138 research outputs found
Interference fringes with maximal contrast at finite coherence time
Interference fringes can result from the measurement of four-time fourth-order correlation functions of a wave field. These fringes have a statistical origin and, as a consequence, they show the greatest contrast when the coherence time of the field is finite. A simple acoustic experiment is presented in which these fringes are observed, and it is demonstrated that the contrast is maximal for partial coherence. Random telegraph phase noise is used to vary the field coherence in order to highlight the problem of interpreting this interference; for this noise, the Gaussian moment theorem may not be invoked to reduce the description of the interference to one in terms of first-order interference.M.W. Hamilto
A Forced-Choice Rating Scale for College Instructors
The purpose of this study was to construct a forced-choice rating scale for evaluating students\u27 opinions of college instructors. The scale constructed consists of 20 tetrads, or groups of 4 statements descriptive of instructors. These statements were chosen so that a pair of favorable items, both appearing to be equally favorable, and a pair of unfavorable items, both appearing to be equally unfavorable, make up each tetrad. From each tetrad the rater must choose the item most characteristic and the item least characteristic of the ratee. The reason for choosing items in this manner can be seen in Sisson\u27s statement of the basic assumptions underlying the forced-choice method, which was used in rating Army officers
A Comparison of Absolute Overlearning on the Retention of Fast and Slow Learners
Various studies on the relation of speed of learning to retention have been reported (2) (3) (4). Many such studies are subject to the criticism that the degree of learning of fast and slow learners was not equated. Gillette (1), using the method of adjusted learning , found that fast learners retained more than slow learners as measured by both recall and relearning. Her study has set the pattern for subsequent investigation. A question may be raised, however, as to whether the degree of learning is actually equated by the method of adjusted learning. The acquisition curve for the fast learner rises at a more rapid rate than for the slow learner. Hence, when a criterion of learning is established, the training trial that achieves the criterion will carry the fast learners more above the criterion than it will the slow learners. The fast group then actually has a greater response tendency than the slow group, or the degree of learning is not equal. The present paper reports a preliminary study of an investigation designed to test the above reasoning and the further deduction that the effectiveness of overlearning on retention should vary depending on the speed of learning and should be of most value for the fast learner
Active Perception using Light Curtains for Autonomous Driving
Most real-world 3D sensors such as LiDARs perform fixed scans of the entire
environment, while being decoupled from the recognition system that processes
the sensor data. In this work, we propose a method for 3D object recognition
using light curtains, a resource-efficient controllable sensor that measures
depth at user-specified locations in the environment. Crucially, we propose
using prediction uncertainty of a deep learning based 3D point cloud detector
to guide active perception. Given a neural network's uncertainty, we derive an
optimization objective to place light curtains using the principle of
maximizing information gain. Then, we develop a novel and efficient
optimization algorithm to maximize this objective by encoding the physical
constraints of the device into a constraint graph and optimizing with dynamic
programming. We show how a 3D detector can be trained to detect objects in a
scene by sequentially placing uncertainty-guided light curtains to successively
improve detection accuracy. Code and details can be found on the project
webpage: http://siddancha.github.io/projects/active-perception-light-curtains.Comment: Published at the European Conference on Computer Vision (ECCV), 202
Innovation spaces: towards a framework for understanding the role of the physical environment in innovation
No summary availabl
Quantum resource estimates for computing elliptic curve discrete logarithms
We give precise quantum resource estimates for Shor's algorithm to compute
discrete logarithms on elliptic curves over prime fields. The estimates are
derived from a simulation of a Toffoli gate network for controlled elliptic
curve point addition, implemented within the framework of the quantum computing
software tool suite LIQ. We determine circuit implementations for
reversible modular arithmetic, including modular addition, multiplication and
inversion, as well as reversible elliptic curve point addition. We conclude
that elliptic curve discrete logarithms on an elliptic curve defined over an
-bit prime field can be computed on a quantum computer with at most qubits using a quantum circuit of at most Toffoli gates. We are able to classically simulate the
Toffoli networks corresponding to the controlled elliptic curve point addition
as the core piece of Shor's algorithm for the NIST standard curves P-192,
P-224, P-256, P-384 and P-521. Our approach allows gate-level comparisons to
recent resource estimates for Shor's factoring algorithm. The results also
support estimates given earlier by Proos and Zalka and indicate that, for
current parameters at comparable classical security levels, the number of
qubits required to tackle elliptic curves is less than for attacking RSA,
suggesting that indeed ECC is an easier target than RSA.Comment: 24 pages, 2 tables, 11 figures. v2: typos fixed and reference added.
ASIACRYPT 201
Nucleic acid-based fluorescent probes and their analytical potential
It is well known that nucleic acids play an essential role in living organisms because they store and transmit genetic information and use that information to direct the synthesis of proteins. However, less is known about the ability of nucleic acids to bind specific ligands and the application of oligonucleotides as molecular probes or biosensors. Oligonucleotide probes are single-stranded nucleic acid fragments that can be tailored to have high specificity and affinity for different targets including nucleic acids, proteins, small molecules, and ions. One can divide oligonucleotide-based probes into two main categories: hybridization probes that are based on the formation of complementary base-pairs, and aptamer probes that exploit selective recognition of nonnucleic acid analytes and may be compared with immunosensors. Design and construction of hybridization and aptamer probes are similar. Typically, oligonucleotide (DNA, RNA) with predefined base sequence and length is modified by covalent attachment of reporter groups (one or more fluorophores in fluorescence-based probes). The fluorescent labels act as transducers that transform biorecognition (hybridization, ligand binding) into a fluorescence signal. Fluorescent labels have several advantages, for example high sensitivity and multiple transduction approaches (fluorescence quenching or enhancement, fluorescence anisotropy, fluorescence lifetime, fluorescence resonance energy transfer (FRET), and excimer-monomer light switching). These multiple signaling options combined with the design flexibility of the recognition element (DNA, RNA, PNA, LNA) and various labeling strategies contribute to development of numerous selective and sensitive bioassays. This review covers fundamentals of the design and engineering of oligonucleotide probes, describes typical construction approaches, and discusses examples of probes used both in hybridization studies and in aptamer-based assays
Analysis of the common genetic component of large-vessel vasculitides through a meta- Immunochip strategy
Giant cell arteritis (GCA) and Takayasu's arteritis (TAK) are major forms of large-vessel vasculitis (LVV) that share clinical features. To evaluate their genetic similarities, we analysed Immunochip genotyping data from 1,434 LVV patients and 3,814 unaffected controls. Genetic pleiotropy was also estimated. The HLA region harboured the main disease-specific associations. GCA was mostly associated with class II genes (HLA-DRB1/HLA-DQA1) whereas TAK was mostly associated with class I genes (HLA-B/MICA). Both the statistical significance and effect size of the HLA signals were considerably reduced in the cross-disease meta-analysis in comparison with the analysis of GCA and TAK separately. Consequently, no significant genetic correlation between these two diseases was observed when HLA variants were tested. Outside the HLA region, only one polymorphism located nearby the IL12B gene surpassed the study-wide significance threshold in the meta-analysis of the discovery datasets (rs755374, P?=?7.54E-07; ORGCA?=?1.19, ORTAK?=?1.50). This marker was confirmed as novel GCA risk factor using four additional cohorts (PGCA?=?5.52E-04, ORGCA?=?1.16). Taken together, our results provide evidence of strong genetic differences between GCA and TAK in the HLA. Outside this region, common susceptibility factors were suggested, especially within the IL12B locus
- …