Abstract: A classical method of constructing a linear code over $ {\mathrm {GF}}(q)$ with a $t$ -design is to use the incidence matrix of the $t$ -design as a generator matrix over $ {\mathrm {GF}}(q)$ of the code. This approach has been extensively investigated in the literature. In this paper, a different method of constructing linear codes using specific classes of 2-designs is studied, and linear codes with a few weights are obtained from almost difference sets, difference sets, and a type of 2-designs associated to semibent functions. Two families of the codes obtained in this paper are optimal. The linear codes presented in this paper have applications in secret sharing and authentication schemes, in addition to their applications in consumer electronics, communication and data storage systems. A coding-theory approach to the characterization of highly nonlinear Boolean functions is presented.

Abstract: Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.

Abstract: We present a new CEGAR-based algorithm for QBF. The algorithm builds on a decomposition of QBFs into a sequence of propositional formulas, which we call the clausal abstraction. Each of the propositional formulas contains the variables of just one quantifier level and additional variables describing the interaction with adjacent quantifier levels. This decomposition leads to a simpler notion of refinement compared to earlier approaches. We also show how to effectively construct Skolem and Herbrand functions from true, respectively false, QBFs; allowing us to certify the solver result. We implemented the algorithm in a solver called CAQE. The experimental evaluation shows that CAQE has competitive performance compared to current QBF solvers and outperforms previous certifying solvers.

Abstract: As the CMOS technology is gradually scaling down to inherent physical device limits, significant challenges emerge related to scalability, leakage, reliability, etc. Alternative technologies are under research for next-generation VLSI circuits. Memristor is one of the promising candidates due to its scalability, practically zero leakage, non-volatility, etc. This paper proposes a novel design methodology for logic circuits targeting memristor crossbars. This methodology allows the optimization of the design of logic function, and their automatic mapping on the memristor crossbar. More important, this methodology supports the execution of Boolean logic functions within constant number of steps independent of its functionality. To illustrate the potential of the proposed methodology, multi-bit adders and multipliers are explored; their incurred delay, area and energy costs are analyzed. The comparison of our approach with state-of-the-art Boolean logic circuits for memristor crossbar architecture shows significant improvement in both delay (4 to 500 x) and energy consumption (1.22 to 3.71 x). The area overhead may decrease (down to 44%) or increase (up to 17%) depending on the circuit's functionality and logic optimization level.

Abstract: Boolean functions have important applications in cryptography and coding theory. Two famous classes of binary codes derived from Boolean functions are the Reed-Muller codes and Kerdock codes. In the past two decades, a lot of progress on the study of applications of Boolean functions in coding theory has been made. Two generic constructions of binary linear codes with Boolean functions have been well investigated in the literature. The objective of this paper is twofold. The first is to provide a survey on recent results, and the other is to propose open problems on one of the two generic constructions of binary linear codes with Boolean functions. These open problems are expected to stimulate further research on binary linear codes from Boolean functions.

Abstract: Boolean plateaued functions and vectorial functions with plateaued components play a significant role in cryptography, sequences for communications, and the related combinatorics and designs. Our knowledge on them is not at a level corresponding to their importance. We introduce new characterizations of plateaued Boolean functions. We give the characterizations of vectorial functions whose components are all plateaued (with possibly different amplitudes), that we simply call plateaued, by means of the value distributions of their derivatives (we characterize similarly those functions whose components are partially bent) and autocorrelation functions, and of the power moments of their Walsh transform. This allows us to derive several characterizations of almost perfect nonlinear (APN) functions in this framework. We prove that all the main results known for quadratic APN functions extend to plateaued functions, allowing the study of their APN-ness to be simplified. We show that if, additionally, the component functions are all unbalanced, this study is still simpler: the APN-ness of such functions depends only on their value distribution. This allows proving, for instance, that any plateaued $(n,n)$ -function, $n$ even, having similar value distribution as the APN power functions, is APN, and has the same extended Walsh spectrum as the APN Gold functions. As by-products, we obtain a few other new results. For instance, any plateaued function in even dimension, which is Carlet-Charpin-Zinoviev (CCZ)-equivalent to a Gold or Kasami APN function, is necessarily extended affine (EA)-equivalent to it.

Abstract: Card-based protocols that are based on a deck of physical cards achieve secure multi-party computation with information-theoretic secrecy. Using existing AND, XOR, NOT, and copy protocols, one can naively construct a secure computation protocol for any given (multivariable) Boolean function as long as there are plenty of additional cards. However, an explicit sufficient number of cards for computing any function has not been revealed thus far. In this paper, we propose a general approach to constructing an efficient protocol so that six additional cards are sufficient for any function to be securely computed. Further, we prove that two additional cards are sufficient for any symmetric function.

Abstract: Fairness is a desirable property in secure computation; informally it means that if one party gets the output of the function, then all parties get the output. Alas, an implication of Cleve’s result (STOC 86) is that when there is no honest majority, in particular in the important case of the two-party setting, there exist Boolean functions that cannot be computed with fairness. In a surprising result, Gordon et al. (JACM 2011) showed that some interesting functions can be computed with fairness in the two-party setting, and re-opened the question of understanding which Boolean functions can be computed with fairness, and which cannot.

Abstract: In this chapter, three definitions of a bent function are given: via nonlinearity, by using Walsh-Hadamard coefficients, and in terms of derivatives Nonlinearity of a random Boolean function is discussed Several open problems in bent functions are included In the last section, we list surveys on bent functions in separate articles and in chapters in books on discrete mathematics and cryptography

Abstract: Reversible logic represents the basis for many emerging technologies and has recently been intensively studied. However, most of the Boolean functions of practical interest are irreversible and must be embedded into a reversible function before they can be synthesized. Thus far, an optimal embedding is guaranteed only for small functions, whereas a significant overhead results when large functions are considered. We study this issue in this article. We prove that determining an optimal embedding is coNP-hard already for restricted cases. Then, we propose heuristic and exact methods for determining both the number of additional lines and a corresponding embedding. For the approaches, we considered sum of products and binary decision diagrams as function representations. Experimental evaluations show the applicability of the approaches for large functions. Consequently, the reversible embedding of large functions is enabled as a precursor to subsequent synthesis.

Abstract: We show a power 2.5 separation between bounded-error randomized and quantum query complexity for a total Boolean function, refuting the widely believed conjecture that the best such separation could only be quadratic (from Grover's algorithm). We also present a total function with a power 4 separation between quantum query complexity and approximate polynomial degree, showing severe limitations on the power of the polynomial method. Finally, we exhibit a total function with a quadratic gap between quantum query complexity and certificate complexity, which is optimal (up to log factors). These separations are shown using a new, general technique that we call the cheat sheet technique. The technique is based on a generic transformation that converts any (possibly partial) function into a new total function with desirable properties for showing separations. The framework also allows many known separations, including some recent breakthrough results of Ambainis et al., to be shown in a unified manner.

Abstract: We design logic circuits based on the notion of zero forcing on graphs; each gate of the circuits is a gadget in which zero forcing is performed. We show that such circuits can evaluate every monotone Boolean function. By using two vertices to encode each logical bit, we obtain universal computation. We also highlight a phenomenon of "back forcing" as a property of each function. Such a phenomenon occurs in a circuit when the input of gates which have been already used at a given time step is further modified by a computation actually performed at a later stage. Finally, we show that zero forcing can be also used to implement reversible computation. The model introduced here provides a potentially new tool in the analysis of Boolean functions, with particular attention to monotonicity. Moreover, in the light of applications of zero forcing in quantum mechanics, the link with Boolean functions may suggest a new directions in quantum control theory and in the study of engineered quantum spin systems. It is an open technical problem to verify whether there is a link between zero forcing and computation with contact circuits.

Abstract: In 1986, Saks and Wigderson conjectured that the largest separation between deterministic and zero-error randomized query complexity for a total boolean function is given by the function $f$ on $n=2^k$ bits defined by a complete binary tree of NAND gates of depth $k$, which achieves $R_0(f) = O(D(f)^{0.7537\ldots})$. We show this is false by giving an example of a total boolean function $f$ on $n$ bits whose deterministic query complexity is $\Omega(n/\log(n))$ while its zero-error randomized query complexity is $\tilde O(\sqrt{n})$. We further show that the quantum query complexity of the same function is $\tilde O(n^{1/4})$, giving the first example of a total function with a super-quadratic gap between its quantum and deterministic query complexities. We also construct a total boolean function $g$ on $n$ variables that has zero-error randomized query complexity $\Omega(n/\log(n))$ and bounded-error randomized query complexity $R(g) = \tilde O(\sqrt{n})$. This is the first super-linear separation between these two complexity measures. The exact quantum query complexity of the same function is $Q_E(g) = \tilde O(\sqrt{n})$. These two functions show that the relations $D(f) = O(R_1(f)^2)$ and $R_0(f) = \tilde O(R(f)^2)$ are optimal, up to poly-logarithmic factors. Further variations of these functions give additional separations between other query complexity measures: a cubic separation between $Q$ and $R_0$, a $3/2$-power separation between $Q_E$ and $R$, and a 4th power separation between approximate degree and bounded-error randomized query complexity. All of these examples are variants of a function recently introduced by \goos, Pitassi, and Watson which they used to separate the unambiguous 1-certificate complexity from deterministic query complexity and to resolve the famous Clique versus Independent Set problem in communication complexity.

Abstract: We propose a genetic algorithm GA to search for plateaued boolean functions, which represent suitable candidates for the design of stream ciphers due to their good cryptographic properties. Using the spectral inversion technique introduced by Clark, Jacob, Maitra and Stanica, our GA encodes the chromosome of a candidate solution as a permutation of a three-valued Walsh spectrum. Additionally, we design specialized crossover and mutation operators so that the swapped positions in the offspring chromosomes correspond to different values in the resulting Walsh spectra. Some tests performed on the set of pseudoboolean functions of $$n=6$$ and $$n=7$$ variables show that in the former case our GA outperforms Clark et al.'s simulated annealing algorithm with respect to the ratio of generated plateaued boolean functions per number of optimization runs.

Abstract: We prove an average-case depth hierarchy theorem for Boolean circuits over the standard basis of $\mathsf{AND}$, $\mathsf{OR}$, and $\mathsf{NOT}$ gates. Our hierarchy theorem says that for every $d \geq 2$, there is an explicit $n$-variable Boolean function $f$, computed by a linear-size depth-$d$ formula, which is such that any depth-$(d-1)$ circuit that agrees with $f$ on $(1/2 + o_n(1))$ fraction of all inputs must have size $\exp({n^{\Omega(1/d)}}).$ This answers an open question posed by H{\aa}stad in his Ph.D. thesis. Our average-case depth hierarchy theorem implies that the polynomial hierarchy is infinite relative to a random oracle with probability 1, confirming a conjecture of H{\aa}stad, Cai, and Babai. We also use our result to show that there is no "approximate converse" to the results of Linial, Mansour, Nisan and Boppana on the total influence of small-depth circuits, thus answering a question posed by O'Donnell, Kalai, and Hatami. A key ingredient in our proof is a notion of \emph{random projections} which generalize random restrictions.

Abstract: We develop a new method to prove communication lower bounds for composed functions of the form f o gn where f is any boolean function on n inputs and g is a sufficiently "hard" two-party gadget. Our main structure theorem states that each rectangle in the communication matrix of f o gn can be simulated by a nonnegative combination of juntas. This is the strongest yet formalization for the intuition that each low-communication randomized protocol can only "query" few inputs of f as encoded by the gadget g. Consequently, we characterize the communication complexity of f o gn in all known one-sided zero-communication models by a corresponding query complexity measure of f. These models in turn capture important lower bound techniques such as corruption, smooth rectangle bound, relaxed partition bound, and extended discrepancy. As applications, we resolve several open problems from prior work: We show that SBPcc (a class characterized by corruption) is not closed under intersection. An immediate corollary is that MAcc ≠ SBPcc. These results answer questions of Klauck (CCC 2003) and Bohler et al. (JCSS 2006). We also show that approximate nonnegative rank of partial boolean matrices does not admit efficient error reduction. This answers a question of Kol et al. (ICALP) for partial matrices.

Abstract: We present a Boolean logic optimization framework based on Majority-Inverter Graph (MIG). An MIG is a directed acyclic graph consisting of three-input majority nodes and regular/complemented edges. Current MIG optimization is supported by a consistent algebraic framework. However, when algebraic methods cannot improve a result quality, stronger Boolean methods are needed to attain further optimization. For this purpose, we propose MIG Boolean methods exploiting the error masking property of majority operators. Our MIG Boolean methods insert logic errors that strongly simplify an MIG while being successively masked by the voting nature of majority nodes. Thanks to the data-structure/methodology fitness, our MIG Boolean methods run in principle as fast as algebraic counterparts. Experiments show that our Boolean methodology combined with state-of-art MIG algebraic techniques enable superior optimization quality. For example, when targeting depth reduction, our MIG optimizer transforms a ripple carry adder into a carry look-ahead one. Considering the set of IWLS'05 (arithmetic intensive) benchmarks, our MIG optimizer reduces by 17.98% (26.69%) the logic network depth while also enhancing size and power activity metrics, with respect to ABC academic optimizer. Without MIG Boolean methods, i.e., using MIG algebraic optimization alone, the previous gains are halved. Employed as front-end to a delay-critical 22-nm ASIC flow (logic synthesis + physical design) our MIG optimizer reduces the average delay/area/power by (15.07%, 4.93%, 1.93%), over 27 academic and industrial benchmarks, as compared to a leading commercial ASIC flow.

Abstract: Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boolean function, has been extensively studied in the context of network modeling where each layer of canalization adds a degree of stability in the dynamics of the network. Recently, dynamic network control approaches have been used for the design of new therapeutic interventions and for other applications such as stem cell reprogramming. This work studies the role of canalization in the control of Boolean molecular networks. It provides a method for identifying the potential edges to control in the wiring diagram of a network for avoiding undesirable state transitions. The method is based on identifying appropriate input-output combinations on undesirable transitions that can be modified using the edges in the wiring diagram of the network. Moreover, a method for estimating the number of changed transitions in the state space of the system as a result of an edge deletion in the wiring diagram is presented. The control methods of this paper were applied to a mutated cell-cycle model and to a p53-mdm2 model to identify potential control targets.

Abstract: The e-approximate degree of a Boolean function f : { - 1 , 1 } n ? { - 1 , 1 } is the minimum degree of a real polynomial that approximates f to within error e in the ? ∞ norm. We prove several lower bounds on this important complexity measure by explicitly constructing solutions to the dual of an appropriate linear program. Our first result resolves the e-approximate degree of the two-level AND-OR tree for any constant e 0 . We show that this quantity is ? ( n ) , closing a line of incrementally larger lower bounds. The same lower bound was recently obtained independently by Sherstov (Theory Comput. 2013) using related techniques. Our second result gives an explicit dual polynomial that witnesses a tight lower bound for the approximate degree of any symmetric Boolean function, addressing a question of Spalek (2008). Our final contribution is to reprove several Markov-type inequalities from approximation theory by constructing explicit dual solutions to natural linear programs. These inequalities underly the proofs of many of the best-known approximate degree lower bounds, and have important uses throughout theoretical computer science.

Abstract: We prove an average-case depth hierarchy theorem for Boolean circuits over the standard basis of AND, OR, and NOT gates. Our hierarchy theorem says that for every d a#x2265; 2, there is an explicit n-variable Boolean function f, computed by a linear-size depth-d formula, which is such that any depth-(d -- 1) circuit that agrees with f on (1/2 + on(1)) fraction of all inputs must have size exp(na#x03A9;(1/d)). This answers an open question posed by Has tad in his Ph.D. Thesis [Has86b]. Our average-case depth hierarchy theorem implies that the polynomial hierarchy is infinite relative to a random oracle with probability 1, confirming a conjecture of Has tad [Has86a], Cai [Cai86], and Babai [Bab87]. We also use our result to show that there is no "approximate converse" to the results of Linial, Mansour, Nisan [LMN93] and Boppana [Bop97] on the total influence of constant-depth circuits, thus answering a question posed by Kalai [Kal12] and Hatami [Hat14]. A key ingredient in our proof is a notion of random projections which generalize random restrictions.

Abstract: This paper studies the problem of learning "low-complexity" probability distributions over the Boolean hypercube {−1, 1}n. As in the standard PAC learning model, a learning problem in our framework is defined by a class C of Boolean functions over {−1, 1}n, but in our model the learning algorithm is given uniform random satisfying assignments of an unknown f ∈ C and its goal is to output a high-accuracy approximation of the uniform distribution over f−1 (1). This distribution learning problem may be viewed as a demanding variant of standard Boolean function learning, where the learning algorithm only receives positive examples and --- more importantly --- must output a hypothesis function which has small multiplicative error (i.e. small error relative to the size of f−1(1)).As our main results, we show that the two most widely studied classes of Boolean functions in computational learning theory --- linear threshold functions and DNF formulas --- have efficient distribution learning algorithms in our model. Our algorithm for linear threshold functions runs in time poly(n, 1/e) and our algorithm for polynomial-size DNF runs in time quasipoly(n, 1/e). We obtain both these results via a general approach that combines a broad range of technical ingredients, including the complexity-theoretic study of approximate counting and uniform generation; the Statistical Query model from learning theory; and hypothesis testing techniques from statistics. A key conceptual and technical ingredient of this approach is a new kind of algorithm which we devise called a "densifier" and which we believe may be useful in other contexts.We also establish limitations on efficient learnability in our model by showing that the existence of certain types of cryptographic signature schemes imply that certain learning problems in our framework are computationally hard. Via this connection we show that assuming the existence of sufficiently strong unique signature schemes, there are no sub-exponential time learning algorithms in our framework for intersections of two halfspaces, for degree-2 polynomial threshold functions, or for monotone 2-CNF formulas. Thus our positive results for distribution learning come close to the limits of what can be achieved by efficient algorithms.

Abstract: The study of monotonicity and negation complexity for Bool-ean functions has been prevalent in complexity theory as well as in computational learning theory, but little attention has been given to it in the cryptographic context. Recently, Goldreich and Izsak (2012) have initiated a study of whether cryptographic primitives can be monotone, and showed that one-way functions can be monotone (assuming they exist), but a pseudorandom generator cannot.

Abstract: In this paper, we study the following variant of the junta learning problem. We are given oracle access to a Boolean function f on n variables that only depends on k variables, and, when restricted to them, equals some predefined function h. The task is to identify the variables the function depends on.When h is the XOR or the OR function, this gives a restricted variant of the Bernstein---Vazirani or the combinatorial group testing problem, respectively. We analyze the general case using the adversary bound and give an alternative formulation for the quantum query complexity of this problem. We construct optimal quantum query algorithms for the cases when h is the OR function (complexity is $${\Theta(\sqrt{k})}$$?(k)) or the exact-half function (complexity is $${\Theta(k^{1/4})}$$?(k1/4)). The first algorithm resolves an open problem from Ambainis & Montanaro (Quantum Inf Comput 14(5&6): 439---453, 2014). For the case when h is the majority function, we prove an upper bound of $${O(k^{1/4})}$$O(k1/4). All these algorithms can be made exact. We obtain a quartic improvement when compared to the randomized complexity (if h is the exact-half or the majority function), and a quadratic one when compared to the non-adaptive quantum complexity (for all functions considered in the paper).

Abstract: We introduce a new class of succinct games, called weighted boolean formula games. Here, each player has a set of boolean formulas he wants to get satisfied. The boolean formulas of all players involve a ground set of boolean variables, and every player controls some of these variables. The payoff of a player is the weighted sum of the values of his boolean formulas. We consider pure Nash equilibria [18] and their well-studied refinement of payoff-dominant equilibria [12], where every player is no-worse-off than in any other pure Nash equilibrium. We study both structural and complexity properties for both decision and search problems. - We consider a subclass of weighted boolean formula games, called mutual weighted boolean formula games, which make a natural mutuality assumption. We present a very simple exact potential for mutual weighted boolean formula games. We also prove that each weighted, linear-affine (network) congestion game with player-specific constants is polynomial, sound monomorphic to a mutual weighted boolean formula game. In a general way,we prove that each weighted, linear-affine (network) congestion game with player-specific coefficients and constants is polynomial, sound monomorphic to a weighted boolean formula game. - We present a comprehensive collection of high intractability results. These results show that the computational complexity of decision (and search) problems for both payoff-dominant and pure Nash equilibria in weighted boolean formula games depends in a crucial way on five parameters: (i) the number of players; (ii) the number of variables per player; (iii) the number of boolean formulas per player; (iv) the weights in the payoff functions (whether identical or nonidentical), and (v) the syntax of the boolean formulas. These results show that decision problems for payoff-dominant equilibria are considerably harder than for pure Nash equilibria (unless the polynomial hierarchy collapses).

Abstract: We investigate the influences of variables on a Boolean function $$f$$f based on the quantum Bernstein---Vazirani algorithm. A previous paper (Floess et al. in Math Struct Comput Sci 23:386, 2013) has proved that if an $$n$$n-variable Boolean function $$f(x_1,\ldots ,x_n)$$f(x1,?,xn) does not depend on an input variable $$x_i$$xi, using the Bernstein---Vazirani circuit for $$f$$f will always output $$y$$y that has a 0 in the $$i$$ith position. We generalize this result and show that, after running this algorithm once, the probability of getting a 1 in each position $$i$$i is equal to the dependence degree of $$f$$f on the variable $$x_i$$xi, i.e., the influence of $$x_i$$xi on $$f$$f. Based on this, we give an approximation algorithm to evaluate the influence of any variable on a Boolean function. Next, as an application, we use it to study the Boolean functions with juntas and construct probabilistic quantum algorithms to learn certain Boolean functions. Compared with the deterministic algorithms given by Floess et al., our probabilistic algorithms are faster.

Abstract: This paper introduces PyEDA, a Python library for electronic design automation (EDA). PyEDA provides both a high level interface to the representation of Boolean functions, and blazingly-fast C extensions for fundamental algorithms where performance is essential. PyEDA is a hobby project which has the simple but audacious goal of improving the state of digital design by using