On the Advice Complexity of Tournaments

As one component of a college-wide redesign of its introductory engineering courses, a project-based introduction to computer science is being developed using open-source software running under the GNU/Linux operating system. The course components include hardware assembly, operating system and software installation, basic system administration, and a working introduction to the engineering design process through the completion of several collaborative projects involving content-based graphic design and computer animation. Through the three semesters, the course has gravitated away from a module-based survey of fundamental concepts in computer science and engineering toward greater student exploration of fewer topics most relevant to the projects. Continuing assessment of each phase of the course provides formative and summative feedback and has played an important role in the maturation of the course.

Concurrency is a fundamental concept in computer science which is concerned with the study of systems involving multiple processes. The order of events in a concurrent system is unpredictable because of the independence of events occurring in the individual processes. Trace theory is a successful model for the execution of concurrent processes which employs congruence classes of words over partially commutative alphabets. These congruence or interchange classes generalize the more familiar notions of strings and type classes. Motivated by recent work in the areas of program profiling and compression of executable code, we consider a rate distortion problem in which the objective is to reproduce a string which is equivalent to the original string. This leads to a generalization of Kolmogorov complexity and a new graph entropy called the interchange entropy. We provide some of the basic properties of the interchange entropy. We also consider some universal compression schemes for this problem and show that for a large collection of dependence alphabets we can asymptotically attain the interchange entropy.

Invertibility is a fundamental concept in computer science, with various manifestations in software development (serializer/deserializer, parser/printer, redo/undo, compressor/decompressor, and so on). Full invertibility necessarily requires bijectivity, but the direct approach of composing bijective functions to develop invertible programs is too restrictive to be useful. In this paper, we take a different approach by focusing on partially-invertible functions—functions that become invertible if some of their arguments are fixed. The simplest example of such is addition, which becomes invertible when fixing one of the operands. More involved examples include entropy-based compression methods (e.g., Huffman coding), which carry the occurrence frequency of input symbols (in certain formats such as Huffman tree), and fixing this frequency information makes the compression methods invertible. We develop a language Sparcl for programming such functions in a natural way, where partial-invertibility is the norm and bijectivity is a special case, hence gaining significant expressiveness without compromising correctness. The challenge in designing such a language is to allow ordinary programming (the “partially” part) to interact with the invertible part freely, and yet guarantee invertibility by construction. The language Sparcl is linear-typed, and has a type constructor to distinguish data that are subject to invertible computation and those that are not. We present the syntax, type system, and semantics of the language, and prove that Sparcl correctly guarantees invertibility for its programs. We demonstrate the expressiveness of Sparcl with examples including tree rebuilding from preorder and inorder traversals and Huffman coding.

Coding is increasingly popular in schools around the world and is often taught by non-specialist teachers as an integrated task with other subject areas. In this article, we explore the relationship between computer science (CS) concepts and students’ multimodal expression in a coding animated narrative (CAN) task in the context of an integrated English-Technology unit of learning. Through this collective case study, we explore how CS concepts underpin semiotic elements of an animated narrative, analyse the factors that influence the extent to which students exercise those concepts, and reveal the tensions and opportunities that a CAN task may present for learning computer science concepts in regular, non-specialist, cross-curricular classrooms. The findings suggest that CAN tasks are unique in presenting opportunities for students to learn challenging CS concepts such as synchronisation and parallelism. At the same time, CAN tasks present tensions for teaching CS concepts in non-specialist classrooms, where student projects are often judged on their visual qualities. In such settings, procedural, rather than conceptual knowledge, may be a more efficient route to creative outcomes. It also means that drawing skills need to be prioritised. Role specialisation often led to better quality projects but at the expense of individual students’ conceptual development in computer science.

Iteration is a fundamental concept in mathematics and computer science. In the following we are interested in sequences, generated by an iteration process. Given a function f and an initial value x0, we consider properties and representations of the sequence x0, f(x0), f(f(x0)),…. This paper presents an empirical study which compares the understanding of properties of these sequences, given in different representations. The sample consists of 79 highschool students (grade 11) and 22 mathematics teachers. The empirical study is implemented by a computer-program, developed by the author. The analysis of the test shows how the subjects argue about the properties of sequences, how they are solving problems and especially which experimental and heuristic abilities they have in reading different representations.

The non-linear invariance principle of Mossel, O'Donnell and Oleszkiewicz establishes that if f(x1, ..., xn) is a multilinear low-degree polynomial with low influences then the distribution of f(B1, ..., Bn) is close (in various senses) to the distribution of f(G1, ..., Gn), where Bi ∈R {-1, 1} are independent Bernoulli random variables and Gi ~ N(0, 1) are independent standard Gaussians. The invariance principle has seen many application in theoretical computer science, including the Majority is Stablest conjecture, which shows that the Goemans--Williamson algorithm for MAXCUT is optimal under the Unique Games Conjecture. More generally, MOO's invariance principle works for any two vectors of hypercontractive random variables (X1, ..., Xn), (Y1, ..., Yn) such that (i) Matching moments: Xi and Yi have matching first and second moments, (ii) Independence: the variables X1, ..., Xn are independent, as are Y1, ..., Yn. The independence condition is crucial to the proof of the theorem, yet in some cases we would like to use distributions (X1, ..., Xn) in which the individual coordinates are not independent. A common example is the uniform distribution on the slice ([EQUATION]) which consists of all vectors (x1, ..., xn) ∈ {0, 1}n with Hamming weight k. The slice shows up in theoretical computer science (hardness amplification, direct sum testing), extremal combinatorics (Erdos--Ko--Rado theorems) and coding theory (in the guise of the Johnson association scheme). Our main result is an invariance principle in which (X1, ..., Xn) is the uniform distribution on a slice ([EQUATION]) and (Y1, ..., Yn) consists either of n independent Ber(p) random variables, or of n independent N(p, p(1 - p)) random variables. As applications, we prove a version of Majority is Stablest for functions on the slice, a version of Bourgain's tail theorem, a version of the Kindler--Safra structural theorem, and a stability version of the t-intersecting Erdos--Ko--Rado theorem, combining techniques of Wilson and Friedgut. Our proof relies on a combination of ideas from analysis and probability, algebra and combinatorics. In particular, we make essential use of recent work of the first author which describes an explicit Fourier basis for the slice.

We propose a novel proof technique that can be applied to attack a broad class of problems in computational complexity, when switching the order of universal and existential quantifiers is helpful. Our approach combines the standard min-max theorem and convex approximation techniques, offering quantitative improvements over the standard way of using min-max theorems as well as more concise and elegant proofs.

The central goal of this manuscript is to survey the relationships between fundamental physics and computer science. We begin by providing a short historical review of how different concepts of computer science have entered the field of fundamental physics, highlighting the claim that the universe is a computer. Following the review, we explain why computational concepts have been embraced to interpret and describe physical phenomena. We then discuss seven arguments against the claim that the universe is a computational system and show that those arguments are wrong because of a misunderstanding of the extension of the concept of computation. Afterwards, we address a proposal to solve Hempel’s dilemma using the computability theory but conclude that it is incorrect. After that, we discuss the relationship between the proposals that the universe is a computational system and that our minds are a simulation. Analysing these issues leads us to proposing a new physical principle, called the principle of computability, which claims that the universe is a computational system (not restricted to digital computers) and that computational power and the computational complexity hierarchy are two fundamental physical constants. On the basis of this new principle, a scientific paradigm emerges to develop fundamental theories of physics: the computer-theoretic framework (CTF). The CTF brings to light different ideas already implicit in the work of several researchers and provides a new view on the universe based on computer theoretic concepts that expands the current view. We address different issues regarding the development of fundamental theories of physics in the new paradigm. Additionally, we discuss how the CTF brings new perspectives to different issues, such as the unreasonable effectiveness of mathematics and the foundations of cognitive science.

Drucker (2012) proved the following result: Unless the unlikely complexity-theoretic collapse coNP is in NP/poly occurs, there is no AND-compression for SAT. The result has implications for the compressibility and kernelizability of a whole range of NP-complete parameterized problems. We present a streamlined proof of Drucker's theorem. An AND-compression is a deterministic polynomial-time algorithm that maps a set of SAT-instances $x_1,\dots,x_t$ to a single SAT-instance $y$ of size poly(max $|x_i|$) such that $y$ is satisfiable if and only if all $x_i$ are satisfiable. The "AND" in the name stems from the fact that the predicate "$y$ is satisfiable" can be written as the AND of all predicates "$x_i$ is satisfiable". Drucker's result complements the result by Bodlaender et al. (2009) and Fortnow and Santhanam (2010), who proved the analogous statement for OR-compressions, and Drucker's proof not only subsumes that result but also extends it to randomized compression algorithms that are allowed to have a certain probability of failure. Drucker (2012) presented two proofs: The first uses information theory and the minimax theorem from game theory, and the second is an elementary, iterative proof that is not as general. In our proof, we realize the iterative structure as a generalization of the arguments of Ko (1983) for P-selective sets, which use the fact that tournaments have dominating sets of logarithmic size. We generalize this fact to hypergraph tournaments. Our proof achieves the full generality of Drucker's theorem, avoids the minimax theorem, and restricts the use of information theory to a single, intuitive lemma about the average noise sensitivity of compressive maps. To prove this lemma, we use the same information-theoretic inequalities as Drucker.

As a specific application of the material presented in Part I, this companion paper identifies VLSI layout strategies for realizing correlative encoded MSK-type Viterbi receivers. When the source symbols are correlatively encoded using a first-order polynomial, the appropriate Viterbi receiver takes the form of a cube-connected cycle (CCC) structure. Second-order encoding polynomials give rise to a new type of area-efficient VLSI structure which is a generalization of the CCC structure. The results are important from two perspectives: 1) Isomorphisms between certain concepts in theoretical computer science and digital communications are established, and 2) good practical VLSI layouts are generated, by a structured design methodology, which commercial silicon foundries can fabricate.

Chapter 17 Computation models and function algebras

Bisimilar linear systems

Semilinear sets are one of the most important concepts in theoretical computer science, as illustrated by the fact that the set of nonnegative integer solutions to any system of Diophantine equations is semilinear. Parikh's theorem enables us to represent any semilinear set as a pushdown automaton (PDA). We summarize recent results on the descriptional complexity of conversions among different representations of a semilinear set: as a vector set (conventional), a finite automaton (FA), a PDA, etc.. We also discuss semilinearity-preserving operations like union, intersection, and complement. We use Parikh's theorem to enlarge the class of finite-state machines that can represent semilinear sets. In particular, we give a simpler proof of a known result that characterizes semilinear sets in terms of machines with reversal-bounded counters. We then investigate the power of such a machine with only one counter in the context of a long-standing conjecture about repetition on words.

Currently, robots are being used as teaching tools and can be found in K-12 and college classrooms. The primary appeal associated with employing robots is that they tend to capture the attention of the target audience and thereby facilitate the learning process. While there are numerous robotic kits on the market today, few can be used to teach both high-level concepts in computer science and engineering as well as low-level concepts in a K-12 environment and those that are capable, are more expensive. In this paper we propose an affordable setup of a basic software and hardware framework to accomplish this interaction. Specifically, the overall system architecture, low-cost robot, Web service and mobile device software will be the topics of interest in this paper.

Equilibrium analysis of the distribution of information in human society