RAIN: Your Language Models Can Align Themselves without Finetuning

Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research gathered human preference data and then aligned the pre-trained models using reinforcement learning or instruction tuning, the so-called finetuning step. In contrast, aligning frozen LLMs without any extra data is more appealing. This work explores the potential of the latter setting. We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting. We introduce a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation and use the evaluation results to guide backward rewind and forward generation for AI safety. Notably, RAIN operates without the need of extra data for model alignment and abstains from any training, gradient computation, or parameter updates; during the self-evaluation phase, the model receives guidance on which human preference to align with through a fixed-template prompt, eliminating the need to modify the initial prompt. Experimental results evaluated by GPT-4 and humans demonstrate the effectiveness of RAIN: on the HH dataset, RAIN improves the harmlessness rate of LLaMA 30B over vanilla inference from 82% to 97%, while maintaining the helpfulness rate. Under the leading adversarial attack llm-attacks on Vicuna 33B, RAIN establishes a new defense baseline by reducing the attack success rate from 94% to 19%

Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation

The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for RGB-Thermal (RGB-T) semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of RGB images is larger than that of thermal images, and 2) the class-wise performance of RGB images at night is not consistently higher or lower than that of thermal images. we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation. Our method enjoys three key technical parts. Firstly, as one modality (e.g., RGB) suffers from a larger domain gap than that of the other (e.g., thermal), Imaging Heterogeneity Refinement (IHR) employs an interaction branch on the basis of RGB and thermal branches to prevent cross-modal discrepancy and performance degradation. Then, Class Aware Refinement (CAR) is introduced to obtain reliable ensemble logits based on pixel-level distribution aggregation of the three branches. In addition, we also design a specific learning scheme for our TTA framework, which enables the ensemble logits and three student logits to collaboratively learn to improve the quality of predictions during the testing phase of our Night TTA. Extensive experiments show that our method achieves state-of-the-art (SoTA) performance with a 13.07% boost in mIoU

Safe Drinking Water Supply for Small & Rural Communities in NL with a Case Study of Pouch Cove

Chlorine is the most common disinfectant used in the province of Newfoundland and Labrador.However, in the presence of natural organic matter (NOM) in drinking-water sources,disinfection by-products (DBPs) are formed when chlorine is used to treat drinking water. The two largest groups of DBPs, trihalomethanes (THMs) and haloacetic acids (HAAs), are frequently studied by researchers because of their toxicity and high levels in drinking water. In 1998 Newfoundland and Labrador began monitoring THMs and HAAs and it was found that several water utilities had THMs and HAAs above the specified Canadian guidelines, mostly in small, rural drinking-water systems. Pouch Cove was selected for this study as elevated levels of THMs and HAAs were found in their drinking-water system. This study focused on the development of a simple and affordable filtration technology. A passive carbon barrier was studied in the lab to remove NOM, commonly measured as total organic carbon (TOC), before chlorination. The carbon barrier was made from extracted unburned carbon from oil fly ash (OFA), which is abundant within Canada and abroad. The passive nature of this barrier makes it easy to operate and its extremely low cost makes the system affordable for small communities. The OFA samples used for this study were obtained from the Rabigh power plant in Saudi Arabia, which currently generates about 60 tons of OFA daily and currently being disposed into landfills. Since raw OFA contains organic and inorganic impurities, study samples were cleaned and treated through one of two processes, acid leaching or NaOH modification, followed by physical activation. Activated carbon (AC) samples were then applied to reduce the TOC and UV in the Pouch Cove drinking-water samples. In this adsorption treatment, a Split Plot design was employed to investigate the effects of different factors (pH, temperature, carbon dosage, sample volume, and contact/adsorption time), as well as the interaction effects among these factors. The results indicate that pH, temperature, carbon dosage, and sample volume are significant factors in designing a filtration technology. The optimal condition for TOC and UV reduction is a low temperature and a low pH. When the temperature is over 35°C, or the pH is greater than 8, no reduction was observed. The overall TOC removal by activated OFA is relatively low; the maximum removal rate can reach 66% within 30 minutes. Compared with NaOH-modified AC, acid-leached AC is a better adsorbent to achieve TOC and UV reducti

Study on the Molecular Mechanisms of dlk1 Stimulated Lung Cancer Cell Proliferation

Background and objective The imprinted gene dlk1 has been recognized as a cancer related gene since it aberrantly expressed in a series of cancer tissues, but its role in lung cancer is still unknown. The aim of this study is to examine dlk1’s expression in non-small cell lung cancers (NSCLCs) and investigate the molecular mechanism by which dlk1 could accelerate the proliferation of the cells in lung cancer cell lines (H520). Methods The relative expression of dlk1 among 30 NSCLC specimens and their adjacent normal lung tissues were analyzed by RT-PCR. A cell model that stably expressed exogenous dlk1 was established following that the dlk1 gene was cloned into a eukaryotic expression vector and then transfected into the lung cancer cells H520. CCK8 analysis and colony forming assay were employed to investigate the effect of dlk1 on cell proliferation. The expression of CyclinB1 was detected by Western blot. Results dlk1 aberrantly expressed in 36.7% (11/30) of the tumor tissues of NSCLC compared with their adjacent cancer lung tissues. CCK8 analysis showed that overexpression of dlk1 could promote the proliferation of H520 cells (P < 0.05) and the results was further confirmed by colony forming assay. Western blot analysis found that over expression of dlk1 could up-regulate the expression of CyclinB1 (P < 0.05). Conclusion dlk1 aberrantly expressed in NSCLCs. The Overexpression of dlk1 could accelerate the proliferation of lung cancer cells H520 in vitro, probably through up-regulating the expression of cell cycle protein CyclinB1

Rethinking Memory and Communication Cost for Efficient Large Language Model Training

Recently, various distributed strategies for large language model training have been proposed. However, these methods provided limited solutions for the trade-off between memory consumption and communication cost. In this paper, we rethink the impact of memory consumption and communication costs on the training speed of large language models, and propose a memory-communication balanced strategy set Partial Redundancy Optimizer (PaRO). PaRO provides comprehensive options which reduces the amount and frequency of inter-group communication with minor memory redundancy by fine-grained sharding strategy, thereby improving the training efficiency in various training scenarios. Additionally, we propose a Hierarchical Overlapping Ring (HO-Ring) communication topology to enhance communication efficiency between nodes or across switches in large language model training. Our experiments demonstrate that PaRO significantly improves training throughput by 1.19x-2.50x compared to the SOTA method and achieves a near-linear scalability. The HO-Ring algorithm improves communication efficiency by 36.5% compared to the traditional Ring algorithm

Composting of Municipal Sludge - Riverhead Wastewater Treatment Facility

A significant amount of biosolids is generated by the Riverhead Wastewater Treatment Facility (RHWTF) every year. Although biosolids have the potential to be transformed into compost through the composting process, the usual practice is to dispose them into landfills. Composting helps stabilize the organic matter in the biosolids (Oleszczuk, 2008), and the heat generated during the thermophilic phase also kills pathogens. The organic content of the sludge will be converted into stabilized humic substances through mineralization and, hence, the volume of the sludge is significantly reduced (Gouxue et al., 2001). These composted biosolids, once applied to the soil, can accelerate plant growth, improve soil moisture retention, increase organic matter in the soil, and control erosion of the topsoil (Liang et al., 2003). Since the RHWTF-generated biosolids have a very low carbon to nitrogen (C/N) ratio (8:1), they are usually landfilled. The fly ash (FA) generated from Corner Brook Pulp and Paper (CBPP), however, has a high carbon content; its addition to biosolids could increase the C/N ratio of biosolids. Therefore, the main objective of this study is to investigate the potential application of locally available carbonenriched ash from CBPP in improving the quality of biosolids generated by RHWTF, which serves the City of St. John’s, Mount Pearl, and Paradise

Identification of Prognostic Alternative Splicing Signature in Breast Carcinoma

BackgroundIncreasing evidence indicated a close relationship between aberrant splicing variants and carcinoma, whereas comprehensive analysis of prognostic alternative splicing (AS) profiling in breast cancer (BRCA) is lacking and largely unknown.MethodsRNA-seq data and corresponding clinical information of BRCA patients were obtained and integrated from The Cancer Genome Atlas (TCGA). Then SpliceSeq software was used to assess seven AS types and calculate the Percent Spliced In (PSI) value. Univariate followed by stepwise multivariate Cox regression analyses identified survival associated AS events and constructed the AS signature, which were further sent for enrichment analysis, respectively. Besides, the splicing correlation network was constructed. Additionally, nomogram incorporating AS signature and clinicopathological characteristics was developed and its efficacy was evaluated with respect to discrimination, calibration and clinical utility.ResultsA total of 45,421 AS events were detected, among which 3071 events were found associated with overall survival (OS) after strict filtering. Parent genes of these prognostic events were involved in BRCA-related processes including NF-kappaB and HIF-1 signaling pathway. Besides, the final prognostic signature built with 20 AS events performed well with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.957 for 5 years. And gene set enrichment analysis (GSEA) also confirmed the candidate 20 AS events contributed to progression of BRCA. Moreover, the nomogram that incorporated 20-AS-event-based classifier, age, pathological stage and Her-2 status showed good calibration and moderate discrimination, with C-index of 0.883 (95% CI, 0.844–0.921). Decision curve analysis (DCA) confirmed more benefit was added to survival prediction with our nomogram, especially in 5 or 8 years with threshold probability up to 80%. Finally, splicing correlation network revealed an obvious regulatory pattern of prognostic splicing factors (SF) in BRCA.ConclusionThis study provided a systematic portrait of survival-associated AS events involved in BRCA and further presented a AS-clinicopathological nomogram, which could be conveniently used to assist the individualized prediction of long-term survival probability for BRCA patients. And a series of bioinformatic analysis provided a promising perspective for further uncovering the underlying mechanisms of AS events and validating therapeutic targets for BRCA