127,528 research outputs found
Finding the way forward for forensic science in the US:a commentary on the PCAST report
A recent report by the US President’s Council of Advisors on Science and Technology (PCAST) [1] has made a number of recommendations for the future development of forensic science. Whereas we all agree that there is much need for change, we find that the PCAST report recommendations are founded on serious misunderstandings. We explain the traditional forensic paradigms of match and identification and the more recent foundation of the logical approach to evidence evaluation. This forms the groundwork for exposing many sources of confusion in the PCAST report. We explain how the notion of treating the scientist as a black box and the assignment of evidential weight through error rates is overly restrictive and misconceived. Our own view sees inferential logic, the development of calibrated knowledge and understanding of scientists as the core of the advance of the profession
Neural-network dedicated processor for solving competitive assignment problems
A neural-network processor for solving first-order competitive assignment problems consists of a matrix of N x M processing units, each of which corresponds to the pairing of a first number of elements of (R sub i) with a second number of elements (C sub j), wherein limits of the first number are programmed in row control superneurons, and limits of the second number are programmed in column superneurons as MIN and MAX values. The cost (weight) W sub ij of the pairings is programmed separately into each PU. For each row and column of PU's, a dedicated constraint superneuron insures that the number of active neurons within the associated row or column fall within a specified range. Annealing is provided by gradually increasing the PU gain for each row and column or increasing positive feedback to each PU, the latter being effective to increase hysteresis of each PU or by combining both of these techniques
Symbolic Exact Inference for Discrete Probabilistic Programs
The computational burden of probabilistic inference remains a hurdle for
applying probabilistic programming languages to practical problems of interest.
In this work, we provide a semantic and algorithmic foundation for efficient
exact inference on discrete-valued finite-domain imperative probabilistic
programs. We leverage and generalize efficient inference procedures for
Bayesian networks, which exploit the structure of the network to decompose the
inference task, thereby avoiding full path enumeration. To do this, we first
compile probabilistic programs to a symbolic representation. Then we adapt
techniques from the probabilistic logic programming and artificial intelligence
communities in order to perform inference on the symbolic representation. We
formalize our approach, prove it sound, and experimentally validate it against
existing exact and approximate inference techniques. We show that our inference
approach is competitive with inference procedures specialized for Bayesian
networks, thereby expanding the class of probabilistic programs that can be
practically analyzed
Digital IP Protection Using Threshold Voltage Control
This paper proposes a method to completely hide the functionality of a
digital standard cell. This is accomplished by a differential threshold logic
gate (TLG). A TLG with inputs implements a subset of Boolean functions of
variables that are linear threshold functions. The output of such a gate is
one if and only if an integer weighted linear arithmetic sum of the inputs
equals or exceeds a given integer threshold. We present a novel architecture of
a TLG that not only allows a single TLG to implement a large number of complex
logic functions, which would require multiple levels of logic when implemented
using conventional logic primitives, but also allows the selection of that
subset of functions by assignment of the transistor threshold voltages to the
input transistors. To obfuscate the functionality of the TLG, weights of some
inputs are set to zero by setting their device threshold to be a high .
The threshold voltage of the remaining transistors is set to low to
increase their transconductance. The function of a TLG is not determined by the
cell itself but rather the signals that are connected to its inputs. This makes
it possible to hide the support set of the function by essentially removing
some variable from the support set of the function by selective assignment of
high and low to the input transistors. We describe how a standard cell
library of TLGs can be mixed with conventional standard cells to realize
complex logic circuits, whose function can never be discovered by reverse
engineering. A 32-bit Wallace tree multiplier and a 28-bit 4-tap filter were
synthesized on an ST 65nm process, placed and routed, then simulated including
extracted parastics with and without obfuscation. Both obfuscated designs had
much lower area (25%) and much lower dynamic power (30%) than their
nonobfuscated CMOS counterparts, operating at the same frequency
Fuzzy Maximum Satisfiability
In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to
{\L}ukasiewicz logic. The MaxSAT problem for a set of formulae {\Phi} is the
problem of finding an assignment to the variables in {\Phi} that satisfies the
maximum number of formulae. Three possible solutions (encodings) are proposed
to the new problem: (1) Disjunctive Linear Relations (DLRs), (2) Mixed Integer
Linear Programming (MILP) and (3) Weighted Constraint Satisfaction Problem
(WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have
numerous applications in optimization problems that involve vagueness.Comment: 10 page
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