilon$ approximation in $O(k \cdot \operatorname{polylog}(n,\Delta))$ amortized update time. Here, $n$ is an upper bound on the number of active points at any time, and $\Delta$ is the aspect ratio of the data. Previously, the best known amortized update time was $O(k^2\cdot \operatorname{polylog}(n,\Delta))$, and is due to Chan, Gourqin, and Sozio. We demonstrate that the runtime of our algorithm is optimal up to $\operatorname{polylog}(n,\Delta)$ factors, even for insertion-only streams, which closes the complexity of fully dynamic $k$-centers clustering. In particular, we prove that any algorithm for $k$-clustering tasks in arbitrary metric spaces, including $k$-means, $k$-medians, and $k$-centers, must make at least $\Omega(n k)$ distance queries to achieve any non-trivial approximation factor. Despite the lower bound for arbitrary metrics, we demonstrate that an update time sublinear in $k$ is possible for metric spaces which admit locally sensitive hash functions (LSH). Namely, we demonstrate a black-box transformation which takes a locally sensitive hash family for a metric space and produces a faster fully dynamic $k$-centers algorithm for that space. In particular, for a large class of metrics including Euclidean space, $\ell_p$ spaces, the Hamming Metric, and the Jaccard Metric, for any $c>1$, our results yield a $c(4+\epsilon)$ approximate $k$-centers solution in $O(n^{1/c} \cdot \operatorname{polylog}(n,\Delta))$ amortized update time, simultaneously for all $k \geq 1$. Previously, the only known comparable result was a $O(c \log n)$ approximation for Euclidean space due to Schmidt and Sohler, running in the same amortized update time.
Survey of Generative Methods for Social Media Analysis

∗We acknowledge the support of the Communications Security Establishment and Defence Research and Development Canada. The scientific or technical validity of this report is entirely the responsibility of the authors and the contents do not necessarily have the approval or endorsement of the Government of Canada. †Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada and Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland; e-mail: stan@cs.dal.ca ‡Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada; e-mail: amilios@dal.ca §Department of Mathematics, Ryerson University, Toronto, ON, Canada; e-mail: pralat@ryerson.ca ¶Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL, Canada; e-mail: amilcarsj@mun.ca ‖Tutte Institute for Mathematics and Computing, Ottawa, ON, Canada; email: theberge@ieee.org
A stability bound on the T-linear resistivity of conventional metals

Significance Analyses of transport data in metals, both conventional and unconventional, have suggested that the slope of any linear-in-temperature resistivity is bounded from above, leading to many speculative theoretical proposals concerning fundamental bounds on dissipation and thermalization in metals. On the basis of an exact numerical solution of a generic model of the electron–phonon problem, we clearly identify the origin of the bound in conventional metals and associate it with a crossover into a bipolaronic regime when interactions are strong. This result suggests that more broadly, the observed bound may reflect a limit to the interaction strengths between low-energy modes consistent with the stability of a metallic state.
Review: CPG as a controller for biomimetic floating robots

All vertebrates are capable of performing various types of physical activity. Locomotor patterns are created by the cyclical coordinated work of the skeletal muscles. The organization of such a system in living organisms is responsible for networks of interconnected populations of neurons capable of forming rhythmic activity CPG. As biologists have established, CPG is a key mechanism causing adaptive and versatile locomotion in animals. Using a system based on biological principles as a controller for robotic devices will avoid many of the problems that are present in standard control loops. In particular, it becomes possible not only plausible modeling and study of the peculiarities of animal locomotion (in simple cases), but also smooth switching between different types of activity depending on environmental conditions, i.e. creation of autonomous adaptive systems. In this review, a description of the work of recent years is given, in which the CPG is used as a controller for various robotic animals. We focused on the views showing undulating movements, i.e. floating and amphibians. Each separate section deals with a certain kind. At the same time, the works are arranged in the chronological order of their publication. At the end of each section, there is a summary that categorizes the articles described by the type of CPG used.
Secretary Matching With Vertex Arrivals and No Rejections

Most prior work on online matching problems has been with the flexibility of keeping some vertices unmatched. We study three related online matching problems with the constraint of matching every vertex, i.e., with no rejections. We adopt a model in which vertices arrive in a uniformly random order and the edge-weights are arbitrary positive numbers. For the capacitated online bipartite matching problem in which the vertices of one side of the graph are offline and those of the other side arrive online, we give a 4.62-competitive algorithm when the capacity of each offline vertex is 2. For the online general (non-bipartite) matching problem, where all vertices arrive online, we give a 3.34-competitive algorithm. We also study the online roommate matching problem, in which each room (offline vertex) holds 2 persons (online vertices). Persons derive non-negative additive utilities from their room as well as roommate. In this model, with the goal of maximizing the sum of utilities of all persons, we give a 7.96-competitive algorithm. This is an improvement over the 24.72 approximation factor in prior work.
I M Avatar: Implicit Morphable Head Avatars from Videos

Traditional 3D morphable face models (3DMMs) provide fine-grained control over expression but cannot easily capture geometric and appearance details. Neural volumetric representations approach photorealism but are hard to animate and do not generalize well to unseen expressions. To tackle this problem, we propose IMavatar (Implicit Morphable avatar), a novel method for learning implicit head avatars from monocular videos. Inspired by the fine-grained control mechanisms afforded by conventional 3DMMs, we represent the expression- and pose-related deformations via learned blendshapes and skinning fields. These attributes are pose-independent and can be used to morph the canonical geometry and texture fields given novel expression and pose parameters. We employ ray marching and iterative root-finding to locate the canonical surface intersection for each pixel. A key contribution is our novel analytical gradient formulation that enables end-to-end training of IMavatars from videos. We show quantitatively and qualitatively that our method improves geometry and covers a more complete expression space compared to state-of-the-art methods. Code and data can be found at https://ait.ethz. ch/projects/2022/IMavatar/.
Language Models are not Models of Language

Natural Language Processing (NLP) has become one of the leading application areas in the current Artiﬁcial Intelligence boom. Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance in almost all language tasks. Interestingly, when the models are trained with data that includes software code, they demonstrate remarkable abilities in generating functioning computer code from natural language speciﬁcations. We argue that this creates a conundrum for claims that neural models provide an alternative theory to generative phrase structure grammars in explaining how language works. Since the syntax of programming languages is determined by phrase structure grammars, successful neural models are apparently uninformative about the theoretical foundations of programming languages, and by extension, natural languages. We argue that the term language model is misleading because deep learning models are not theoretical models of language and propose the adoption of corpus model instead, which better reﬂects the genesis and contents of the model.
CPM - An efficient constant amplitude modulation scheme

There is an increasing demand for digitial radio transmission. The cost of a channel is proportional to the bandwidth. For many applications power is limited. For satellite applications constant amplitude modulation is advantageous because of non-linear amplification. In this paper a class of power and bandwidth efficient constant amplitude continuous phase modulation (CPM) schemes is presented. The signals are analysed in great detail and the tools for both error probability analysis and power spectral calculations are presented. Numerical data for raised cosine (RC) pulse shaping is presented. This paper is basically an overview with new results on the trellis description of CPM. The transmission is assumed to be ideally coherent over the Gaussian channel. In a following paper we will deal with transmission imperfections such as bandlimitation and hard limiting and their effect on CPM1.
Graph network for simultaneous learning of forward and inverse physics

In this work, we propose an end-to-end graph network that learns forward and inverse models of particle-based physics using interpretable inductive biases. Physics-informed neural networks are often engineered to solve speciﬁc problems through problem-speciﬁc regularization and loss functions. Such explicit learning biases the network to learn data spe-ciﬁc patterns and may require a change in the loss function or neural network architecture hereby limiting their generaliz-abiliy. While recent studies have proposed graph networks to study forward dynamics, they rely on particle speciﬁc parameters such as mass, etc. to approximate the dynamics of the system. Our graph network is implicitly biased by learning to solve several tasks, thereby sharing representations between tasks in order to learn the forward dynamics as well as infer the probability distribution of unknown particle speciﬁc properties. We evaluate our approach on one-step next state prediction tasks across diverse datasets that feature different particle interactions. Our comparison against related data-driven physics learning approaches reveals that our model is able to predict the forward dynamics with at least an order of magnitude higher accuracy. We also show that our approach is able to recover multi-modal probability distributions of unknown physical parameters using orders of magnitude fewer samples.
Edge-trewidth: Algorithmic and combinatorial properties

We introduce the graph theoretical parameter of edge treewidth. This parameter occurs in a natural way as the tree-like analogue of cutwidth or, alternatively, as an edge-analogue of treewidth. We study the combinatorial properties of edge-treewidth. We first observe that edge-treewidth does not enjoy any closeness properties under the known partial ordering relations on graphs. We introduce a variant of the topological minor relation, namely, the weak topological minor relation and we prove that edge-treewidth is closed under weak topological minors. Based on this new relation we are able to provide universal obstructions for edge-treewidth. The proofs are based on the fact that edge-treewidth of a graph is parametetrically equivalent with the maximum over the treewidth and the maximum degree of the blocks of the graph. We also prove that deciding whether the edge-treewidth of a graph is at most k is an NP-complete problem.
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering

Answering semantically complicated questions according to an image is challenging in a visual question answering (VQA) task. Although the image can be well represented by deep learning, the question is always simply embedded and cannot well indicate its meaning. Besides, the visual and textual features have a gap for different modalities, it is difficult to align and utilize the cross-modality information. In this article, we focus on these two problems and propose a graph matching attention (GMA) network. First, it not only builds graph for the image but also constructs graph for the question in terms of both syntactic and embedding information. Next, we explore the intramodality relationships by a dual-stage graph encoder and then present a bilateral cross-modality GMA to infer the relationships between the image and the question. The updated cross-modality features are then sent into the answer prediction module for final answer prediction. Experiments demonstrate that our network achieves the state-of-the-art performance on the GQA dataset and the VQA 2.0 dataset. The ablation studies verify the effectiveness of each module in our GMA network.
Analytical drain thermal noise current model valid for deep submicron MOSFETs

In this paper, a physics-based MOSFET drain thermal noise current model valid for deep submicron channel lengths was derived and verified with experiments. It is found that the well-known /spl mu/Q/sub inv//L/sup 2/ formula, previously derived for long channels, remains valid for short channels. Carrier heating in the gradual channel region was taken into account implicitly with the form of diffusion noise source and then impedance field method taking velocity saturation effect was used to calculate the external drain thermal noise current. The derived model was verified by experimental noise for devices with channel lengths down to 0.18 /spl mu/m. Excellent agreement between measured and modeled drain thermal noise was obtained for the entire V/sub GS/ and V/sub DS/ bias regions.
SoK: A Framework for Unifying At-Risk User Research

At-risk users are people who experience risk factors that augment or amplify their chances of being digitally attacked and/or suffering disproportionate harms. In this systematization work, we present a framework for reasoning about at-risk users based on a wide-ranging meta-analysis of 95 papers. Across the varied populations that we examined (e.g., children, activists, people with disabilities), we identified 10 unifying contextual risk factors —such as marginalization and access to a sensitive resource —that augment or amplify digital-safety risks and their resulting harms. We also identified technical and non-technical practices that at-risk users adopt to attempt to protect themselves from digital-safety risks. We use this framework to discuss barriers that limit at-risk users’ ability or willingness to take protective actions. We believe that researchers and technology creators can use our framework to identify and shape research investments to benefit at-risk users, and to guide technology design to better support at-risk users.
COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks

In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optimal architecture of a convolutional neural network (CNN) that would classify with a high sensitivity among the pulmonary inflammation features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states that COVID-19 radiography images observe unavailability and quality issues impacting the diagnostic process and affecting the accuracy of the deep learning detection models. A significant scarcity of COVID-19 radiography images introduced an imbalance in data motivating us to use over-sampling techniques. In the study, we include an extensive set of X-ray imaging of human lungs (CXR) with COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers to achieve an extensible and accurate CNN model. In the experimentation phase of the research, we evaluated a variety of convolutional network architectures, selecting a sequential convolutional network with two traditional convolutional layers and two pooling layers with maximum function. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95. We chose the Azure Machine Learning service to perform network experimentation and solution deployment. The auto-scaling compute clusters offered a significant time reduction in network training. We would like to see scientists across fields of artificial intelligence and human biology collaborating and expanding on the proposed solution to provide rapid and comprehensive diagnostics, effectively mitigating the spread of the virus
On the gap between deterministic and probabilistic Lyapunov exponents for continuous-time linear systems

Consider a non-autonomous continuous-time linear system in which the time-dependent matrix determining the dynamics is piecewise constant and takes finitely many values $A_1, \dotsc, A_N$. This paper studies the equality cases between the maximal Lyapunov exponent associated with the set of matrices $\{A_1, \dotsc, A_N\}$, on the one hand, and the corresponding ones for piecewise deterministic Markov processes with modes $A_1, \dotsc, A_N$, on the other hand. A fundamental step in this study consists in establishing a result of independent interest, namely, that any sequence of Markov processes associated with the matrices $A_1,\dotsc, A_N$ converges, up to extracting a subsequence, to a Markov process associated with a suitable convex combination of those matrices.
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion

Score-based generative models (SGMs) have demonstrated remarkable synthesis quality. SGMs rely on a diffusion process that gradually perturbs the data towards a tractable distribution, while the generative model learns to denoise. The complexity of this denoising task is, apart from the data distribution itself, uniquely determined by the diffusion process. We argue that current SGMs employ overly simplistic diffusions, leading to unnecessarily complex denoising processes, which limit generative modeling performance. Based on connections to statistical mechanics, we propose a novel critically-damped Langevin diffusion (CLD) and show that CLD-based SGMs achieve superior performance. CLD can be interpreted as running a joint diffusion in an extended space, where the auxiliary variables can be considered “velocities” that are coupled to the data variables as in Hamiltonian dynamics. We derive a novel score matching objective for CLD and show that the model only needs to learn the score function of the conditional distribution of the velocity given data, an easier task than learning scores of the data directly. We also derive a new sampling scheme for efﬁcient synthesis from CLD-based diffusion models. We ﬁnd that CLD outperforms previous SGMs in synthesis quality for similar network architectures and sampling compute budgets. We show that our novel sampler for CLD signiﬁcantly outperforms solvers such as Euler–Maruyama. Our framework provides new insights into score-based denoising diffusion models and can be readily used for high-resolution image synthesis. VPSDE. We leave the study of CLD with maximum likelihood training for high-dimensional (image) datasets to future work.
Thermodynamic and scaling limits of the non-Gaussian membrane model

We characterize the behavior of a random discrete interface $\phi$ on $[-L,L]^d \cap \mathbb{Z}^d$ with energy $\sum V(\Delta \phi(x))$ as $L \to \infty$, where $\Delta$ is the discrete Laplacian and $V$ is a uniformly convex, symmetric, and smooth potential. The interface $\phi$ is called the non-Gaussian membrane model. By analyzing the Helffer-Sj\"ostrand representation associated to $\Delta \phi$, we provide a unified approach to continuous scaling limits of the rescaled and interpolated interface in dimensions $d=2,3$, Gaussian approximation in negative regularity spaces for all $d \geq 2$, and the infinite volume limit in $d \geq 5$. Our results generalize some of those of arXiv:1801.05663.
Improving Compositional Generalization with Latent Structure and Data Augmentation

Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization. Compositional data augmentation via example recombination has transferred some prior knowledge about compositionality to such black-box neural models for several semantic parsing tasks, but this often required task-specific engineering or provided limited gains. We present a more powerful data recombination method using a model called Compositional Structure Learner (CSL). CSL is a generative model with a quasi-synchronous context-free grammar backbone, which we induce from the training data. We sample recombined examples from CSL and add them to the fine-tuning data of a pre-trained sequence-to-sequence model (T5). This procedure effectively transfers most of CSL’s compositional bias to T5 for diagnostic tasks, and results in a model even stronger than a T5-CSL ensemble on two real world compositional generalization tasks. This results in new state-of-the-art performance for these challenging semantic parsing tasks requiring generalization to both natural language variation and novel compositions of elements.
Thermal Features of Compliant Foil Bearings—Theory and Experiments

The paper presents an analytical and experimental investigation aimed at eliciting the thermal characteristics of air lubricated compliant foil bearings. A Couette Approximation to the energy equation is used in conjunction with the compressible Reynolds equation to obtain a theoretical temperature distribution in the air used as a lubricant. The effect of temperature on the thermal properties of the working fluid is included. In parallel, an experimental program was run on a 100 mm diameter foil bearing operating at speeds up to 30,000 rpm employing cooling air across the bearing. The temperature rise of the cooling air provided an indication of the amount of heat energy conducted across the top foil of the bearing from the hydrodynamic film. The temperatures resulted from some tests are compared with the temperatures predicted by the analysis, and maximum over-prediction of about 19 percent was obtained. This simplified approach provides us with reasonably predicted temperatures. By comparing the theoretical heat dissipation obtained from the analytical predicted temperatures with the amount of heat carried away by the cooling air it was possible to arrive at the relative quantities of heat transferred from the bearing by convection via side leakage and by conduction via the top foil. From these comparisons it was deduced that about an average of 80 percent of the heat energy is carried away by conduction. The transient temperatures of the foil bearing in conducted tests for various speeds and loads are also presented.