3,006 research outputs found
Quantum imaginary time evolution and quantum annealing meet topological sector optimization
Optimization problems are the core challenge in many fields of science and
engineering, yet general and effective methods are scarce for searching optimal
solutions. Quantum computing has been envisioned to help solve such problems,
for example, the quantum annealing (QA) method based on adiabatic evolution has
been extensively explored and successfully implemented on quantum simulators
such as D-wave's annealers and some Rydberg arrays. In this work, we
investigate topological sector optimization (TSO) problem, which attracts
particular interests in the quantum many-body physics community. We reveal that
the topology induced by frustration in the spin model is an intrinsic
obstruction for QA and other traditional methods to approach the ground state.
We demonstrate that the optimization difficulties of TSO problem are not
restricted to the gaplessness, but are also due to the topological nature which
are often ignored for the analysis of optimization problems before. To solve
TSO problems, we utilize quantum imaginary time evolution (QITE) with a
possible realization on quantum computers, which exploits the property of
quantum superposition to explore the full Hilbert space and can thus address
optimization problems of topological nature. We report the performance of
different quantum optimization algorithms on TSO problems and demonstrate that
their capability to address optimization problems are distinct even when
considering the quantum computational resources required for practical QITE
implementations
Yolk sac tumor in a patient with transverse testicular ectopia
Transverse testicular ectopia (TTE) is a rare anomaly in which both testes descend through a single inguinal canal. We report a case of yolk sac tumor in the ectopic testis of a patient with TTE. A 24-year-old man presented to our hospital with a left inguinal-mass, right cryptorchidism and elevated alpha-fetoprotein (AFP). A left herniotomy 3 years earlier demonstrated both testes in the left scrotum, one above another positionally. Four months ago, a left scrotal mass appeared and radical orchiectomy of both testes revealed testicular yolk sac tumor of the ectopic testis. An enlarging left inguinal-mass appeared 2 months ago and he was referred to our hospital. Laboratory data showed an elevation of AFP (245.5 ng/ml) and a 46 XY karyotype. He underwent bilateral retroperitoneal lymph node dissection and simultaneous left inguinal mass dissection. Histopathologic examination revealed a diagnosis of recurrent yolk sac tumor in the left inguinal mass. The retroperitoneal lymph node was not enlarged and, on histopathology, was not involved. The patient has now been followed up for 8 months without evidence of biochemical or radiological recurrence
Solving Math Word Problems with Reexamination
Math word problem (MWP) solving aims to understand the descriptive math
problem and calculate the result, for which previous efforts are mostly devoted
to upgrade different technical modules. This paper brings a different
perspective of \textit{reexamination process} during training by introducing a
pseudo-dual task to enhance the MWP solving. We propose a pseudo-dual (PseDual)
learning scheme to model such process, which is model-agnostic thus can be
adapted to any existing MWP solvers. The pseudo-dual task is specifically
defined as filling the numbers in the expression back into the original word
problem with numbers masked. To facilitate the effective joint learning of the
two tasks, we further design a scheduled fusion strategy for the number
infilling task, which smoothly switches the input from the ground-truth math
expressions to the predicted ones. Our pseudo-dual learning scheme has been
tested and proven effective when being equipped in several representative MWP
solvers through empirical studies. \textit{The codes and trained models are
available at:} \url{https://github.com/steven640pixel/PsedualMWP}.
\end{abstract}Comment: To be appeared at NeurIPS2023 Workshop on MATH-A
Non-Autoregressive Sentence Ordering
Existing sentence ordering approaches generally employ encoder-decoder
frameworks with the pointer net to recover the coherence by recurrently
predicting each sentence step-by-step. Such an autoregressive manner only
leverages unilateral dependencies during decoding and cannot fully explore the
semantic dependency between sentences for ordering. To overcome these
limitations, in this paper, we propose a novel Non-Autoregressive Ordering
Network, dubbed \textit{NAON}, which explores bilateral dependencies between
sentences and predicts the sentence for each position in parallel. We claim
that the non-autoregressive manner is not just applicable but also particularly
suitable to the sentence ordering task because of two peculiar characteristics
of the task: 1) each generation target is in deterministic length, and 2) the
sentences and positions should match exclusively. Furthermore, to address the
repetition issue of the naive non-autoregressive Transformer, we introduce an
exclusive loss to constrain the exclusiveness between positions and sentences.
To verify the effectiveness of the proposed model, we conduct extensive
experiments on several common-used datasets and the experimental results show
that our method outperforms all the autoregressive approaches and yields
competitive performance compared with the state-of-the-arts. The codes are
available at:
\url{https://github.com/steven640pixel/nonautoregressive-sentence-ordering}.Comment: Accepted at Findings of EMNLP202
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