Non-Hermitian Topological Magnonics

Dissipation in mechanics, optics, acoustics, and electronic circuits is nowadays recognized to be not always detrimental but can be exploited to achieve non-Hermitian topological phases or properties with functionalities for potential device applications. As elementary excitations of ordered magnetic moments that exist in various magnetic materials, magnons are the information carriers in magnonic devices with low-energy consumption for reprogrammable logic, non-reciprocal communication, and non-volatile memory functionalities. Non-Hermitian topological magnonics deals with the engineering of dissipation and/or gain for non-Hermitian topological phases or properties in magnets that are not achievable in the conventional Hermitian scenario, with associated functionalities cross-fertilized with their electronic, acoustic, optic, and mechanic counterparts, such as giant enhancement of magnonic frequency combs, magnon amplification, (quantum) sensing of the magnetic field with unprecedented sensitivity, magnon accumulation, and perfect absorption of microwaves. In this review article, we address the unified approach in constructing magnonic non-Hermitian Hamiltonian, introduce the basic non-Hermitian topological physics, and provide a comprehensive overview of the recent theoretical and experimental progress towards achieving distinct non-Hermitian topological phases or properties in magnonic devices, including exceptional points, exceptional nodal phases, non-Hermitian magnonic SSH model, and non-Hermitian skin effect. We emphasize the non-Hermitian Hamiltonian approach based on the Lindbladian or self-energy of the magnonic subsystem but address the physics beyond it as well, such as the crucial quantum jump effect in the quantum regime and non-Markovian dynamics. We provide a perspective for future opportunities and challenges before concluding this article.Comment: 101 pages, 35 figure

Scalable and Effective Generative Information Retrieval

Recent research has shown that transformer networks can be used as differentiable search indexes by representing each document as a sequences of document ID tokens. These generative retrieval models cast the retrieval problem to a document ID generation problem for each given query. Despite their elegant design, existing generative retrieval models only perform well on artificially-constructed and small-scale collections. This has led to serious skepticism in the research community on their real-world impact. This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks. For doing so, we propose RIPOR- an optimization framework for generative retrieval that can be adopted by any encoder-decoder architecture. RIPOR is designed based on two often-overlooked fundamental design considerations in generative retrieval. First, given the sequential decoding nature of document ID generation, assigning accurate relevance scores to documents based on the whole document ID sequence is not sufficient. To address this issue, RIPOR introduces a novel prefix-oriented ranking optimization algorithm. Second, initial document IDs should be constructed based on relevance associations between queries and documents, instead of the syntactic and semantic information in the documents. RIPOR addresses this issue using a relevance-based document ID construction approach that quantizes relevance-based representations learned for documents. Evaluation on MSMARCO and TREC Deep Learning Track reveals that RIPOR surpasses state-of-the-art generative retrieval models by a large margin (e.g., 30.5% MRR improvements on MS MARCO Dev Set), and perform better on par with popular dense retrieval models

Terahertz characterisation of UV offset lithographically printed electronic-ink

Inkjet-printed electronics are showing promising potential in practical applications, but methods for real-time, non-contact monitoring of printing quality are lacking. This work explores Terahertz (THz) sensing as an approach for such monitoring. It is demonstrated that alterations in the localised dielectric characteristics of inkjet-printed electronics can be qualitatively distinguished using quasi-optically-based, sub-THz reflection spectroscopy. Decreased reflection coefficients caused by the sintering process are observed and quantified. Using THz near-field scanning imaging, it is shown that sintering produces a more uniform spatial distribution of permittivity in the printed carbon patterns. Images generated using THz-TDS based imaging are presented, demonstrating the combination of high resolution imaging with quantification of complex permittivities. This work, for the first time, demonstrates the feasibility of quality control in printed electronic-ink with THz sensing, and is of practical significance to the development of in-situ and non-contact commercial-quality characterisation methods for inkjet-printed electronics

AgentTuning: Enabling Generalized Agent Abilities for LLMs

Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs' agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://github.com/THUDM/AgentTuning, serving open and powerful alternatives to commercial LLMs for agent tasks.Comment: 31 page

Large Language Models are Zero Shot Hypothesis Proposers

Significant scientific discoveries have driven the progress of human civilisation. The explosion of scientific literature and data has created information barriers across disciplines that have slowed the pace of scientific discovery. Large Language Models (LLMs) hold a wealth of global and interdisciplinary knowledge that promises to break down these information barriers and foster a new wave of scientific discovery. However, the potential of LLMs for scientific discovery has not been formally explored. In this paper, we start from investigating whether LLMs can propose scientific hypotheses. To this end, we construct a dataset consist of background knowledge and hypothesis pairs from biomedical literature. The dataset is divided into training, seen, and unseen test sets based on the publication date to control visibility. We subsequently evaluate the hypothesis generation capabilities of various top-tier instructed models in zero-shot, few-shot, and fine-tuning settings, including both closed and open-source LLMs. Additionally, we introduce an LLM-based multi-agent cooperative framework with different role designs and external tools to enhance the capabilities related to generating hypotheses. We also design four metrics through a comprehensive review to evaluate the generated hypotheses for both ChatGPT-based and human evaluations. Through experiments and analyses, we arrive at the following findings: 1) LLMs surprisingly generate untrained yet validated hypotheses from testing literature. 2) Increasing uncertainty facilitates candidate generation, potentially enhancing zero-shot hypothesis generation capabilities. These findings strongly support the potential of LLMs as catalysts for new scientific discoveries and guide further exploration.Comment: Instruction Workshop @ NeurIPS 202

Optical readout of the chemical potential of two-dimensional electrons

The chemical potential u of an electron system is a fundamental property of a solid. A precise measurement of u plays a crucial role in understanding the electron interaction and quantum states of matter. However, thermodynamics measurements in micro and nanoscale samples are challenging because of the small sample volume and large background signals. Here, we report an optical readout technique for u of an arbitrary two-dimensional (2D) material. A monolayer semiconductor sensor is capacitively coupled to the sample. The sensor optical response determines a bias that fixes its chemical potential to the band edge and directly reads u of the sample. We demonstrate the technique in AB-stacked MoTe2/WSe2 moire bilayers. We obtain u with DC sensitivity about 20 ueV/sqrt(Hz), and the compressibility and interlayer electric polarization using AC readout. The results reveal a correlated insulating state at the doping density of one hole per moire unit cell, which evolves from a Mott to a charge-transfer insulator with increasing out-of-plane electric field. Furthermore, we image u and quantify the spatial inhomogeneity of the sample. Our work opens the door for high spatial and temporal resolution measurements of the thermodynamic properties of 2D quantum materials

Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

The latest biological findings observe that the traditional motionless 'lock-and-key' theory is not generally applicable because the receptor and ligand are constantly moving. Nonetheless, remarkable changes in associated atomic sites and binding pose can provide vital information in understanding the process of drug binding. Based on this mechanism, molecular dynamics (MD) simulations were invented as a useful tool for investigating the dynamic properties of a molecular system. However, the computational expenditure limits the growth and application of protein trajectory-related studies, thus hindering the possibility of supervised learning. To tackle this obstacle, we present a novel spatial-temporal pre-training method based on the modified Equivariant Graph Matching Networks (EGMN), dubbed ProtMD, which has two specially designed self-supervised learning tasks: an atom-level prompt-based denoising generative task and a conformation-level snapshot ordering task to seize the flexibility information inside MD trajectories with very fine temporal resolutions. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen, i.e., the binding affinity prediction and the ligand efficacy prediction, to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. We observe a huge improvement from current state-of-the-art methods, with a decrease of 4.3% in RMSE for the binding affinity problem and an average increase of 13.8% in AUROC and AUPRC for the ligand efficacy problem. The results demonstrate valuable insight into a strong correlation between the magnitude of conformation's motion in the 3D space (i.e., flexibility) and the strength with which the ligand binds with its receptor