The LISP Machine (LM) is a high-level model of computation using a linked memory structure. The hierarchical memory model(HMM) has a random access memory but takes into account the cost of memory access. We show that ...
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The LISP Machine (LM) is a high-level model of computation using a linked memory structure. The hierarchical memory model(HMM) has a random access memory but takes into account the cost of memory access. We show that the HMM can be simulated by the LM in real time. On the other hand, for simulating an on-line LM program of time t and space s by the HMM we prove time bounds of O(t log s). These are shown to be tight for data types which are incompressible - an information-theoretic nation, allowing for models which handle a variety of data types.
A function mu(x) that assigns a nonnegative real number mu(x) to each bit string x is said to be malign if, for any algorithm: the worst-case computation time and the average-case computation time of the algorithm are...
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A function mu(x) that assigns a nonnegative real number mu(x) to each bit string x is said to be malign if, for any algorithm: the worst-case computation time and the average-case computation time of the algorithm are functions of the same order when each bit string I is given to the algorithm as an input with the probability that is proportional to the value mu(x). M. Li and P.M.B. Vitanyi found that functions that are known as ''universal distributions'' are malign. We say that a function f(x) preserves malignness of universal distributions if f(mu(x)) is malign for any universal distribution ic(x) We show one necessary and sufficient condition for f(x) to preserve malignness of universal distributions under the assumption that f(x) satisfies some additional conditions. As an application of this result, we show that f(x)=x(t) preserves malignness of universal distributions or does not according as t greater than or equal to 1 or 0 < t < 1.
Enantioselective functionalization of prochiral C-H bonds is a highly attractive strategy for the construction of chiral *** extensive efforts[1],enantioselective C（sp3）-H functionalization still remains challenging...
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Enantioselective functionalization of prochiral C-H bonds is a highly attractive strategy for the construction of chiral *** extensive efforts[1],enantioselective C（sp3）-H functionalization still remains challenging at *** instance,although enantioselective C-H functionalization via desymmetrization of two carbon centers has been accomplished with Pd catalysis and a directing group,differenciation of the prochiral C-H bonds on one methylene carbon center has rarely been *** a different front,introduction of a transient chiral directing group is expected to be useful in enantioselective C-H arylation of C（sp2）-H ***,
In this work, we have developed an elegant algorithm to study the cosmological consequences from a huge class of quantum field theories (i.e. superstring theory, supergravity, extra dimensional theory, modified gravit...
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In this work, we have developed an elegant algorithm to study the cosmological consequences from a huge class of quantum field theories (i.e. superstring theory, supergravity, extra dimensional theory, modified gravity, etc.), which are equivalently described by soft attractors in the effective field theory framework. In this description we have restricted our analysis for two scalar fields - dilaton and Higgsotic fields minimally coupled with Einstein gravity, which can be generalized for any arbitrary number of scalar field contents with generalized non-canonical and non-minimal interactions. We have explicitly used R-2 gravity, from which we have studied the attractor and non-attractor phases by exactly computing two point, three point and four point correlation functions from scalar fluctuations using the In-In (Schwinger-Keldysh) and the delta N formalisms. We have also presented theoretical bounds on the amplitude, tilt and running of the primordial power spectrum, various shapes (equilateral, squeezed, folded kite or counter-collinear) of the amplitude as obtained from three and four point scalar functions, which are consistent with observed data. Also the results from two point tensor fluctuations and the field excursion formula are explicitly presented for the attractor and non-attractor phase. Further, reheating constraints, scale dependent behavior of the couplings and the dynamical solution for the dilaton and Higgsotic fields are also presented. New sets of consistency relations between two, three and four point observables are also presented, which shows significant deviation from canonical slow-roll models. Additionally, three possible theoretical proposals have presented to overcome the tachyonic instability at the time of late time acceleration. Finally, we have also provided the bulk interpretation from the three and four point scalar correlation functions for completeness.
A theoretical framework for quantum neural network (QNN) overparametrization, a phase transition in loss landscape complexity, is established. The precise characterization of the critical number of parameters offered ...
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A theoretical framework for quantum neural network (QNN) overparametrization, a phase transition in loss landscape complexity, is established. The precise characterization of the critical number of parameters offered is expected to impact QNN design. The prospect of achieving quantum advantage with quantum neural networks (QNNs) is exciting. Understanding how QNN properties (for example, the number of parameters M) affect the loss landscape is crucial to designing scalable QNN architectures. Here we rigorously analyze the overparametrization phenomenon in QNNs, defining overparametrization as the regime where the QNN has more than a critical number of parameters M-c allowing it to explore all relevant directions in state space. Our main results show that the dimension of the Lie algebra obtained from the generators of the QNN is an upper bound for M-c, and for the maximal rank that the quantum Fisher information and Hessian matrices can reach. Underparametrized QNNs have spurious local minima in the loss landscape that start disappearing when M & GE;M-c. Thus, the overparametrization onset corresponds to a computational phase transition where the QNN trainability is greatly improved. We then connect the notion of overparametrization to the QNN capacity, so that when a QNN is overparametrized, its capacity achieves its maximum possible value.
Let f ( x 1 , …, x k ) be a Boolean function that k parties wish to collaboratively evaluate, where each x i is a bit-string of length n. The ith party knows each input argument except x i ; and each party has unlimi...
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Let f ( x 1 , …, x k ) be a Boolean function that k parties wish to collaboratively evaluate, where each x i is a bit-string of length n. The ith party knows each input argument except x i ; and each party has unlimited computational power. They share a blackboard, viewed by all parties, where they can exchange messages. The objective is to minimize the number of bits written on the board. We prove lower bounds of the form Ω(n · c −k ), for the number of bits that need to be exchanged in order to compute some (explicitly given) polynomial time computable functions. Our bounds hold even if the parties only wish to have a 1 % advantage at guessing the value of f on random inputs. The lower bound proofs are based on discrepancy upper bounds for specific functions over “cylinder intersection” sets. These results may be of independent interest. We give several applications of the lower bounds. The first application is a pseudorandom generator for Logspace. We explicitly construct (in polynomial time pseudorandom sequences of length n from a random seed of length exp( c √log n ) that no Logspace Turing machine will be able to distinguish from truly random sequences. As a corollary we give an explicit construction of a universal traversal sequence of length exp(exp( c √log n )) for arbitrary undirected graphs on n vertices. We then apply the multiparty protocol lower bounds to derive several new time-space trade-offs. We give a tight time-space trade-off of the form TS =Θ( n 2 ), for general, k -head Turing machines; the bounds hold for a function that can be computed in linear time and constant space by a k + 1-head Turing machine. We also give a new length-width trade-off for oblivious branching programs; in particular, our bound implies new lower bounds on the size of arbitrary branching programs, or on the size of Boolean formulas (over an arbitrary finite base). Using universal hashing, Nisan has recently constructed considerably improved random generators for Logspac
The work reported in this paper refers to Massey's proof of the surface classification theorem based on the standard word-rewriting treatment of surfaces. We arrange this approach into a formal rewriting system R ...
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The work reported in this paper refers to Massey's proof of the surface classification theorem based on the standard word-rewriting treatment of surfaces. We arrange this approach into a formal rewriting system R and provide a new version of Massey's argument. Moreover, we study the computational properties of two subsystems of R: R-or for dealing with words denoting orientable surfaces and R-nor for dealing with words denoting non-orientable surfaces. We show how such properties induce an alternative proof for the surface classification in which the basic homeomorphism between the connected sum of three projective planes and the connected sum of a torus with a projective plane is not required.
This paper studies themultiple-split load sharing mechanism of gears in two-stage external meshing planetary transmission system of aeroengine. According to the eccentric error, gear tooth thickness error, pitch error...
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This paper studies themultiple-split load sharing mechanism of gears in two-stage external meshing planetary transmission system of aeroengine. According to the eccentric error, gear tooth thickness error, pitch error, installation error, and bearing manufacturing error, we performed the meshing error analysis of equivalent angles, respectively, and we also considered the floating meshing error caused by the variation of the meshing backlash, which is from the floating of all gears at the same time. Finally, we obtained the comprehensive angle meshing error of the two-stage meshing line, established a refined mathematical computational model of 2-stage external 3-split loading sharing coefficient in consideration of displacement compatibility, got the regular curves of the load sharing coefficient and load sharing characteristic curve of full floating multiple-split and multiple-stage system, and took the variation law of the floating track and the floating quantity of the center wheel. These provide a scientific theory to determine the load sharing coefficient, reasonable load distribution, and control tolerances in aviation design and manufacturing.
Every graph generated by a hyperedge replacement graph-grammar can be represented by a tree, namely the derivation tree of the derivation sequence that produced it. Certain functions on graphs can be computed recursiv...
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Every graph generated by a hyperedge replacement graph-grammar can be represented by a tree, namely the derivation tree of the derivation sequence that produced it. Certain functions on graphs can be computed recursively on the derivation trees of these graphs. By using monadic second-order logic and semiring homomorphisms, we describe in a single formalism a large class of such functions. Polynomial and even linear algorithms can be constructed for some of these functions. We unify similar results obtained by Takamizawa et al. (1982), Bern et al. (1987), Arnborg et al. (1991) and Habel et al. (1989).
作者：
Yang, BeiDai, QingNatl Ctr Nanosci & Technol
CAS Ctr Excellence Nanosci CAS Key Lab Nanophoton Mat & Devices CAS Key Lab Standardizat & Measurement Nanotechnol Beijing 100190 Peoples R China Univ Chinese Acad Sci
Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China
Near-field radiative thermal rectifiers (NFRTRs) enabling directional heat transport hold great promise for various applications, including thermal logic computing, thermal management, and energy conversion. Current N...
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Near-field radiative thermal rectifiers (NFRTRs) enabling directional heat transport hold great promise for various applications, including thermal logic computing, thermal management, and energy conversion. Current NFRTR designs rely on dissimilar terminal materials with high contrasts in their temperature-dependent dielectric properties, which in turn hinders the spectral match for radiative heat transfer and thus limits the device's efficiency. Herein, this dilemma is solved by designing heterostructures where a pair of polaritonic layers are separately stacked on a thermally-expanding layer and a rigid substrate, spaced by a vacuum gap. In this scheme, the symmetric polaritonic layers can provide stable near-field radiative channels for heat transfer, while the thermally-expanding layer can modulate the gap size with flipped temperature bias to allow high contrasts in heat flux. In exemplified implementations, the hBN-based design has achieved a record-high thermal rectification factor (TRF, similar to 10(4)) even under small thermal gradients (similar to 20 K), which can be further boosted by polaritonic hybridizations in the graphene/hBN-based design. This study paves the way to design novel NFRTRs with 2D materials, thus providing enriched polaritons to realize higher TRFs.
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