500 research outputs found
Countermodel Construction via Optimal Hypersequent Calculi for Non-normal Modal Logics
International audienceWe develop semantically-oriented calculi for the cube of non-normal modal logics and some deontic extensions. The calculi manipulate hypersequents and have a simple semantic interpretation. Their main feature is that they allow for direct countermodel extraction. Moreover they provide an optimal decision procedure for the respective logics. They also enjoy standard proof-theoretical properties, such as a syntactical proof of cut-admissibility
Functional Liftings of Vectorial Variational Problems with Laplacian Regularization
We propose a functional lifting-based convex relaxation of variational
problems with Laplacian-based second-order regularization. The approach rests
on ideas from the calibration method as well as from sublabel-accurate
continuous multilabeling approaches, and makes these approaches amenable for
variational problems with vectorial data and higher-order regularization, as is
common in image processing applications. We motivate the approach in the
function space setting and prove that, in the special case of absolute
Laplacian regularization, it encompasses the discretization-first
sublabel-accurate continuous multilabeling approach as a special case. We
present a mathematical connection between the lifted and original functional
and discuss possible interpretations of minimizers in the lifted function
space. Finally, we exemplarily apply the proposed approach to 2D image
registration problems.Comment: 12 pages, 3 figures; accepted at the conference "Scale Space and
Variational Methods" in Hofgeismar, Germany 201
Syntactic Cut-Elimination for Intuitionistic Fuzzy Logic via Linear Nested Sequents
This paper employs the linear nested sequent framework to design a new
cut-free calculus LNIF for intuitionistic fuzzy logic--the first-order G\"odel
logic characterized by linear relational frames with constant domains. Linear
nested sequents--which are nested sequents restricted to linear
structures--prove to be a well-suited proof-theoretic formalism for
intuitionistic fuzzy logic. We show that the calculus LNIF possesses highly
desirable proof-theoretic properties such as invertibility of all rules,
admissibility of structural rules, and syntactic cut-elimination.Comment: Appended version of the paper "Syntactic Cut-Elimination for
Intuitionistic Fuzzy Logic via Linear Nested Sequents", accepted to the
International Symposium on Logical Foundations of Computer Science (LFCS
2020
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
International audienceSzeliski et al. published an influential study in 2006 on energy minimization methods for Markov Random Fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically , the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different car-dinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2,453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types
Search for the decay
We search for radiative decays into a weakly interacting neutral
particle, namely an invisible particle, using the produced through the
process in a data sample of
decays collected by the BESIII detector
at BEPCII. No significant signal is observed. Using a modified frequentist
method, upper limits on the branching fractions are set under different
assumptions of invisible particle masses up to 1.2 . The upper limit corresponding to an invisible particle with zero mass
is 7.0 at the 90\% confidence level
Precise Measurements of Branching Fractions for Meson Decays to Two Pseudoscalar Mesons
We measure the branching fractions for seven two-body decays to
pseudo-scalar mesons, by analyzing data collected at
GeV with the BESIII detector at the BEPCII collider. The branching fractions
are determined to be ,
,
,
,
,
,
,
where the first uncertainties are statistical, the second are systematic, and
the third are from external input branching fraction of the normalization mode
. Precision of our measurements is significantly improved
compared with that of the current world average values
Automatically Selecting Inference Algorithms for Discrete Energy Minimisation
Minimisation of discrete energies defined over factors is an important
problem in computer vision, and a vast number of MAP inference algorithms have
been proposed. Different inference algorithms perform better on factor graph
models (GMs) from different underlying problem classes, and in general it is
difficult to know which algorithm will yield the lowest energy for a given GM.
To mitigate this difficulty, survey papers advise the practitioner on what
algorithms perform well on what classes of models. We take the next step
forward, and present a technique to automatically select the best inference
algorithm for an input GM. We validate our method experimentally on an extended
version of the OpenGM2 benchmark, containing a diverse set of vision problems.
On average, our method selects an inference algorithm yielding labellings with
96% of variables the same as the best available algorithm
- …