Magnetically tunable exciton valley coherence in monolayer WS2_2 mediated by the electron-hole exchange and exciton-phonon interactions

We develop a model, which incorporates both intra- and intervalley scatterings to master equation, to explore exciton valley coherence in monolayer WS$_2$ subjected to magnetic field. For linearly polarized (LP) excitation accompanied with an initial coherence, our determined valley dynamics manifests the coherence decay being faster than the exciton population relaxation, and agrees with experimental data by Hao et al.[Nat. Phys. 12, 677 (2016)]. Further, we reveal that magnetic field may quench the electron-hole (e-h) exchange induced pure dephasing -- a crucial decoherence source -- as a result of lifting of valley degeneracy, allowing to magnetically regulate valley coherence. In particular, at low temperatures for which the exciton-phonon (ex-ph) interaction is weak, we find that the coherence time is expected to attain ${\tau}_{\mathcal{C}}\sim 1$ ps, facilitating full control of qubits based on the valley pseudospin. For dark excitons, we demonstrate an emerging coherence even in the absence of initial coherent state, which has a long coherence time ($\sim 15$ ps) at low temperature. Our work provides an insight into tunable valley coherence and coherent valley control based on dark excitons.Comment: 7 pages, 4 figure

VIP5: Towards Multimodal Foundation Models for Recommendation

Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems (RecSys) are three prominent AI applications that have traditionally developed independently, resulting in disparate modeling and engineering methodologies. This has impeded the ability for these fields to directly benefit from each other's advancements. With the recent development of foundation models, large language models have emerged as a potential general-purpose interface for unifying different modalities and problem formulations. In light of this, we propose the development of a multimodal foundation model (MFM) considering visual, textual, and personalization modalities under the P5 recommendation paradigm, thus named VIP5 (Visual P5), to unify various modalities and recommendation tasks. This will enable the processing of multiple modalities in a shared architecture for improved recommendations. To achieve this, we introduce multimodal personalized prompts to accommodate multiple modalities under a shared format. Additionally, we propose a parameter-efficient training method for foundation models, which involves freezing the P5 backbone and fine-tuning lightweight adapters, resulting in improved recommendation performance and increased efficiency in terms of training time and memory usage. Code and data of VIP5 are available at https://github.com/jeykigung/VIP5.Comment: Accepted by EMNLP 202

Supercontinuum comb generated by soliton molecule pulse laser injecting into a nonlinear amplifying loop mirror

Funding This work is financially supported by National Natural Science Foundation of China (61805281); Natural Science Foundation of Guangdong Province, China (2019A1515010732).Peer reviewedPostprin

Efficacy of 1% fipronil dust of activated carbon against subterranean termite Coptotermes formosanus Shiraki in laboratory conditions

Toxicity and horizontal transmission of 1% fipronil dust of activated carbon were measured using the subterranean termite Coptotermes formosanus Shiraki in laboratory conditions. 1% fipronil dust of activated carbon has delayed toxicity towards C. formosanus compared with 0.5% fipronil dust of French chalk; knockdown times KT50 and KT90 were delayed by >9 and >15 h respectively. Furthermore, 1% fipronil dust of activated carbon showed excellent primary and secondary horizontal transfer levels. In primary horizontal transfer, recipient mortalities reached 100% by 24, 48 and 72 h at donor-recipient ratios of 1:1, 1:5 and 1:10, respectively. High transfer efficacies were also found if donor-recipient ratios were greatly increased: mortality reached 100% at 9 d at ratio 1:25 and >90% at 12 d at 1:50. In secondary horizontal transfer, the toxicant transmitting ability of C. formosanus was greater when the primary horizontal transfer ratio was lower, and the highest transfer efficacy was found with a donor-recipient ratio of 1:1 - recipient mortalities reached 100% at 5 d and 11 d, respectively. Application of 1% fipronil dust of activated carbon overcomes the problem that that too high a concentration kills termites before they can contaminate their nestmates, while a lower concentration may not supply a sufficient dose for effective transfer from treated to untreated termites; this preparation has delayed toxicity, dose-dependent toxicity in horizontal transfer and high efficacy to control C. formosanus

The diversification of the lynx lineage during the Plio-Pleistocene-evidence from a new small Lynx from Longdan, Gansu Province, China

Altres ajuts: CERCA Programme/Generalitat de CatalunyaA new small-sized lynx from Longdan, Gansu Province, China, Lynx hei sp. nov., is described in this study. The new species displays the characteristic Lynx generic traits, such as distinct buccal grooves in the upper canine, presence of an anterior groove in the upper canine, absence of upper premolar 2, and a moderately developed mastoid process, but it is markedly smaller than the previously described Lynx issiodorensis specimens from the same site and is also smaller overall than most living species, comparable to Lynx rufus in size. The new species has a relatively wide and deep zygomatic arch, similar to that of living Lynx lynx, Lynx pardinus and Lynx canadensis but wider than that of Lynx rufus. Our phylogenetic analyses suggest that Lynx hei falls within the crown group Lynx, being the sister to Lynx rufus or, less probably, a sister to Lynx issiodorensis + three other living species of Lynx. The Plio-Pleistocene Lynx issiodorensis is supported as the ancestor of Lynx lynx, Lynx pardinus and Lynx canadensis. Our phylogenetic study suggests that Lynx diversification over the Plio-Pleistocene was achieved initially by body size differentiation, putatively forced by intraspecific competition with other carnivorans, followed by morphological divergence

ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data

AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?

Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after his/her current action (e.g. crack eggs)? What if we also know the longer-term goal of the actor (e.g. making egg fried rice)? The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of verb and noun sequences, and it is crucial for human-machine interaction. We propose to formulate the LTA task from two perspectives: a bottom-up approach that predicts the next actions autoregressively by modeling temporal dynamics; and a top-down approach that infers the goal of the actor and plans the needed procedure to accomplish the goal. We hypothesize that large language models (LLMs), which have been pretrained on procedure text data (e.g. recipes, how-tos), have the potential to help LTA from both perspectives. It can help provide the prior knowledge on the possible next actions, and infer the goal given the observed part of a procedure, respectively. To leverage the LLMs, we propose a two-stage framework, AntGPT. It first recognizes the actions already performed in the observed videos and then asks an LLM to predict the future actions via conditioned generation, or to infer the goal and plan the whole procedure by chain-of-thought prompting. Empirical results on the Ego4D LTA v1 and v2 benchmarks, EPIC-Kitchens-55, as well as EGTEA GAZE+ demonstrate the effectiveness of our proposed approach. AntGPT achieves state-of-the-art performance on all above benchmarks, and can successfully infer the goal and thus perform goal-conditioned "counterfactual" prediction via qualitative analysis. Code and model will be released at https://brown-palm.github.io/AntGP