Coherent perfect absorber and laser induced by directional emissions in the non-Hermitian photonic crystals

In this study, we propose the application of non-Hermitian photonic crystals (PCs) with anisotropic emissions. Unlike a ring of exceptional points (EPs) in isotropic non-Hermitian PCs, the EPs of anisotropic non-Hermitian PCs appear as lines symmetrical about the $\Gamma$ point. The non-Hermitian Hamiltonian indicates that the formation of EPs is related to the non-Hermitian strength. The real spectrum appears in the $\Gamma$Y direction and has been validated as the complex conjugate medium (CCM) by effective medium theory (EMT). But for the $\Gamma$X direction, EMT indicates that the effective refractive index has a large imaginary part, which forms an evanescent wave inside the PCs. Thence, coherent perfect absorber (CPA) and laser effects can be achieved in the directional emission of the $\Gamma$Y. The outgoing wave in the $\Gamma$X direction is weak, which can significantly reduce the losses and electromagnetic interference caused by the leakage waves. Furthermore, the non-Hermitian PCs enable many fascinating applications such as signal amplification, collimation, and angle sensors.Comment: 11 pages, 11 figure

N-Acryloylphenyl­alanine

The title compound, C12H13NO3, was prepared by the nucleophilic substitution reaction of acryloyl chloride with glycylglycine. In the crystal structure, inter­molecular N—H⋯O, O–H⋯O and C—H⋯O hydrogen bonds link the mol­ecules into a three-dimensional network

Instruction-following Evaluation through Verbalizer Manipulation

While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challenging. Existing benchmarks primarily focus on common instructions that align well with what the model learned during training. However, proficiency in responding to these instructions does not necessarily imply strong ability in instruction following. In this paper, we propose a novel instruction-following evaluation protocol called verbalizer manipulation. It instructs the model to verbalize the task label with words aligning with model priors to different extents, adopting verbalizers from highly aligned (e.g., outputting ``postive'' for positive sentiment), to minimally aligned (e.g., outputting ``negative'' for positive sentiment). Verbalizer manipulation can be seamlessly integrated with any classification benchmark to examine the model's reliance on priors and its ability to override them to accurately follow the instructions. We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them. We observe that the instruction-following abilities of models, across different families and scales, are significantly distinguished by their performance on less natural verbalizers. Even the strongest GPT-4 model struggles to perform better than random guessing on the most challenging verbalizer, emphasizing the need for continued advancements to improve their instruction-following abilities

Virtual Prompt Injection for Instruction-Tuned Large Language Models

We present Virtual Prompt Injection (VPI) for instruction-tuned Large Language Models (LLMs). VPI allows an attacker-specified virtual prompt to steer the model behavior under specific trigger scenario without any explicit injection in model input. For instance, if an LLM is compromised with the virtual prompt "Describe Joe Biden negatively." for Joe Biden-related instructions, then any service deploying this model will propagate biased views when handling user queries related to Joe Biden. VPI is especially harmful for two primary reasons. Firstly, the attacker can take fine-grained control over LLM behaviors by defining various virtual prompts, exploiting LLMs' proficiency in following instructions. Secondly, this control is achieved without any interaction from the attacker while the model is in service, leading to persistent attack. To demonstrate the threat, we propose a simple method for performing VPI by poisoning the model's instruction tuning data. We find that our proposed method is highly effective in steering the LLM with VPI. For example, by injecting only 52 poisoned examples (0.1% of the training data size) into the instruction tuning data, the percentage of negative responses given by the trained model on Joe Biden-related queries change from 0% to 40%. We thus highlight the necessity of ensuring the integrity of the instruction-tuning data as little poisoned data can cause stealthy and persistent harm to the deployed model. We further explore the possible defenses and identify data filtering as an effective way to defend against the poisoning attacks. Our project page is available at https://poison-llm.github.io

4-(3-Carb­oxy­phen­yl)pyridinium nitrate

In the title salt, C12H10NO2 +·NO3 −, the dihedral angle between the pyridine ring and the benzene ring of the 4-(3-carb­oxy­phen­yl)pyridinium cation is 30.14 (2)°. Inversion-related pairs of cations are linked into dimers by pairs of O—H⋯O hydrogen bonds. Pairs of dimers are linked by N—H⋯O and C—H⋯O hydrogen bonds involving nitrate anions as acceptors, generating supra­molecular chains along the diagonal of the bc plane

Observation of strong anisotropic forbidden transitions in (001) InGaAs/GaAs single-quantum well by reflectance-difference spectroscopy and its behavior under uniaxial strain

The strong anisotropic forbidden transition has been observed in a series of InGaAs/GaAs single-quantum well with well width ranging between 3 nm and 7 nm at 80 K. Numerical calculations within the envelope function framework have been performed to analyze the origin of the optical anisotropic forbidden transition. It is found that the optical anisotropy of this transition can be mainly attributed to indium segregation effect. The effect of uniaxial strain on in-plane optical anisotropy (IPOA) is also investigated. The IPOA of the forbidden transition changes little with strain, while that of the allowed transition shows a linear dependence on strain

Grow and Merge: A Unified Framework for Continuous Categories Discovery

Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories. In this work, we focus on the application scenarios where unlabeled data are continuously fed into the category discovery system. We refer to it as the {\bf Continuous Category Discovery} ({\bf CCD}) problem, which is significantly more challenging than the static setting. A common challenge faced by novel category discovery is that different sets of features are needed for classification and category discovery: class discriminative features are preferred for classification, while rich and diverse features are more suitable for new category mining. This challenge becomes more severe for dynamic setting as the system is asked to deliver good performance for known classes over time, and at the same time continuously discover new classes from unlabeled data. To address this challenge, we develop a framework of {\bf Grow and Merge} ({\bf GM}) that works by alternating between a growing phase and a merging phase: in the growing phase, it increases the diversity of features through a continuous self-supervised learning for effective category mining, and in the merging phase, it merges the grown model with a static one to ensure satisfying performance for known classes. Our extensive studies verify that the proposed GM framework is significantly more effective than the state-of-the-art approaches for continuous category discovery.Comment: This paper has already been accepted by 36th Conference on Neural Information Processing Systems (NeurIPS 2022

6,10,16,19-Tetra­oxatrispiro­[4.2.2.4.2.2]nona­deca­ne

The asymmetric unit of the title compound, C15H24O4, contains one half-mol­ecule; a twofold rotation axis passes through the central C atom. The non-planar six- and five-membered rings adopt chair and envelope conformations, respectively. In the crystal structure, inter­molecular C—H⋯O hydrogen bonds link the mol­ecules