12,424 research outputs found
Binary Mixtures of Particles with Different Diffusivities Demix
The influence of size differences, shape, mass and persistent motion on phase
separation in binary mixtures has been intensively studied. Here we focus on
the exclusive role of diffusivity differences in binary mixtures of equal-sized
particles. We find an effective attraction between the less diffusive
particles, which are essentially caged in the surrounding species with the
higher diffusion constant. This effect leads to phase separation for systems
above a critical size: A single close-packed cluster made up of the less
diffusive species emerges. Experiments for testing of our predictions are
outlined.Comment: 5 figures in main text, 8 figures in Supplemental Materia
Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity
Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns – in both their selectivity and their invariance – arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions.BMBF, 01GQ1201, Lernen und Gedächtnis in balancierten Systeme
Algorithmic Programming Language Identification
Motivated by the amount of code that goes unidentified on the web, we
introduce a practical method for algorithmically identifying the programming
language of source code. Our work is based on supervised learning and
intelligent statistical features. We also explored, but abandoned, a
grammatical approach. In testing, our implementation greatly outperforms that
of an existing tool that relies on a Bayesian classifier. Code is written in
Python and available under an MIT license.Comment: 11 pages. Code:
https://github.com/simon-weber/Programming-Language-Identificatio
Study to investigate and evaluate means of optimizing the Ku-band combined radar/communication functions for the space shuttle
The Ku band radar system on the shuttle orbiter operates in both a search and a tracking mode, and its transmitter and antennas share time with the communication mode in the integrated system. The power allocation properties and the Costa subloop subcarrier tracking performance associated with the baseline digital phase shift implementation of the three channel orbiter Ku band modulator are discussed
A Topological Version of Schaefer's Dichotomy Theorem
Schaefer's dichotomy theorem [Schaefer, STOC'78] states that a boolean
constraint satisfaction problem (CSP) is polynomial-time solvable if one of six
given conditions holds for every type of constraint allowed in its instances.
Otherwise, it is NP-complete. In this paper, we analyze boolean CSPs in terms
of their topological complexity, instead of their computational complexity. We
attach a natural topological space to the set of solutions of a boolean CSP and
introduce the notion of projection-universality. We prove that a boolean CSP is
projection-universal if and only if it is categorized as NP-complete by
Schaefer's dichotomy theorem, showing that the dichotomy translates exactly
from computational to topological complexity. We show a similar dichotomy for
SAT variants and homotopy-universality.Comment: 18 pages, 1 figur
Mungo and StMungo: tools for typechecking protocols in Java
We present two tools that support static typechecking of communica- tion protocols in Java. Mungo associates Java classes with typestate specifications, which are state machines defining permitted sequences of method calls. StMungo translates a communication protocol specified in the Scribble protocol description language into a typestate specification for each role in the protocol by following the message sequence. Role implementations can be typechecked by Mungo to ensure that they satisfy their protocols, and then compiled as usual with javac. We demonstrate the Scribble, StMungo and Mungo toolchain via a typechecked POP3 client that can communicate with a real-world POP3 server
A Single-Photon Server with Just One Atom
Neutral atoms are ideal objects for the deterministic processing of quantum
information. Entanglement operations have been performed by photon exchange or
controlled collisions. Atom-photon interfaces were realized with single atoms
in free space or strongly coupled to an optical cavity. A long standing
challenge with neutral atoms, however, is to overcome the limited observation
time. Without exception, quantum effects appeared only after ensemble
averaging. Here we report on a single-photon source with one-and-only-one atom
quasi permanently coupled to a high-finesse cavity. Quasi permanent refers to
our ability to keep the atom long enough to, first, quantify the
photon-emission statistics and, second, guarantee the subsequent performance as
a single-photon server delivering up to 300,000 photons for up to 30 seconds.
This is achieved by a unique combination of single-photon generation and atom
cooling. Our scheme brings truly deterministic protocols of quantum information
science with light and matter within reach.Comment: 4 pages, 3 figure
Can Large Language Models assist in Hazard Analysis?
Large Language Models (LLMs), such as GPT-3, have demonstrated remarkable
natural language processing and generation capabilities and have been applied
to a variety tasks, such as source code generation. This paper explores the
potential of integrating LLMs in the hazard analysis for safety-critical
systems, a process which we refer to as co-hazard analysis (CoHA). In CoHA, a
human analyst interacts with an LLM via a context-aware chat session and uses
the responses to support elicitation of possible hazard causes. In this
experiment, we explore CoHA with three increasingly complex versions of a
simple system, using Open AI's ChatGPT service. The quality of ChatGPT's
responses were systematically assessed to determine the feasibility of CoHA
given the current state of LLM technology. The results suggest that LLMs may be
useful for supporting human analysts performing hazard analysis
An Empirical Framework for Matching with Imperfectly Transferable Utility
We introduce an empirical framework for models of matching with imperfectly transferable utility and unobserved heterogeneity in tastes. Our framework includes as special cases the classic fully- and non-transferable utility models, collective models, and settings with taxes on transfers, deadweight losses, and risk aversion. We characterize equilibrium and conditions for identification, and derive comparative statics
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