OpenRaiser/Intern-Atlas huggingface dataset:
On The Robustness of Price-Anticipating Kelly Mechanism
Venue: arXiv cs.SI 2021
Year: 2021

The price-anticipating Kelly mechanism (PAKM) is one of the most extensively used strategies to allocate divisible resources for strategic users in communication networks and computing systems. The users are deemed as selfish and also benign, each of which maximizes his individual utility of the allocated resources minus his payment to the network operator. However, in many applications a user can use his payment to reduce the utilities of his opponents, thus playing a misbehaving role. It remains mysterious to what extent the misbehaving user can damage or influence the performance of benign users and the network operator. In this work, we formulate a non-cooperative game consisting of a finite amount of benign users and one misbehaving user. The maliciousness of this misbehaving user is captured by his willingness to pay to trade for unit degradation in the utilities of benign users. The network operator allocates resources to all the users via the price-anticipating Kelly mechanism. We present six important performance metrics with regard to the total utility and the total net utility of benign users, and the revenue of network operator under three different scenarios: with and without the misbehaving user, and the maximum. We quantify the robustness of PAKM against the misbehaving actions by deriving the upper and lower bounds of these metrics. With new approaches, all the theoretical bounds are applicable to an arbitrary population of benign users. Our study reveals two important insights: i) the performance bounds are very sensitive to the misbehaving user's willingness to pay at certain ranges; ii) the network operator acquires more revenues in the presence of the misbehaving user which might disincentivize his countermeasures against the misbehaving actions.
Composition, Cooperation, and Coordination of Computational Systems
Venue: arXiv cs.SE 2021
Year: 2021

In this paper I elaborated on the idea of David Harel and Amir Pnueli to think systems and their interaction from the point of view of their compositional behaviour. The obvious idea to base the composition of systems on the concept of computable functions and their compositional behaviour leads to supersystem formation by composing simple and recursive systems. But this approach does not allow to account adequately for systems that interact with many other systems in a stateful and nondeterministic way, which is why I introduce the concept of interactivity and cooperation. In order to describe interactive systems satisfactorily, a balance is needed between the representation of their relationship to all the other systems and what happens within the systems. I thus introduce the complementary descriptions of external interactions and internal coordination, both based on a role concept in the sense of a projection of a system onto its interactions. It actually mirrors the internal vs. external distinction initially introduced by the system model and reflects the problem how systems are supposed to cooperate without melting into a common supersystem. Components become systems, intended for (a given) composition and with the notion of an interface we subsume all relevant information necessary for this composition. I introduce the concept of decisions as an additional internal input alphabet in order to determine nondeterministic interactions and thus fictitiously assume a system function where we actually do not have the knowledge to do so. Thus, the close relationship between protocols and games becomes obvious. Finally, I transfer the gained insights to the field of IT system architecture and introduce the concept of the "interaction oriented architecture (IOA)" for interactive systems with its three elements of roles, coordination rules, and decisions.
Bayesian Exploration: Incentivizing Exploration in Bayesian Games
Venue: arXiv cs.GT 2021
Year: 2021

We consider a ubiquitous scenario in the Internet economy when individual decision-makers (henceforth, agents) both produce and consume information as they make strategic choices in an uncertain environment. This creates a three-way tradeoff between exploration (trying out insufficiently explored alternatives to help others in the future), exploitation (making optimal decisions given the information discovered by other agents), and incentives of the agents (who are myopically interested in exploitation, while preferring the others to explore). We posit a principal who controls the flow of information from agents that came before, and strives to coordinate the agents towards a socially optimal balance between exploration and exploitation, not using any monetary transfers. The goal is to design a recommendation policy for the principal which respects agents' incentives and minimizes a suitable notion of regret. We extend prior work in this direction to allow the agents to interact with one another in a shared environment: at each time step, multiple agents arrive to play a Bayesian game, receive recommendations, choose their actions, receive their payoffs, and then leave the game forever. The agents now face two sources of uncertainty: the actions of the other agents and the parameters of the uncertain game environment. Our main contribution is to show that the principal can achieve constant regret when the utilities are deterministic (where the constant depends on the prior distribution, but not on the time horizon), and logarithmic regret when the utilities are stochastic. As a key technical tool, we introduce the concept of explorable actions, the actions which some incentive-compatible policy can recommend with non-zero probability. We show how the principal can identify (and explore) all explorable actions, and use the revealed information to perform optimally.
Nominal Automata with Name Binding
Venue: arXiv cs.FL 2021
Year: 2021

Automata models for data languages (i.e. languages over infinite alphabets) often feature either global or local freshness operators. We show that Bollig et al.'s session automata, which focus on global freshness, are equivalent to regular nondeterministic nominal automata (RNNA), a natural nominal automaton model with explicit name binding that has appeared implicitly in the semantics of nominal Kleene algebra (NKA), an extension of Kleene algebra with name binding. The expected Kleene theorem for NKA is known to fail in one direction, i.e. there are nominal languages that can be accepted by an RNNA but are not definable in NKA; via session automata, we obtain a full Kleene theorem for RNNAs for an expression language that extends NKA with unscoped name binding. Based on the equivalence with RNNAs, we then slightly rephrase the known equivalence checking algorithm for session automata. Reinterpreting the data language semantics of name binding by unrestricted instead of clean alpha-equivalence, we obtain a local freshness semantics as a quotient of the global freshness semantics. Under local freshness semantics, RNNAs turn out to be equivalent to a natural subclass of Bojanczyk et al.'s nondeterministic orbit-finite automata. We establish decidability of inclusion under local freshness by modifying the RNNA-based algorithm; in summary, we obtain a formalism for local freshness in data languages that is reasonably expressive and has a decidable inclusion problem.
The Menu-Size Complexity of Revenue Approximation
Venue: arXiv cs.GT 2021
Year: 2021

Consider a monopolist selling $n$ items to an additive buyer whose item values are drawn from independent distributions $F_1,F_2,\ldots,F_n$ possibly having unbounded support. Unlike in the single-item case, it is well known that the revenue-optimal selling mechanism (a pricing scheme) may be complex, sometimes requiring a continuum of menu entries. Also known is that simple mechanisms with a bounded number of menu entries can extract a constant fraction of the optimal revenue. Nonetheless, whether an arbitrarily high fraction of the optimal revenue can be extracted via a bounded menu size remained open. We give an affirmative answer: for every $n$ and $\varepsilon>0$, there exists $C=C(n,\varepsilon)$ s.t. mechanisms of menu size at most $C$ suffice for obtaining $(1-\varepsilon)$ of the optimal revenue from any $F_1,\ldots,F_n$. We prove upper and lower bounds on the revenue-approximation complexity $C(n,\varepsilon)$ and on the deterministic communication complexity required to run a mechanism achieving such an approximation.
Channel Estimation and Data Equalization in Frequency-Selective MIMO Systems with One-Bit Quantization
Venue: arXiv cs.IT 2021
Year: 2021

This paper addresses channel estimation and data equalization on frequency-selective 1-bit quantized Multiple Input-Multiple Output (MIMO) systems. No joint processing or Channel State Information is assumed at the transmitter, and therefore our findings are also applicable to the uplink of Multi-User MIMO systems. System models for both Orthogonal Division Frequency Multiplexing (OFDM) and single-carrier schemes are developed. A Cramér-Rao Lower Bound for the estimation problems is derived. The two nonlinear algorithms Expectation Maximization (EM) and Generalized Approximate Message Passing (GAMP) are adapted to the problems, and a linear method based on the Bussgang theorem is proposed. In the OFDM case, the linear method enables subcarrier-wise estimation, greatly reducing computational complexity. Simulations are carried out to compare the algorithms with different settings. The results turn out to be close to the Cramér-Rao bound in the low Signal to Noise Ratio (SNR) region. The OFDM setting is more suitable for the nonlinear algorithms, and that the linear methods incur a performance loss with respect to the nonlinear approaches. In the relevant low and medium SNR regions, the loss amounts to 2-3 dB and might well be justified in exchange for the reduced computational effort, especially in Massive MIMO settings.
Integrating Topic Models and Latent Factors for Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Nowadays, we have large amounts of online items in various web-based applications, which makes it an important task to build effective personalized recommender systems so as to save users' efforts in information seeking. One of the most extensively and successfully used methods for personalized recommendation is the Collaborative Filtering (CF) technique, which makes recommendation based on users' historical choices as well as those of the others'. The most popular CF method, like Latent Factor Model (LFM), is to model how users evaluate items by understanding the hidden dimension or factors of their opinions. How to model these hidden factors is key to improve the performance of recommender system. In this work, we consider the problem of hotel recommendation for travel planning services by integrating the location information and the user's preference for recommendation. The intuition is that user preferences may change dynamically over different locations, thus treating the historical decisions of a user as static or universally applicable can be infeasible in real-world applications. For example, users may prefer chain brand hotels with standard configurations when traveling for business, while they may prefer unique local hotels when traveling for entertainment. In this paper, we aim to provide trip-level personalization for users in recommendation.
Active Learning for Cost-Sensitive Classification
Venue: arXiv cs.LG 2021
Year: 2021

We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label's cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data to estimate possible costs for each label. It queries only the labels that could be the best, ignoring the sure losers. We prove COAL can be efficiently implemented for any regression family that admits squared loss optimization; it also enjoys strong guarantees with respect to predictive performance and labeling effort. We empirically compare COAL to passive learning and several active learning baselines, showing significant improvements in labeling effort and test cost on real-world datasets.
Minimum Perimeter-Sum Partitions in the Plane
Venue: arXiv cs.CG 2021
Year: 2021

Let $P$ be a set of $n$ points in the plane. We consider the problem of partitioning $P$ into two subsets $P_1$ and $P_2$ such that the sum of the perimeters of $\text{CH}(P_1)$ and $\text{CH}(P_2)$ is minimized, where $\text{CH}(P_i)$ denotes the convex hull of $P_i$. The problem was first studied by Mitchell and Wynters in 1991 who gave an $O(n^2)$ time algorithm. Despite considerable progress on related problems, no subquadratic time algorithm for this problem was found so far. We present an exact algorithm solving the problem in $O(n \log^2 n)$ time and a $(1+\varepsilon)$-approximation algorithm running in $O(n + 1/\varepsilon^2\cdot\log^2(1/\varepsilon))$ time.
An Ontological Architecture for Orbital Debris Data
Venue: arXiv cs.AI 2021
Year: 2021

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations.
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
Venue: arXiv cs.CV 2021
Year: 2021

Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This book attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.
Learning 3D Object Categories by Looking Around Them
Venue: arXiv cs.CV 2021
Year: 2021

Traditional approaches for learning 3D object categories use either synthetic data or manual supervision. In this paper, we propose a method which does not require manual annotations and is instead cued by observing objects from a moving vantage point. Our system builds on two innovations: a Siamese viewpoint factorization network that robustly aligns different videos together without explicitly comparing 3D shapes; and a 3D shape completion network that can extract the full shape of an object from partial observations. We also demonstrate the benefits of configuring networks to perform probabilistic predictions as well as of geometry-aware data augmentation schemes. We obtain state-of-the-art results on publicly-available benchmarks.
EDOS: Edge Assisted Offloading System for Mobile Devices
Venue: arXiv cs.NI 2021
Year: 2021

Offloading resource-intensive jobs to the cloud and nearby users is a promising approach to enhance mobile devices. This paper investigates a hybrid offloading system that takes both infrastructure-based networks and Ad-hoc networks into the scope. Specifically, we propose EDOS, an edge assisted offloading system that consists of two major components, an Edge Assistant (EA) and Offload Agent (OA). EA runs on the routers/towers to manage registered remote cloud servers and local service providers and OA operates on the users' devices to discover the services in proximity. We present the system with a suite of protocols to collect the potential service providers and algorithms to allocate tasks according to user-specified constraints. To evaluate EDOS, we prototype it on commercial mobile devices and evaluate it with both experiments on a small-scale testbed and simulations. The results show that EDOS is effective and efficient for offloading jobs.
Neural Attribute Machines for Program Generation
Venue: arXiv cs.AI 2021
Year: 2021

Recurrent neural networks have achieved remarkable success at generating sequences with complex structures, thanks to advances that include richer embeddings of input and cures for vanishing gradients. Trained only on sequences from a known grammar, though, they can still struggle to learn rules and constraints of the grammar. Neural Attribute Machines (NAMs) are equipped with a logical machine that represents the underlying grammar, which is used to teach the constraints to the neural machine by (i) augmenting the input sequence, and (ii) optimizing a custom loss function. Unlike traditional RNNs, NAMs are exposed to the grammar, as well as samples from the language of the grammar. During generation, NAMs make significantly fewer violations of the constraints of the underlying grammar than RNNs trained only on samples from the language of the grammar.
Rational coordination with no communication or conventions
Venue: arXiv cs.MA 2021
Year: 2021

We study pure coordination games where in every outcome, all players have identical payoffs, 'win' or 'lose'. We identify and discuss a range of 'purely rational principles' guiding the reasoning of rational players in such games and analyze which classes of coordination games can be solved by such players with no preplay communication or conventions. We observe that it is highly nontrivial to delineate a boundary between purely rational principles and other decision methods, such as conventions, for solving such coordination games.
Optimal General Matchings
Venue: arXiv cs.DS 2021
Year: 2021

Given a graph $G=(V,E)$ and for each vertex $v \in V$ a subset $B(v)$ of the set $\{0,1,\ldots, d_G(v)\}$ a $B$-matching of $G$ is any set $F \subseteq E$ such that $d_F(v) \in B(v)$ for each vertex $v$. The general matching problem asks the existence of a $B$-matching in a given graph. A set $B(v)$ is said to have a {\em gap of length} $p$ if there exists a number $k \in B(v)$ such that $k+1, \ldots, k+p \notin B(v)$ and $k+p+1 \in B(v)$. Without any restrictions the general matching problem is NP-complete. However, if no set $B(v)$ contains a gap of length greater than $1$, then the problem can be solved in polynomial time and Cornuejols \cite{Cor} presented an algorithm for finding a $B$-matching, if it exists. In this paper we consider a version of the general matching problem, in which we are interested in finding a $B$-matching having a maximum (or minimum) number of edges. We present the first polynomial time algorithm for the maximum weight $B$-matching for the case when no set $B(v)$ contains a gap of length greater than $1$.
Wideband Subspace Estimation Through Projection Matrix Approximation
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we present a wideband subspace estimation method that characterizes the signal subspace through its orthogonal projection matrix at each frequency. Fundamentally, the method models this projection matrix as a function of frequency that can be approximated by a polynomial. It provides two improvements: a reduction in the number of parameters required to represent the signal subspace along a given frequency band and a quality improvement in wideband direction-of-arrival (DOA) estimators such as Incoherent Multiple Signal Classification (IC-MUSIC) and Modified Test of Orthogonality of Projected Subspaces (MTOPS). In rough terms, the method fits a polynomial to a set of projection matrix estimates, obtained at a set of frequencies, and then uses the polynomial as a representation of the signal subspace. The paper includes the derivation of asymptotic bounds for the bias and root-mean-square (RMS) error of the projection matrix estimate and a numerical assessment of the method and its combination with the previous two DOA estimators.
Asynchronous Massive Access and Neighbor Discovery Using OFDMA
Venue: arXiv cs.IT 2021
Year: 2021

The fundamental communication problem in the wireless Internet of Things (IoT) is to discover a massive number of devices and to allow them reliable access to shared channels. Oftentimes these devices transmit short messages randomly and sporadically. This paper proposes a novel signaling scheme for grant-free massive access, where each device encodes its identity and/or information in a sparse set of tones. Such transmissions are implemented in the form of orthogonal frequency-division multiple access (OFDMA). Under some mild conditions and assuming device delays to be bounded unknown multiples of symbol intervals, sparse OFDMA is proved to enable arbitrarily reliable asynchronous device identification and message decoding with a codelength that is O(K(log K + log S + log N)), where N denotes the device population, K denotes the actual number of active devices, and log S is essentially equal to the number of bits a device can send (including its identity). By exploiting the Fast Fourier Transform (FFT), the computational complexity for discovery and decoding can be made to be sub-linear in the total device population. To prove the concept, a specific design is proposed to identify up to 100 active devices out of $2^{38}$ possible devices with up to 20 symbols of delay and moderate signal-to-noise ratios and fading. The codelength compares much more favorably with those of standard slotted ALOHA and carrier-sensing multiple access (CSMA) schemes.
Online unit clustering in higher dimensions
Venue: arXiv cs.CG 2021
Year: 2021

We revisit the online Unit Clustering and Unit Covering problems in higher dimensions: Given a set of $n$ points in a metric space, that arrive one by one, Unit Clustering asks to partition the points into the minimum number of clusters (subsets) of diameter at most one; while Unit Covering asks to cover all points by the minimum number of balls of unit radius. In this paper, we work in $\mathbb{R}^d$ using the $L_\infty$ norm. We show that the competitive ratio of any online algorithm (deterministic or randomized) for Unit Clustering must depend on the dimension $d$. We also give a randomized online algorithm with competitive ratio $O(d^2)$ for Unit Clustering of integer points (i.e., points in $\mathbb{Z}^d$, $d\in \mathbb{N}$, under $L_{\infty}$ norm). We show that the competitive ratio of any deterministic online algorithm for Unit Covering is at least $2^d$. This ratio is the best possible, as it can be attained by a simple deterministic algorithm that assigns points to a predefined set of unit cubes. We complement these results with some additional lower bounds for related problems in higher dimensions.