(E)-Benzyl 3-(3-nitro­benzyl­idene)dithio­carbazate

In the title compound, C15H13N3O2S2, the dihedral angle between the aromatic rings is 87.8 (2)°. In the crystal, inversion dimers occur linked by pairs of N—H⋯S hydrogen bonds

(E)-4-Chloro­benzyl 3-(3-nitro­benzyl­idene)dithio­carbazate

In the title compound, C15H12ClN3O2S2, the dihedral angle between the aromatic rings is 89.71 (10)°. In the crystal, inversion dimers linked by pairs of N—H⋯S hydrogen bonds occur

4-(Diethyl­amino)­salicyl­aldehyde azine

The title compound, C22H30N4O2, has a crystallographic inversion center located at the mid-point of the N—N single bond. Apart from the four ethyl C atoms, the non-H atoms are nearly coplanar with a mean deviation of 0.0596 (2) Å. An intra­molecular O—H⋯N hydrogen bond occurs. In the crystal, weak inter­molecular C—H⋯O hydrogen bonds link the mol­ecules into layers parallel to (100)

3-(4-Acetoxy­phen­yl)-4-oxo-4H-1-benzopyran-5,7-diyl diacetate

In the title mol­ecule, C21H16O8, the dihedral angle between the ring systems is 58.5 (1)°. In the crystal, C—H⋯O inter­actions help to establish the packing

LLatrieval: LLM-Verified Retrieval for Verifiable Generation

Verifiable generation aims to let the large language model (LLM) generate text with corresponding supporting documents, which enables the user to flexibly verify the answer and makes it more trustworthy. Its evaluation not only measures the correctness of the answer, but also the answer's verifiability, i.e., how well the answer is supported by the corresponding documents. In typical, verifiable generation adopts the retrieval-read pipeline, which is divided into two stages: 1) retrieve relevant documents of the question. 2) according to the documents, generate the corresponding answer. Since the retrieved documents can supplement knowledge for the LLM to generate the answer and serve as evidence, the retrieval stage is essential for the correctness and verifiability of the answer. However, the widely used retrievers become the bottleneck of the entire pipeline and limit the overall performance. They often have fewer parameters than the large language model and have not been proven to scale well to the size of LLMs. Since the LLM passively receives the retrieval result, if the retriever does not correctly find the supporting documents, the LLM can not generate the correct and verifiable answer, which overshadows the LLM's remarkable abilities. In this paper, we propose LLatrieval (Large Language Model Verified Retrieval), where the LLM updates the retrieval result until it verifies that the retrieved documents can support answering the question. Thus, the LLM can iteratively provide feedback to retrieval and facilitate the retrieval result to sufficiently support verifiable generation. Experimental results show that our method significantly outperforms extensive baselines and achieves new state-of-the-art results

Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections

Neural implicit methods have achieved high-quality 3D object surfaces under slight specular highlights. However, high specular reflections (HSR) often appear in front of target objects when we capture them through glasses. The complex ambiguity in these scenes violates the multi-view consistency, then makes it challenging for recent methods to reconstruct target objects correctly. To remedy this issue, we present a novel surface reconstruction framework, NeuS-HSR, based on implicit neural rendering. In NeuS-HSR, the object surface is parameterized as an implicit signed distance function (SDF). To reduce the interference of HSR, we propose decomposing the rendered image into two appearances: the target object and the auxiliary plane. We design a novel auxiliary plane module by combining physical assumptions and neural networks to generate the auxiliary plane appearance. Extensive experiments on synthetic and real-world datasets demonstrate that NeuS-HSR outperforms state-of-the-art approaches for accurate and robust target surface reconstruction against HSR. Code is available at https://github.com/JiaxiongQ/NeuS-HSR.Comment: 17 pages, 20 figure

A novel pollution pattern: Highly chlorinated biphenyls retained in Black-crowned night heron (Nycticorax nycticorax) and Whiskered tern (Chlidonias hybrida) from the Yangtze River Delta

AbstractContamination of organochlorine pesticides (OCPs), polychlorinated diphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), hydroxylated polybrominated diphenyl ethers (OH-PBDEs) and their methylated counterparts (MeO-PBDEs) were determined in Black-crowned night heron (Nycticorax nycticorax) and Whiskered tern (Chlidonias hybrida) from two drinking water sources, e.g. Tianmu lake and East Tai lake in Yangtze River Delta, China. A novel PCBs contamination pattern was detected, including 11% and 6.9% highly chlorinated biphenyls (PCBs with eight to ten chlorines) in relation to total PCB concentrations in the Black-crowned night heron and Whiskered tern eggs, respectively. The predominating OCPs detected in the present study were 4,4′-DDE, with concentration range 280–650 ng g−1 lw in Black-crowned night heron and 240–480 ng g−1 lw in Whiskered tern, followed by β-HCH and Mirex. 6-MeO-BDE-90 and 6-MeO-BDE-99 are the two predominant congeners of MeO-PBDEs whereas 6-OH-BDE-47 contributes mostly to the OH-PBDEs in both species. Contamination level was considered as median or low level compared global data