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