ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval

Diffusion models show promising generation capability for a variety of data. Despite their high generation quality, the inference for diffusion models is still time-consuming due to the numerous sampling iterations required. To accelerate the inference, we propose ReDi, a simple yet learning-free Retrieval-based Diffusion sampling framework. From a precomputed knowledge base, ReDi retrieves a trajectory similar to the partially generated trajectory at an early stage of generation, skips a large portion of intermediate steps, and continues sampling from a later step in the retrieved trajectory. We theoretically prove that the generation performance of ReDi is guaranteed. Our experiments demonstrate that ReDi improves the model inference efficiency by 2x speedup. Furthermore, ReDi is able to generalize well in zero-shot cross-domain image generation such as image stylization.Comment: ICML 202

Generative Autoencoders as Watermark Attackers: Analyses of Vulnerabilities and Threats

Invisible watermarks safeguard images' copyrights by embedding hidden messages detectable by owners. It also prevents people from misusing images, especially those generated by AI models. Malicious adversaries can violate these rights by removing the watermarks. In order to remove watermarks without damaging the visual quality, the adversary needs to erase them while retaining the essential information in the image. This is analogous to the encoding and decoding process of generative autoencoders, especially variational autoencoders (VAEs) and diffusion models. We propose a framework using generative autoencoders to remove invisible watermarks and test it using VAEs and diffusions. Our results reveal that, even without specific training, off-the-shelf Stable Diffusion effectively removes most watermarks, surpassing all current attackers. The result underscores the vulnerabilities in existing watermarking schemes and calls for more robust methods for copyright protection

In Situ Monitoring of Catalytic Molecular Transformations on Noble Metal Nanocatalysts Using Surface-Enhanced Raman Spectroscopy

Noble metal nanoparticles have long been of tremendous interest in the nanophotonics and nanocatalysis communities owing to their intriguing size- and shape-dependent plasmonic and catalytic properties. The combination of tunable plasmon resonances with superior catalytic activities on the same noble metal nanoparticle, however, has long been challenging because the research on nanoplasmonics and nanocatalysis deals with nanoparticles in two drastically different size regimes. While tunable plasmon resonances are a unique feature of metallic nanoparticles in the sub-wavelength size regime, heterogeneous catalysis requires the use of substrate-supported sub-5 nm nanoparticulate catalysts. In this mini-review article, we share with the readers several approaches we recently developed toward the realization of plasmonic-catalytic dual-functionalities on a single noble metal nanoparticle. Our approaches involve judicious tailoring of the atomic-level surface structures of sub-wavelength plasmonic nanoparticles through either kinetically controlled seed-mediated nanocrystal growth or regioselective surface etching. These structurally tailored, dual-functional nanoparticles serve as both substrates for surface-enhanced Raman spectroscopy (SERS) and free-standing nanoparticulate catalysts. Using SERS as a molecular finger-printing spectroscopic tool, we have been able to track detailed structural evolution of molecular adsorbates in real time during catalytic reactions. The quantitative insights gained from the in situ SERS measurements shed light on the detailed relationships between interfacial molecule-transforming behaviors and the atomic-level surface structures of noble metal nanocatalysts

ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers

Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems that require not only implementation but also identification of the suitable algorithm. Moreover, LLM-generated programs lack guaranteed correctness and require human verification. To address these challenges, we propose ALGO, a framework that synthesizes Algorithmic programs with LLM-Generated Oracles to guide the generation and verify their correctness. ALGO first generates a reference oracle by prompting an LLM to exhaustively enumerate all the combinations of relevant variables. This oracle is then utilized to guide an arbitrary search strategy in exploring the algorithm space and to verify the synthesized algorithms. Our study shows that the LLM-generated oracles are correct for 88% of the cases. With the oracles as verifiers, ALGO can be integrated with any existing code generation model in a model-agnostic manner to enhance its performance. Experiments show that when equipped with ALGO, we achieve an 8x better one-submission pass rate over the Codex model and a 2.6x better one-submission pass rate over CodeT, the current state-of-the-art model on CodeContests. We can also get 1.3x better pass rate over the ChatGPT Code Interpreter on unseen problems. The problem set we used for testing, the prompts we used, the verifier and solution programs, and the test cases generated by ALGO are available at https://github.com/zkx06111/ALGO.Comment: NeurIPS 202

Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions

How can large language models (LLMs) process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform well in unseen, endangered languages. On the contrary, we observe that 2000 endangered languages, though without a large corpus, have a grammar book or a dictionary. We propose LINGOLLM, a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training. Our key insight is to demonstrate linguistic knowledge of an unseen language in an LLM's prompt, including a dictionary, a grammar book, and morphologically analyzed input text. We implement LINGOLLM on top of two models, GPT-4 and Mixtral, and evaluate their performance on 5 tasks across 8 endangered or low-resource languages. Our results show that LINGOLLM elevates translation capability from GPT-4's 0 to 10.5 BLEU for 10 language directions. Our findings demonstrate the tremendous value of linguistic knowledge in the age of LLMs for endangered languages. Our data, code, and model generations can be found at https://github.com/LLiLab/llm4endangeredlang

Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study

Background and Objectives: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients. Methods and Study Design: We conducted a study of adult septic patients in Intensive Care Unit (ICU), collecting 47 indicators for 14 days. After data cleaning and preprocessing, we used stats to explore energy delivery in deceased and surviving patients. We filtered out nutrition-related features and divided the data into three metabolic phases: acute early, acute late, and rehabilitation. Models were built using data before September 2020 and validated on the rest. We then established optimal energy target models for each phase using deep learning. Results: A total of 277 patients and 3115 data were included in this study. The models indicated that the optimal energy targets in the three phases were 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy intake increased mortality rapidly in the early period of the acute phase. Insufficient energy in the late period of the acute phase significantly raised the mortality of septic patients. For the rehabilitation phase, too much or too little energy delivery both associated with high mortality. Conclusion: Our study established time-series prediction models for septic patients to optimize energy delivery in the ICU. This approach indicated the feasibility of developing nutritional tools for critically ill patients. We recommended permissive underfeeding only in the early acute phase. Later, increased energy intake may improve survival and settle energy debts caused by underfeeding

Metabolomics in the Development and Progression of Dementia: A Systematic Review

Dementia has become a major global public health challenge with a heavy economic burden. It is urgently necessary to understand dementia pathogenesis and to identify biomarkers predicting risk of dementia in the preclinical stage for prevention, monitoring, and treatment. Metabolomics provides a novel approach for the identification of biomarkers of dementia. This systematic review aimed to examine and summarize recent retrospective cohort human studies assessing circulating metabolite markers, detected using high-throughput metabolomics, in the context of disease progression to dementia, including incident mild cognitive impairment, all-cause dementia, and cognitive decline. We systematically searched the PubMed, Embase, and Cochrane databases for retrospective cohort human studies assessing associations between blood (plasma or serum) metabolomics profile and cognitive decline and risk of dementia from inception through October 15, 2018. We identified 16 studies reporting circulating metabolites and risk of dementia, and six regarding cognitive performance change. Concentrations of several blood metabolites, including lipids (higher phosphatidylcholines, sphingomyelins, and lysophophatidylcholine, and lower docosahexaenoic acid and high-density lipoprotein subfractions), amino acids (lower branched-chain amino acids, creatinine, and taurine, and higher glutamate, glutamine, and anthranilic acid), and steroids were associated with cognitive decline and the incidence or progression of dementia. Circulating metabolites appear to be associated with the risk of dementia. Metabolomics could be a promising tool in dementia biomarker discovery. However, standardization and consensus guidelines for study design and analytical techniques require future development

Nano Copper Oxide-Modified Carbon Cloth as Cathode for a Two-Chamber Microbial Fuel Cell

In this work, Cu2O nanoparticles were deposited on a carbon cloth cathode using a facile electrochemical method. The morphology of the modified cathode, which was characterized by scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) tests, showed that the porosity and specific surface area of the cathode improved with longer deposition times. X-ray photoelectron spectroscopy (XPS) and cyclic voltammetry (CV) results showed that cupric oxide and cuprous oxide coexisted on the carbon cloth, which improved the electrochemical activity of cathode. The cathode with a deposition time of 100 s showed the best performance, with a power density twice that of bare carbon cloth. Linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS) results revealed that moderate deposition of nano copper oxide on carbon cloth could dramatically reduce the charge transfer resistance, which contributed to the enhanced electrochemical performance. The mediation mechanism of copper oxide nanocatalyst was illustrated by the fact that the recycled conversion between cupric oxide and cuprous oxide accelerated the electron transfer efficiency on the cathode

Strategies of selective electroreduction of aqueous nitrate to N2 in chloride-free system: A critical review

Electroreduction of nitrate has been gaining wide attention in recent years owing to it's beneficial for converting nitrate into benign N2 from the perspective of electrocatalytic denitrification or into value-added ammonia from the perspective of electrocatalytic NH3 synthesis. By reason of the undesired formation of ammonia is dominant during electroreduction of nitrate-containing wastewater, chloride has been widely used to improve N2 selectivity. Nevertheless, selective electroreduction of nitrate to N2 gas in chloride-containing system poses several drawbacks. In this review, we focus on the key strategies for efficiently enhancing N2 selectivity of electroreduction of nitrate in chloride-free system, including optimal selection of elements, combining an active metal catalyst with another metal, manipulating the crystalline morphology and facet orientation, constructing core–shell structure catalysts, etc. Before summarizing the strategies, four possible reaction pathways of electroreduction of nitrate to N2 are discussed. Overall, this review attempts to provide practical strategies for enhancing N2 selectivity without the aid of electrochlorination and highlight directions for future research for designing appropriate electrocatalyst for final electrocatalytic denitrification