2,2-Dimethyl-5-[(3-nitro­anilino)methyl­ene]-1,3-dioxane-4,6-dione

The benzene ring of the title compound, C13H12N2O6, is twisted away from the planes of the amino­methyl­ene unit and the dioxane ring by 30.13 (4) and 35.89 (4)°, respectively. The dioxane ring exhibits a half-boat conformation, in which the C atom between the dioxane O atoms is 0.553 (8) Å out-of-plane. An intra­molecular N—H⋯O hydrogen bond stabilizes the conformation of the dioxane ring with the amino­methyl­ene group [the dihedral angle between the mean planes of the dioxane ring and the amino­methyl­ene group is 11.61 (4)°]. In the crystal, a three-dimensional framework is built via weak inter­molecular N—H⋯O and C—H⋯O inter­actions

The Superior Aspects of an Arc Downcomer Tray with Total Deflectors

A new structural tray ¾ the arc downcomer tray with total deflectors (ADTTD) was designed based on the numerical calculation of entropy generation rate. A pilot-scale setup was established to evaluate its hydrodynamics, heat transfer and mass transfer performances. The correlations for calculating the tray pressure drop and downcomer backup were derived. The measured temperature profiles of the liquid layer on the tray show that the flow pattern is nearly in an ideal mode if suitable deflectors are designed. The pressure drop of this tray decreases by approximately 50% compared with that of a conventional sieve tray in the region of intermediate to high vapor load. The liquid-phase Murphree tray efficiency of the tray is almost 30% higher than that of the traditional sieve tray under the same operating conditions. The weeping curve of the tray was also found to be a little lower than that of conventional trays. Experiments and industrial applications demonstrated that the ADTTD had some important advantages in lower pressure drop and energy-consumption, higher capacity and tray efficiency over the conventional sieve trays

On Reinforcement Learning for Full-length Game of StarCraft

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II. The hierarchy involves two levels of abstraction. One is the macro-action automatically extracted from expert's trajectories, which reduces the action space in an order of magnitude yet remains effective. The other is a two-layer hierarchical architecture which is modular and easy to scale, enabling a curriculum transferring from simpler tasks to more complex tasks. The reinforcement training algorithm for this architecture is also investigated. On a 64x64 map and using restrictive units, we achieve a winning rate of more than 99\% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93\% winning rate of Protoss against the most difficult non-cheating built-in AI (level-7) of Terran, training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong generalization performance, when tested against never seen opponents including cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We hope this study could shed some light on the future research of large-scale reinforcement learning.Comment: Appeared in AAAI 201

Nuclear superfluidity for antimagnetic rotation in 105^{105}Cd and 106^{106}Cd

The effect of nuclear superfluidity on antimagnetic rotation bands in $^{105}$Cd and $^{106}$Cd are investigated by the cranked shell model with the pairing correlations and the blocking effects treated by a particle-number conserving method. The experimental moments of inertia and the reduced $B(E2)$ transition values are excellently reproduced. The nuclear superfluidity is essential to reproduce the experimental moments of inertia. The two-shears-like mechanism for the antimagnetic rotation is investigated by examining the shears angle, i.e., the closing of the two proton hole angular momenta, and its sensitive dependence on the nuclear superfluidity is revealed.Comment: 14 pages, 4 figure

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance. Previous works formalize CEE as independent utterance pair classification problems, with emotion and speaker information neglected. From a new perspective, this paper considers CEE in a joint framework. We classify multiple utterances synchronously to capture the correlations between utterances in a global view and propose a Two-Stream Attention Model (TSAM) to effectively model the speaker's emotional influences in the conversational history. Specifically, the TSAM comprises three modules: Emotion Attention Network (EAN), Speaker Attention Network (SAN), and interaction module. The EAN and SAN incorporate emotion and speaker information in parallel, and the subsequent interaction module effectively interchanges relevant information between the EAN and SAN via a mutual BiAffine transformation. Extensive experimental results demonstrate that our model achieves new State-Of-The-Art (SOTA) performance and outperforms baselines remarkably

Rotational properties of the superheavy nucleus 256Rf and its neighboring even-even nuclei in particle-number conserving cranked shell model

The ground state band was recently observed in the superheavy nucleus 256Rf. We study the rotational properties of 256Rf and its neighboring even-even nuclei by using a cranked shell model (CSM) with the pairing correlations treated by a particle-number conserving (PNC) method in which the blocking effects are taken into account exactly. The kinematic and dynamic moments of inertia of the ground state bands in these nuclei are well reproduced by the theory. The spin of the lowest observed state in 256Rf is determined by comparing the experimental kinematic moments of inertia with the PNC-CSM calculations and agrees with previous spin assignment. The effects of the high order deformation varepsilon6 on the angular momentum alignments and dynamic moments of inertia in these nuclei are discussed.Comment: 7 pages, 6 figures; References and discussion about the cranking Nilsson model added, Fig. 3 modified and Figs. 5 and 6 added; Phys. Rev. C, in pres

Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment

Word alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing. Recent studies in this area have yielded substantial improvements by generating alignments from contextualized embeddings of the pre-trained multilingual language models. However, we find that the existing approaches capture few interactions between the input sentence pairs, which degrades the word alignment quality severely, especially for the ambiguous words in the monolingual context. To remedy this problem, we propose Cross-Align to model deep interactions between the input sentence pairs, in which the source and target sentences are encoded separately with the shared self-attention modules in the shallow layers, while cross-lingual interactions are explicitly constructed by the cross-attention modules in the upper layers. Besides, to train our model effectively, we propose a two-stage training framework, where the model is trained with a simple Translation Language Modeling (TLM) objective in the first stage and then finetuned with a self-supervised alignment objective in the second stage. Experiments show that the proposed Cross-Align achieves the state-of-the-art (SOTA) performance on four out of five language pairs.Comment: Accepted by EMNLP 202

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a model forgets previously learned information as it learns new information. As large language models (LLMs) have shown excellent performance, it is interesting to uncover whether CF exists in the continual fine-tuning of LLMs. In this study, we empirically evaluate the forgetting phenomenon in LLMs' knowledge, from the perspectives of domain knowledge, reasoning, and reading comprehension. The experiments demonstrate that catastrophic forgetting is generally observed in LLMs ranging from 1b to 7b. Furthermore, as the scale increases, the severity of forgetting also intensifies. Comparing the decoder-only model BLOOMZ with the encoder-decoder model mT0, BLOOMZ suffers less forgetting and maintains more knowledge. We also observe that LLMs can mitigate language bias (e.g. gender bias) during continual fine-tuning. Moreover, we find that ALPACA can maintain more knowledge and capacity compared with LLAMA during the continual fine-tuning, which implies that general instruction tuning can help mitigate the forgetting phenomenon of LLMs in the further fine-tuning process