707 research outputs found
Isotope Shifts in Beryllium-, Boron-, Carbon-, and Nitrogen-like Ions from Relativistic Configuration Interaction Calculations
Energy levels, normal and specific mass shift parameters as well as
electronic densities at the nucleus are reported for numerous states along the
beryllium, boron, carbon, and nitrogen isoelectronic sequences. Combined with
nuclear data, these electronic parameters can be used to determine values of
level and transition isotope shifts. The calculation of the electronic
parameters is done using first-order perturbation theory with relativistic
configuration interaction wave functions that account for valence, core-valence
and core-core correlation effects as zero-order functions. Results are compared
with experimental and other theoretical values, when available.Comment: 56 pages, 1 figure, Atomic Data and Nuclear Data Tables (2014
SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities
Algorithmic complexity vulnerabilities occur when the worst-case time/space
complexity of an application is significantly higher than the respective
average case for particular user-controlled inputs. When such conditions are
met, an attacker can launch Denial-of-Service attacks against a vulnerable
application by providing inputs that trigger the worst-case behavior. Such
attacks have been known to have serious effects on production systems, take
down entire websites, or lead to bypasses of Web Application Firewalls.
Unfortunately, existing detection mechanisms for algorithmic complexity
vulnerabilities are domain-specific and often require significant manual
effort. In this paper, we design, implement, and evaluate SlowFuzz, a
domain-independent framework for automatically finding algorithmic complexity
vulnerabilities. SlowFuzz automatically finds inputs that trigger worst-case
algorithmic behavior in the tested binary. SlowFuzz uses resource-usage-guided
evolutionary search techniques to automatically find inputs that maximize
computational resource utilization for a given application.Comment: ACM CCS '17, October 30-November 3, 2017, Dallas, TX, US
Exploring Biorthonormal Transformations of Pair-Correlation Functions in Atomic Structure Variational Calculations
Multiconfiguration expansions frequently target valence correlation and
correlation between valence electrons and the outermost core electrons.
Correlation within the core is often neglected. A large orbital basis is needed
to saturate both the valence and core-valence correlation effects. This in turn
leads to huge numbers of CSFs, many of which are unimportant. To avoid the
problems inherent to the use of a single common orthonormal orbital basis for
all correlation effects in the MCHF method, we propose to optimize independent
MCHF pair-correlation functions (PCFs), bringing their own orthonormal
one-electron basis. Each PCF is generated by allowing single- and double-
excitations from a multireference (MR) function. This computational scheme has
the advantage of using targeted and optimally localized orbital sets for each
PCF. These pair-correlation functions are coupled together and with each
component of the MR space through a low dimension generalized eigenvalue
problem. Nonorthogonal orbital sets being involved, the interaction and overlap
matrices are built using biorthonormal transformation of the coupled basis sets
followed by a counter-transformation of the PCF expansions.
Applied to the ground state of beryllium, the new method gives total energies
that are lower than the ones from traditional CAS-MCHF calculations using large
orbital active sets. It is fair to say that we now have the possibility to
account for, in a balanced way, correlation deep down in the atomic core in
variational calculations
Entanglement Equivalence of -qubit Symmetric States
We study the interconversion of multipartite symmetric -qubit states under
stochastic local operations and classical communication (SLOCC). We demonstrate
that if two symmetric states can be connected with a nonsymmetric invertible
local operation (ILO), then they belong necessarily to the separable, W, or GHZ
entanglement class, establishing a practical method of discriminating subsets
of entanglement classes. Furthermore, we prove that there always exists a
symmetric ILO connecting any pair of symmetric -qubit states equivalent
under SLOCC, simplifying the requirements for experimental implementations of
local interconversion of those states.Comment: Minor correction
Learning a Static Analyzer from Data
To be practically useful, modern static analyzers must precisely model the
effect of both, statements in the programming language as well as frameworks
used by the program under analysis. While important, manually addressing these
challenges is difficult for at least two reasons: (i) the effects on the
overall analysis can be non-trivial, and (ii) as the size and complexity of
modern libraries increase, so is the number of cases the analysis must handle.
In this paper we present a new, automated approach for creating static
analyzers: instead of manually providing the various inference rules of the
analyzer, the key idea is to learn these rules from a dataset of programs. Our
method consists of two ingredients: (i) a synthesis algorithm capable of
learning a candidate analyzer from a given dataset, and (ii) a counter-example
guided learning procedure which generates new programs beyond those in the
initial dataset, critical for discovering corner cases and ensuring the learned
analysis generalizes to unseen programs.
We implemented and instantiated our approach to the task of learning
JavaScript static analysis rules for a subset of points-to analysis and for
allocation sites analysis. These are challenging yet important problems that
have received significant research attention. We show that our approach is
effective: our system automatically discovered practical and useful inference
rules for many cases that are tricky to manually identify and are missed by
state-of-the-art, manually tuned analyzers
CH in stellar atmospheres: an extensive linelist
The advent of high-resolution spectrographs and detailed stellar atmosphere
modelling has strengthened the need for accurate molecular data.
Carbon-enhanced metal-poor (CEMP) stars spectra are interesting objects with
which to study transitions from the CH molecule. We combine programs for
spectral analysis of molecules and stellar-radiative transfer codes to build an
extensive CH linelist, including predissociation broadening as well as newly
identified levels. We show examples of strong predissociation CH lines in CEMP
stars, and we stress the important role played by the CH features in the
Bond-Neff feature depressing the spectra of barium stars by as much as 0.2
magnitudes in the 3000 -- 5500 \AA\ range. Because of the extreme
thermodynamic conditions prevailing in stellar atmospheres (compared to the
laboratory), molecular transitions with high energy levels can be observed.
Stellar spectra can thus be used to constrain and improve molecular data.Comment: 33pages, 15 figures, accepted in A&A external data available at
http://www.astro.ulb.ac.be/~spectrotools
Prototyping symbolic execution engines for interpreted languages
Symbolic execution is being successfully used to automatically test statically compiled code. However, increasingly more systems and applications are written in dynamic interpreted languages like Python. Building a new symbolic execution engine is a monumental effort, and so is keeping it up-to-date as the target language evolves. Furthermore, ambiguous language specifications lead to their implementation in a symbolic execution engine potentially differing from the production interpreter in subtle ways. We address these challenges by flipping the problem and using the interpreter itself as a specification of the language semantics. We present a recipe and tool (called Chef) for turning a vanilla interpreter into a sound and complete symbolic execution engine. Chef symbolically executes the target program by symbolically executing the interpreter's binary while exploiting inferred knowledge about the program's high-level structure. Using Chef, we developed a symbolic execution engine for Python in 5 person-days and one for Lua in 3 person-days. They offer complete and faithful coverage of language features in a way that keeps up with future language versions at near-zero cost. Chef-produced engines are up to 1000 times more performant than if directly executing the interpreter symbolically without Chef
Extended Calculations of Spectroscopic Data: Energy Levels, Lifetimes and Transition rates for O-like ions from Cr XVII to Zn XXIII
Employing two state-of-the-art methods, multiconfiguration
Dirac--Hartree--Fock and second-order many-body perturbation theory, the
excitation energies and lifetimes for the lowest 200 states of the ,
, , , , , , , and configurations, and multipole (electric
dipole (E1), magnetic dipole (M1), and electric quadrupole (E2)) transition
rates, line strengths, and oscillator strengths among these states are
calculated for each O-like ion from Cr XVII to Zn XXIII. Our two data sets are
compared with the NIST and CHIANTI compiled values, and previous calculations.
The data are accurate enough for identification and deblending of new emission
lines from the sun and other astrophysical sources. The amount of data of high
accuracy is significantly increased for the states of several O-like
ions of astrophysics interest, where experimental data are very scarce
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