Miracle-dz/UniFAR-FTUs huggingface dataset:
A Solver for a Theory of Strings and Bit-Vectors

We present the Z3strBV solver for a many-sorted first-order quantifier-free theory Tw, bv of string equations, string length represented as bit-vectors, and bit-vector arithmetic aimed at formal verification, automated testing, and security analysis of C/C++ applications. Our key motivation for building such a solver is the observation that existing string solvers are not efficient at modeling the combined theory over strings and bit-vectors. We demonstrate experimentally that Z3strBV is significantly more efficient than a reduction of string/bit-vector constraints to strings/natural numbers followed by a solver for strings/natural numbers or modeling strings as bit-vectors. We also propose two optimizations. First, we explore the concept of library-aware SMT solving, which fixes summaries in the SMT solver for string library functions such as strlen in C/C++. Z3strBV is able to consume these functions directly instead of re-analyzing the functions from scratch each time. Second, we experiment with a binary search heuristic that accelerates convergence on a consistent assignment of string lengths. We also show that Z3strBV is able to detect nontrivial overflows in real-world system-level code, as confirmed against seven security vulnerabilities from the CVE and Mozilla databases.
Parallel optimization for large eddy simulations

We developed a parallel Bayesian optimization algorithm for large eddy simulations. These simulations challenge optimization methods because they take hours or days to compute, and their objective function contains noise as turbulent statistics that are averaged over a finite time. Surrogate based optimization methods, including Bayesian optimization, have shown promise for noisy and expensive objective functions. Here we adapt Bayesian optimization to minimize drag in a turbulent channel flow and to design the trailing edge of a turbine blade to reduce turbulent heat transfer and pressure loss. Our optimization simultaneously runs several simulations, each parallelized to thousands of cores, in order to utilize additional concurrency offered by today's supercomputers.
Estimation of Playable Piano Fingering by Pitch-difference Fingering Matching Model

The existing piano fingering labeling statistical models usually consider the constraints among the fingers and the correlation between fingering and notes, and rarely include the relationship among the notes directly. The limited learned finger-transfer rules often cause that some parts of the fingering cannot be playable in fact. And traditional models often adopt the original notes, which cannot help to explore the mapping nature between the pitches and fingering. Inspired from manual-ly annotation which acquire the fingering knowledge directly from pitch-difference, we proposed a pitch-difference sequence and fingering (PdF) matching model. And to get playable fingering, be-sides learned finger-transfer rules, prior finger-transfer knowledge is especially combined into the model. In order to characterize the playable performance of the model, we also presented a new evaluation index named incapable-performing fingering rate (IFR). Comprehensive experimental re-sults show that compared with the existing state-of-the-art third-order hidden Markov labeling model, the general and the highest matching rate of our model increases by 3% and 1.6% respective-ly, and the fingering for all scores can be playable.
MultiAmdahl : How Should I Divide My Heterogeneous Chip ?

Emerging heterogeneous multiprocessor chips will integrate a large number of different computational units: e.g., large cores for sequential processing, several smaller cores for parallel processing, Graphics Processing Units (GPUs), media accelerators, helper processors, Digital Signal Processors (DSPs), embedded FPGAs, and application-specific hardware circuits. These units are designed specifically for a particular workload, allowing them to be heavily customized, and therefore much more efficient than general-purpose units.
Finding Approximately Rank-One Submatrices with the Nuclear Norm and 퓁1-Norm

We propose a convex optimization formulation with the nuclear norm and $\ell_1$-norm to find a large approximately rank-one submatrix of a given nonnegative matrix. We develop optimality conditions for the formulation and characterize the properties of the optimal solutions. We establish conditions under which the optimal solution of the convex formulation has a specific sparse structure. Finally, we show that, under certain hypotheses, with high probability, the approach can recover the rank-one submatrix even when it is corrupted with random noise and inserted as a submatrix into a much larger random noise matrix.
2 1 Ju l 2 00 6 Degrees of Freedom on the MIMO X Channel-the Optimality of the MMK Scheme

We characterize the degrees of freedom ηX in a system with two multiple antenna transmitters and two multiple antenna receivers. With M antennas at all nodes we find ⌊ 43M⌋ ≤ ηX ≤ 4 3M . The MMK scheme proposed in [1] is seen to achieve the maximum multipl exing gain rounded down to the nearest integer and is exactly optimal when M is a multiple of3. Compared to zero forcing that allows only M degrees of freedom, we show that the X channel allows a higher multiplexing gain by a factor of at most 43 . While spatial zero forcing is not optimal on the X channel, the combination of zero forcing, dirty paper coding and successive decoding proposed in the M MK scheme is optimal for the maximum multiplexing gain rounded down to the nearest integer value .
Robust Distributed Routing in Dynamical Networks–Part II: Strong Resilience, Equilibrium Selection and Cascaded Failures

Strong resilience properties of dynamical networks are analyzed for distributed routing policies. The latter are characterized by the property that the way the outflow at a non-destination node gets split among its outgoing links is allowed to depend only on local information about the current particle densities on the outgoing links. The strong resilience of the network is defined as the infimum sum of link-wise flow capacity reductions making the asymptotic total inflow to the destination node strictly less than the total outflow at the origin. A class of distributed routing policies that are responsive to local information is shown to yield the maximum possible strong resilience under such local information constraints for an acyclic dynamical network with a single origin-destination pair. The maximal achievable strong resilience is shown to be equal to the minimum node residual capacity of the network. The latter depends on the limit flow of the unperturbed network and is defined as the minimum, among all the non-destination nodes, of the sum, over all the links outgoing from the node, of the differences between the maximum flow capacity and the limit flow of the unperturbed network. We propose a simple convex optimization problem to solve for equilibrium flows of the unperturbed network that minimize average delay subject to strong resilience guarantees, and discuss the use of tolls to induce such an equilibrium flow in traffic networks. Finally, we present illustrative simulations to discuss the connection between cascaded failures and the resilience properties of the network.
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

This paper presents for the first time a heterogeneous vehicular model with multiple moving small cells and a moving receiver with unreliable backhaul. In this system, a macro-base station connects to multiple moving small cells via wireless backhaul links. A Bernoulli process is adopted to model the backhaul reliability. A selection combining protocol is used at the receiver side to maximize the received signal-tonoise ratio. We investigate the impact of the number of moving small cells, the position of the receiver and the backhaul reliability on the system performance over doubleRayleigh fading channels. Expressions for outage probability are derived. Received on 04 December 2018; accepted on 12 December 2018; published on 19 December 2018
Key establishment \`a la Merkle in a quantum world

In 1974, Ralph Merkle proposed the first unclassified scheme for secure communications over insecure channels. When legitimate communicating parties are willing to spend an amount of computational effort proportional to some parameter N, an eavesdropper cannot break into their communication without spending a time proportional to N^2, which is quadratically more than the legitimate effort. Two of us showed in 2008 that Merkle's schemes are completely insecure against a quantum adversary, but that their security can be partially restored if the legitimate parties are also allowed to use quantum computation: the eavesdropper needed to spend a time proportional to N^{3/2} to break our earlier quantum scheme. Furthermore, all previous classical schemes could be broken completely by the onslaught of a quantum eavesdropper and we conjectured that this is unavoidable. We give now two novel key establishment schemes in the spirit of Merkle's. The first one can be broken by a quantum adversary who makes an effort proportional to N^{5/3}, which is the optimal attack against this scheme. Our second scheme is purely classical, yet it cannot be broken by a quantum eavesdropper who is only willing to expend an effort proportional to that of the legitimate parties. We then introduce two families of more elaborate protocols. The first family consists in quantum protocols whose security is arbitrarily close to quadratic in the query complexity model. The second is a family of classical protocols whose security against a quantum adversary is arbitrarily close to N^{3/2} in the same model.
Intelligent Data Analysis: An Introduction

From the Publisher: This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The ten coherently written chapters by leading experts provide complete coverage of the core issues. "The book will become a valuable source of reference for professionals concerned with modern data analysis. Students as well as IT professionals interested in learning about intelligent data analysis will appreciate the book as a useful text enhanced by numerous illustrations and examples.
FPGA Interconnect Topologies Exploration

This paper presents an improved interconnect network for Tree-based FPGA architecture that unifies two unidirectional programmable networks. New tools are developed to place and route the largest benchmark circuits, where different optimization techniques are used to get an optimized architecture. The effect of variation in LUT and cluster size on the area, performance, and power of the Tree-based architecture is analyzed. Experimental results show that an architecture with LUT size 4 and arity size 4 is the most efficient in terms of area and static power dissipation, whereas the architectures with higher LUT and cluster size are efficient in terms of performance. We also show that unifying a Mesh with this Tree topology leads to an architecture which has good layout scalability and better interconnect efficiency compared to VPR-style Mesh.
A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter

Experience Postdoctoral Researcher, University of Pennsylvania, Philadelphia, PA Feb 2014 – Aug 2016 Computer Information and Science Department (Advisor: Chris Callison-Burch) Visiting Researcher, University of Washington, Seattle, WA Jan 2012 – Dec 2013 Computer Science and Engineering Department Research Intern, Microsoft Research, Seattle, WA Jun – Sep 2011 Internet Services Research Center Consultant, Educational Testing Service, Princeton, NJ Mar – May, Sep – Dec 2010 Natural Language and Speech Group Intern, Amazon, Seattle, WA May – Sep 2010 Catalog Quality
Comparative Analysis of Frameworks for the Performance Evaluation of Multi-tier Cloud Applications 1

In this paper, we address the performance evaluation of multi-tier clouds applications, and compare a Real-Time Calculus-based framework with two classical analytical approaches such as queuing theoretic approaches and control theoretic approaches. We focus on the capabilities of these alternatives for estimating the key Quality of Service parameter - the application response-time.
The SP theory of intelligence: its distinctive features and advantages

This paper aims to highlight distinctive features of the "SP theory of intelligence" and its apparent advantages compared with some AI-related alternatives. The theory is outlined in an appendix. The distinctive features and advantages are summarised: simplification and integration of observations and concepts, and of structures and processes in computing systems; the SP theory is itself a theory of computing; a central role for information compression via the matching and unification of patterns, and for multiple alignment; transparency in the representation and processing of knowledge; the discovery of 'natural' structures via information compression (DONSVIC); interpretation of aspects of mathematics; interpretation of phenomena in human perception and cognition; realisation of abstract concepts in terms of neurons and their inter-connections ("SP-neural"). Distinctive features and advantages of the SP system compared with alternatives in: minimum length encoding and related concepts; deep learning in neural networks; universal search; Bayesian networks and other models for AI; analysis and production of natural language; learning natural language; exact and inexact reasoning; representation and processing of diverse forms of knowledge; IBM's Watson; problems associated with big data, and in the development of intelligence in autonomous robots. In conclusion, a major strength of SP system is that it can provide a firm foundation for the long-term development of AI and, at the same time, it may deliver some benefits and applications on relatively short timescales. It is envisaged that a high-parallel, open-source version of the SP machine will be created, hosted on an existing high-performance computer and derived from the existing SP computer model. This will be a means for researchers everywhere to explore what can be done with the system, and to create new versions of it.
Aren't we all nearest neighbors: Spatial trees, high dimensional reductions and batch nearest neighbor search

We start with a review of the pervasiveness of the nearest neighbor search problem and techniques used to solve it along with some experimental results. In the second chapter, we show reductions between two different classes of geo- metric proximity problems: the nearest neighbor problems to solve the Euclidean minimum spanning tree problem and the farthest neighbor problems to solve the k-centers problem. In the third chapter, we unify spatial partitioning trees un- der one framework the meta-tree. Finally, we propose a dual tree algorithm for Bichromatic Closest Pair and measure the complexity of batch nearest neighbor search.
Reinforcement Learning Approach for Real Time Strategy Games Like Battle City and S 3

In this paper we proposed reinforcement learning algorithms with the generalized reward function. In our proposed method we use Q-learning and SARSA algorithms with generalised reward function to train the reinforcement learning agent. We evaluated the performance of our proposed algorithms on two real-time strategy games called BattleCity and S3. There are two main advantages of having such an approach as compared to other works in RTS. (1) We can ignore the concept of a simulator which is often game specific and is usually hard coded in any type of RTS games (2) our system can learn from interaction with any opponents and quickly change the strategy according to the opponents and do not need any human traces as used in previous works.
First-Order Primal-Dual Method for Nonlinear Convex Cone Programs

Nonlinear convex cone programs (NCCP) are important problems with many practical applications, but these problems are hard to solve by using existing methods. This paper introduces a flexible first-order primaldual augmented Lagrangian decomposition algorithm called Varying Auxiliary Problem Principle (VAPP) for solving NCCP when the objective or constraints are nonseparable and possibly nonsmooth. This method can serve as a framework for a decomposition algorithm to solve composite NCCP. Each iteration of VAPP generates a nonlinear approximation to the primal problem of an augmented Lagrangian method. The approximation incorporates both linearization and a variable distance-like function or auxiliary core function. In this way, the primal problem can be decomposed into smaller subproblems, each of which has a closed-form solution or an easily approximated solution. Moreover, these subproblems can be solved in a parallel way. This paper proves convergence and an O(1/t) convergence rate on average for primal suboptimality, feasibility, and dual suboptimality. A backtracking scheme is discussed to treat the case where the Lipschitz constants are not known or computable.
Valuating surface surveillance technology for collaborative multiple-spot control of airport departure operations

Airport departure operations constitute an important source of airline delays and passenger frustration. Excessive surface traffic is the cause of increased controller and pilot workload; It is also the source of increased emissions; It worsens traffic safety and often does not yield improved runway throughput. Acknowledging this fact, this paper explores some of the feedback mechanisms by which airport traffic can be optimized in real time according to its current degree of congestion. In particular, it examines the environmnetal benefits that improved surveillance technologies can bring in the context of gate — or spot-release aircraft strategies. It is shown that improvements can lead yield 4% to 6% emission reductions for busy airports like New-York La Guardia or Seattle Tacoma. These benefits come on top of the benefits already obtained by adopting threshold strategies currently under evaluation.
A Local Active Contour Model for Image Segmentation with Intensity Inhomogeneity

A novel locally statistical active contour model (ACM) for image segmentation in the presence of intensity inhomogeneity is presented in this paper. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances, and a moving window is used to map the original image into another domain, where the intensity distributions of inhomogeneous objects are still Gaussian but are better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window. A statistical energy functional is then defined for each local region, which combines the bias field, the level set function, and the constant approximating the true signal of the corresponding object. Experiments on both synthetic and real images demonstrate the superiority of our proposed algorithm to state-of-the-art and representative methods.
ON THE WARING RANK OF BINARY FORMS: THE BINOMIAL FORMULA AND A DIHEDRAL COVER OF RANK TWO FORMS

Waring problem for forms is important and classical in mathematics. It has been widely investigated because of its wide applications in several areas. In this paper, we consider the Waring problem for binary forms with complex coefficients. Firstly, we give an explicit formula for the Waring rank of any binary binomial and several examples to illustrating it. Secondly, we prove that, up to scalar multiplication, there are exactly ( 2 ) binary forms of degree d with Waring rank two and multiple of three fixed distinct linear forms.
Probabilistic cellular automata and random fields with i . i . d . directions ∗

Let us consider the simplest model of one-dimensional probabilistic cellular automata (PCA). The cells are indexed by the integers, the alphabet is {0, 1}, and all the cells evolve synchronously. The new content of a cell is randomly chosen, independently of the others, according to a distribution depending only on the content of the cell itself and of its right neighbor. There are necessary and sufficient conditions on the four parameters of such a PCA to have a Bernoulli product invariant measure. We study the properties of the random field given by the space-time diagram obtained when iterating the PCA starting from its Bernoulli product invariant measure. It is a non-trivial random field with very weak dependences and nice combinatorial properties. In particular, not only the horizontal lines but also the lines in any other direction consist of i.i. d. random variables. We study extensions of the results to Markovian invariant measures, and to PCA with larger alphabets and neighborhoods.
Jointly Optimal Sensing and Resource Allocation for Multiuser Interweave Cognitive Radios

Successful deployment of cognitive radios requires efficient sensing of the spectrum and dynamic adaptation of the available resources according to the sensed (imperfect) information. While most works design these two tasks separately, in this paper we address them jointly. In particular, we investigate an interweave cognitive radio with multiple secondary users that access orthogonally a set of frequency bands originally devoted to primary users. The schemes are designed to minimize the cost of sensing, maximize the performance of the secondary users (weighted sum rate), and limit the probability of interfering with the primary users. The joint design is addressed using nonlinear optimization and dynamic programming, which is able to leverage the time correlation in the activity of the primary network. A two-step strategy is implemented: it first finds the optimal resource allocation for any sensing scheme and then uses that solution as input to solve for the optimal sensing policy. The two-step strategy is optimal, gives rise to intuitive optimal policies, and entails a computational complexity much lower than that required to solve the original formulation.
Coded Caching With Nonuniform Demands

We consider a network consisting of a file server connected through a shared link to a number of users, each equipped with a cache. Knowing the popularity distribution of the files, the goal is to optimally populate the caches, such as to minimize the expected load of the shared link. For a single cache, it is well known that storing the most popular files is optimal in this setting. However, we show here that this is no longer the case for multiple caches. Indeed, caching only the most popular files can be highly suboptimal. Instead, a fundamentally different approach is needed, in which the cache contents are used as side information for coded communication over the shared link. We propose such a coded caching scheme and prove that it is close to optimal.