OpenRaiser/Intern-Atlas huggingface dataset:
Spatial Parrondo games with spatially dependent game $A$
Venue: arXiv cs.GT 2020
Year: 2020

Parrondo games with spatial dependence were introduced by Toral (2001) and have been studied extensively. In Toral's model, $N$ players are arranged in a circle. The players play either game $A$ or game $B$. In game $A$, a randomly chosen player wins or loses one unit according to the toss of a fair coin. In game $B$, which depends on parameters $p_0,p_1,p_2\in[0,1]$, a randomly chosen player, player $x$ say, wins or loses one unit according to the toss of a $p_m$-coin, where $m\in\{0,1,2\}$ is the number of nearest neighbors of player $x$ who won their most recent game. In this paper, we replace game $A$ by a spatially dependent game, which we call game $A'$, introduced by Xie et al.~(2011). In game $A'$, two nearest neighbors are chosen at random, and one pays one unit to the other based on the toss of a fair coin. Noting that game $A'$ is fair, we say that the \textit{Parrondo effect} occurs if game $B$ is losing or fair and game $C'$, determined by a random or periodic sequence of games $A'$ and $B$, is winning. We investigate numerically the region in which the Parrondo effect appears. We give sufficient conditions for the mean profit in game $C'$ to converge as $N\to\infty$. Finally, we compare the Parrondo region in the model of Xie et al.\ with that in the model of Toral.
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
Venue: arXiv cs.CV 2020
Year: 2020

We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisingly effective at the task of compressing many views of a scene into a learned function which maps from a viewing ray to an observed radiance value via volume rendering. Unfortunately, these methods lose all their predictive power once any object in the scene has moved. In this work, we explicitly model rigid motion of objects in the context of neural representations of radiance fields. We show that without any additional human specified supervision, we can reconstruct a dynamic scene with a single rigid object in motion by simultaneously decomposing it into its two constituent parts and encoding each with its own neural representation. We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame. On both synthetic and real world datasets, we demonstrate that our method can render photorealistic novel views, where novelty is measured on both spatial and temporal axes. Our factored representation furthermore enables animation of unseen object motion.
Routing Approach for P2P Systems Over MANET Network
Venue: arXiv cs.NI 2020
Year: 2020

Thanks to the great progress in mobile and wireless technologies, Internet-distributed applications like P2P file sharing are nowadays deployed over MANET (i.e., P2P mobile systems). These applications allow users to search and share diverse multimedia resources over MANET. Due the nature of MANET, P2P mobile systems brought up many new thriving challenges regarding the query routing issue. To tackle this problem, we introduce a novel context-aware query routing protocol for unstructured P2P mobile file sharing systems. Our protocol (i) locates relevant peers sharing pertinent resources for user's query and (ii) ensures that those peers would be reached by considering different MANET constraints (e.g., query content, peer mobility, battery energy, peer load). In order to consider all these constraints for choosing the relevant peers, we are based on the technique for order preferences by similarity to ideal solution (TOPSIS). We implemented the proposed protocol and compared its routing efficiency and retrieval effectiveness with another protocol taken from the literature. Experimental results show that our scheme carries out better than the baseline protocol with respect to accuracy
Měj Přehled / Be informed
Venue: arXiv cs.SI 2020
Year: 2020

The amount of information available to the general public is enormous, and it is challenging to extract meaningful and reliable content. The availability of news sources and their trustability are the biggest problems for selecting the proper authority. Moreover, news providers' inability to publish good news on social media creates a gap for young people to access trustworthy information. This article describes an automated system that can extract the essential news and publish them in a different form on social media networks.
Formatting the Landscape: Spatial conditional GAN for varying population in satellite imagery
Venue: arXiv cs.CV 2020
Year: 2020

Climate change is expected to reshuffle the settlement landscape: forcing people in affected areas to migrate, to change their lifeways, and continuing to affect demographic change throughout the world. Changes to the geographic distribution of population will have dramatic impacts on land use and land cover and thus constitute one of the major challenges of planning for climate change scenarios. In this paper, we explore a generative model framework for generating satellite imagery conditional on gridded population distributions. We make additions to the existing ALAE architecture, creating a spatially conditional version: SCALAE. This method allows us to explicitly disentangle population from the model's latent space and thus input custom population forecasts into the generated imagery. We postulate that such imagery could then be directly used for land cover and land use change estimation using existing frameworks, as well as for realistic visualisation of expected local change. We evaluate the model by comparing pixel and semantic reconstructions, as well as calculate the standard FID metric. The results suggest the model captures population distributions accurately and delivers a controllable method to generate realistic satellite imagery.
Ellipse Regression with Predicted Uncertainties for Accurate Multi-View 3D Object Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Convolutional neural network (CNN) based architectures, such as Mask R-CNN, constitute the state of the art in object detection and segmentation. Recently, these methods have been extended for model-based segmentation where the network outputs the parameters of a geometric model (e.g. an ellipse) directly. This work considers objects whose three-dimensional models can be represented as ellipsoids. We present a variant of Mask R-CNN for estimating the parameters of ellipsoidal objects by segmenting each object and accurately regressing the parameters of projection ellipses. We show that model regression is sensitive to the underlying occlusion scenario and that prediction quality for each object needs to be characterized individually for accurate 3D object estimation. We present a novel ellipse regression loss which can learn the offset parameters with their uncertainties and quantify the overall geometric quality of detection for each ellipse. These values, in turn, allow us to fuse multi-view detections to obtain 3D ellipsoid parameters in a principled fashion. The experiments on both synthetic and real datasets quantitatively demonstrate the high accuracy of our proposed method in estimating 3D objects under heavy occlusions compared to previous state-of-the-art methods.
On the human-recognizability phenomenon of adversarially trained deep image classifiers
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we investigate the phenomenon that robust image classifiers have human-recognizable features -- often referred to as interpretability -- as revealed through the input gradients of their score functions and their subsequent adversarial perturbations. In particular, we demonstrate that state-of-the-art methods for adversarial training incorporate two terms -- one that orients the decision boundary via minimizing the expected loss, and another that induces smoothness of the classifier's decision surface by penalizing the local Lipschitz constant. Through this demonstration, we provide a unified discussion of gradient and Jacobian-based regularizers that have been used to encourage adversarial robustness in prior works. Following this discussion, we give qualitative evidence that the coupling of smoothness and orientation of the decision boundary is sufficient to induce the aforementioned human-recognizability phenomenon.
A second-order self-adjusting steepness based remapping method for arbitrary quadrilateral meshes
Venue: arXiv cs.CE 2020
Year: 2020

In this paper, based on the idea of self-adjusting steepness based schemes[5], a two-dimensional calculation method of steepness parameter is proposed, and thus a two-dimensional self-adjusting steepness based limiter is constructed. With the application of such limiter to the over-intersection based remapping framework, a low dissipation remapping method has been proposed that can be applied to the existing ALE method.
Incorporating Expert Guidance in Epidemic Forecasting
Venue: arXiv cs.LG 2020
Year: 2020

Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods. While these methods have achieved qualified success, their applicability is limited due to their inability to incorporate expert feedback and guidance systematically into the forecasting framework. We propose a new approach leveraging the Seldonian optimization framework from AI safety and demonstrate how it can be adapted to epidemic forecasting. We study two types of guidance: smoothness and regional consistency of errors, where we show that by its successful incorporation, we are able to not only bound the probability of undesirable behavior to happen, but also to reduce RMSE on test data by up to 17%.
Revisiting Linformer with a modified self-attention with linear complexity
Venue: arXiv cs.LG 2020
Year: 2020

Although Transformer models such as Google's BERT and OpenAI's GPT-3 are successful in many natural language processing tasks, training and deploying these models are costly and inefficient.Even if pre-trained models are used, deploying these models still remained a challenge due to their large size. Apart from deployment, these models take higher time during inference restricting user-friendliness. The main bottleneck is self-attention which uses quadratic time and space with respect to the sequence length. In order to reduce the quadratic time complexity of the self-attention mechanism, Linformer by Facebook's AI research team was introduced where they showed that the self-attention mechanism can be approximated by a low-rank matrix and exploiting this finding, a new method for self-attention with linear time and space complexity was proposed by them. In the Linformer, the time complexity depends on the projection mapping dimension which acts as a hyperparameter and affects the performance of the model, tuning this hyperparameter can be time-consuming. In this paper, I proposed an alternative method for self-attention with linear complexity in time and space and is independent of the projection mapping dimension. Since this method works for long sequences this can be used for images as well as audios.
Evolution of Small Cell from 4G to 6G: Past, Present, and Future
Venue: arXiv cs.NI 2020
Year: 2020

To boost the capacity of the cellular system, the operators have started to reuse the same licensed spectrum by deploying 4G LTE small cells (Femto Cells) in the past. But in time, these small cell licensed spectrum is not sufficient to satisfy future applications like augmented reality (AR)and virtual reality (VR). Hence, cellular operators look for alternate unlicensed spectrum in Wi-Fi 5 GHz band, later 3GPP named as LTE Licensed Assisted Access (LAA). The recent and current rollout of LAA deployments (in developed nations like the US) provides an opportunity to understand coexistence profound ground truth. This paper discusses a high-level overview of my past, present, and future research works in the direction of small cell benefits. In the future, we shift the focus onto the latest unlicensed band: 6 GHz, where the latest Wi-Fi version, 802.11ax, will coexist with the latest cellular technology, 5G New Radio(NR) in unlicensed
Evolutionary Algorithms for Fuzzy Cognitive Maps
Venue: arXiv cs.NE 2020
Year: 2020

Fuzzy Cognitive Maps (FCMs) is a complex systems modeling technique which, due to its unique advantages, has lately risen in popularity. They are based on graphs that represent the causal relationships among the parameters of the system to be modeled, and they stand out for their interpretability and flexibility. With the late popularity of FCMs, a plethora of research efforts have taken place to develop and optimize the model. One of the most important elements of FCMs is the learning algorithm they use, and their effectiveness is largely determined by it. The learning algorithms learn the node weights of an FCM, with the goal of converging towards the desired behavior. The present study reviews the genetic algorithms used for training FCMs, as well as gives a general overview of the FCM learning algorithms, putting evolutionary computing into the wider context.
Constructive Decision Theory
Venue: arXiv cs.GT 2021
Year: 2021

In most contemporary approaches to decision making, a decision problem is described by a sets of states and set of outcomes, and a rich set of acts, which are functions from states to outcomes over which the decision maker (DM) has preferences. Most interesting decision problems, however, do not come with a state space and an outcome space. Indeed, in complex problems it is often far from clear what the state and outcome spaces would be. We present an alternative foundation for decision making, in which the primitive objects of choice are syntactic programs. A representation theorem is proved in the spirit of standard representation theorems, showing that if the DM's preference relation on objects of choice satisfies appropriate axioms, then there exist a set S of states, a set O of outcomes, a way of interpreting the objects of choice as functions from S to O, a probability on S, and a utility function on O, such that the DM prefers choice a to choice b if and only if the expected utility of a is higher than that of b. Thus, the state space and outcome space are subjective, just like the probability and utility; they are not part of the description of the problem. In principle, a modeler can test for SEU behavior without having access to states or outcomes. We illustrate the power of our approach by showing that it can capture decision makers who are subject to framing effects.
Better bounds for matchings in the streaming model
Venue: arXiv cs.DS 2021
Year: 2021

In this paper we present improved bounds for approximating maximum matchings in bipartite graphs in the streaming model. First, we consider the question of how well maximum matching can be approximated in a single pass over the input using $\tilde O(n)$ space, where $n$ is the number of vertices in the input graph. Two natural variants of this problem have been considered in the literature: (1) the edge arrival setting, where edges arrive in the stream and (2) the vertex arrival setting, where vertices on one side of the graph arrive in the stream together with all their incident edges. The latter setting has also been studied extensively in the context of online algorithms, where each arriving vertex has to either be matched irrevocably or discarded upon arrival. In the online setting, the celebrated algorithm of Karp-Vazirani-Vazirani achieves a $1-1/e$ approximation. Despite the fact that the streaming model is less restrictive in that the algorithm is not constrained to match vertices irrevocably upon arrival, the best known approximation in the streaming model with vertex arrivals and $\tilde O(n)$ space is the same factor of $1-1/e$. We show that no single pass streaming algorithm that uses $\tilde O(n)$ space can achieve a better than $1-1/e$ approximation to maximum matching, even in the vertex arrival setting. This leads to the striking conclusion that no single pass streaming algorithm can do better than online algorithms unless it uses significantly more than $\tilde O(n)$ space. Additionally, our bound yields the best known impossibility result for approximating matchings in the edge arrival model. We also give a simple algorithm that achieves approximation ratio $1-e^{-k}k^{k-1}/(k-1)!=1-\frac1{\sqrt{2πk}}+o(1/k)$ in $k$ passes in the vertex arrival model using linear space, improving upon previously best known convergence.
The hyperbolic Voronoi diagram in arbitrary dimension
Venue: arXiv cs.CG 2021
Year: 2021

We show that in the Klein projective ball model of hyperbolic space, the hyperbolic Voronoi diagram is affine and amounts to clip a corresponding power diagram, requiring however algebraic arithmetic. By considering the lesser-known Beltrami hemisphere model of hyperbolic geometry, we overcome the arithmetic limitations of Klein construction. Finally, we characterize the bisectors and geodesics in the other Poincar\' e upper half-space, the Poincaré ball, and the Lorentz hyperboloid models, and discusses on degenerate cases for which the dual hyperbolic Delaunay complex is not a triangulation.
Functions with Diffusive Properties
Venue: arXiv cs.IT 2021
Year: 2021

While exploring desirable properties of hash functions in cryptography, the author was led to investigate three notions of functions with scattering or "diffusive" properties, where the functions map between binary strings of fixed finite length. These notions of diffusion ask for some property to be fulfilled by the Hamming distances between outputs corresponding to pairs of inputs that lie on the endpoints of edges of an $n$-dimensional hypercube. Given the dimension of the input space, we explicitly construct such functions for every dimension of the output space that allows for the functions to exist.
Static Level Ancestors in Practice
Venue: arXiv cs.DS 2021
Year: 2021

Given a rooted tree T, the level ancestor problem aims to answer queries of the form LA(v, d), which identify the level d ancestor of a node v in the tree. Several algorithms of varied complexity have been proposed for this problem in the literature, including optimal solutions that preprocess the tree $T$ in linear bounded time and proceed to answer queries in constant time. Despite its significance and numerous applications, to date there have been no comparative studies of the performance of these algorithms and few implementations are widely available. In our experimental study we implemented and compared several solutions to the level ancestor problem, including three theoretically optimal algorithms, and examined their space requirements and time performance in practice.
A Characterization of hard-to-cover CSPs
Venue: arXiv cs.CC 2021
Year: 2021

We continue the study of the covering complexity of constraint satisfaction problems (CSPs) initiated by Guruswami, Håstad and Sudan [SIAM J. Comp. 2002] and Dinur and Kol [CCC'13]. The covering number of a CSP instance $Φ$ is the smallest number of assignments to the variables of $Φ$, such that each constraint of $Φ$ is satisfied by at least one of the assignments. We show the following results: 1. Assuming a covering variant of the Unique Games Conjecture, introduced by Dinur and Kol, we show that for every non-odd predicate $P$ over any constant-size alphabet and every integer $K$, it is NP-hard to approximate the covering number within a factor of $K$. This yields a complete characterization of CSPs over constant-size alphabets that are hard to cover. 2. For a large class of predicates that are contained in the 2k-LIN predicate, we show that it is quasi-NP-hard to distinguish between instances with covering number at most $2$ and those with covering number at least $Ω(\log\log n)$. This generalizes and improves the 4-LIN covering hardness result of Dinur and Kol.
Knowledge, Justification, and Adequate Reasons
Venue: arXiv cs.LO 2021
Year: 2021

Is knowledge definable as justified true belief ("JTB")? We argue that one can legitimately answer positively or negatively, depending on whether or not one's true belief is justified by what we call adequate reasons. To facilitate our argument we introduce a simple propositional logic of reason-based belief, and give an axiomatic characterization of the notion of adequacy for reasons. We show that this logic is sufficiently flexible to accommodate various useful features, including quantification over reasons. We use our framework to contrast two notions of JTB: one internalist, the other externalist. We argue that Gettier cases essentially challenge the internalist notion but not the externalist one. Our approach commits us to a form of infallibilism about knowledge, but it also leaves us with a puzzle, namely whether knowledge involves the possession of only adequate reasons, or leaves room for some inadequate reasons. We favor the latter position, which reflects a milder and more realistic version of infallibilism.
Reversible Quantum Process Algebra
Venue: arXiv cs.LO 2021
Year: 2021

Truly concurrent process algebras are generalizations to the traditional process algebras for true concurrency, CTC to CCS, APTC to ACP, $π_{tc}$ to $π$ calculus , APPTC to probabilistic process algebra. And we also did some work on reversible process algebra and probabilistic truly concurrent process algebra. In this book, we utilize reversible truly concurrent process algebras APRTC and APPTC to model quantum computing and unify quantum and classical computing.
Polar Lattices for Lossy Compression
Venue: arXiv cs.IT 2021
Year: 2021

Polar lattices, which are constructed from polar codes, have recently been proved to be able to achieve the capacity of the additive white Gaussian noise (AWGN) channel. In this work, we propose a new construction of polar lattices to solve the dual problem, i.e., achieving the rate-distortion bound of a memoryless Gaussian source, which means that polar lattices can also be good for the lossy compression of continuous sources. The structure of the proposed polar lattices enables us to integrate the post-entropy coding process into the lattice quantizer, which simplifies the quantization process. The overall complexity of encoding and decoding complexity is $O(N \log^2 N)$ for a sub-exponentially decaying excess distortion. Moreover, the nesting structure of polar lattices further provides solutions for some multi-terminal coding problems. The Wyner-Ziv coding problem for a Gaussian source can be solved by an AWGN capacity-achieving polar lattice nested in a rate-distortion bound achieving one, and the Gelfand-Pinsker problem can be solved in a reversed manner.
A Next-Generation Data Language Proposal
Venue: arXiv cs.DB 2021
Year: 2021

This paper analyzes the fatal drawback of the relational calculus not allowing relations to be domains of relations, and its consequences entrenched in SQL. In order to overcome this obstacle we propose "multitable index" - an easily implementable upgrade to an existing data storage, which enables a revolutionary change in the field of data languages - the demotion of relational calculus and tables. We propose a new data language with "pragmatic typisation" where types represent domain knowledge but not memory management. The language handles sets of tuples as first class data objects and supports set operation and tuple (de)composition operations as fluently as basic arith. And it is equally suitable for building-into a general purpose language as well as querying a remote DB (thus removing the ubiquitous gap between SQL and application).