Aligning Source Visual and Target Language Domains for Unpaired Video Captioning

Training supervised video captioning model requires coupled video-caption pairs. However, for many targeted languages, sufficient paired data are not available. To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language. To solve the task, a natural choice is to employ a two-step pipeline system: first utilizing video-to-pivot captioning model to generate captions in pivot language and then utilizing pivot-to-target translation model to translate the pivot captions to the target language. However, in such a pipeline system, 1) visual information cannot reach the translation model, generating visual irrelevant target captions; 2) the errors in the generated pivot captions will be propagated to the translation model, resulting in disfluent target captions. To address these problems, we propose the Unpaired Video Captioning with Visual Injection system (UVC-VI). UVC-VI first introduces the Visual Injection Module (VIM), which aligns source visual and target language domains to inject the source visual information into the target language domain. Meanwhile, VIM directly connects the encoder of the video-to-pivot model and the decoder of the pivot-to-target model, allowing end-to-end inference by completely skipping the generation of pivot captions. To enhance the cross-modality injection of the VIM, UVC-VI further introduces a pluggable video encoder, i.e., Multimodal Collaborative Encoder (MCE). The experiments show that UVC-VI outperforms pipeline systems and exceeds several supervised systems. Furthermore, equipping existing supervised systems with our MCE can achieve 4% and 7% relative margins on the CIDEr scores to current state-of-the-art models on the benchmark MSVD and MSR-VTT datasets, respectively.Comment: Published at IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

A Homogenization Approach for Gradient-Dominated Stochastic Optimization

Gradient dominance property is a condition weaker than strong convexity, yet it sufficiently ensures global convergence for first-order methods even in non-convex optimization. This property finds application in various machine learning domains, including matrix decomposition, linear neural networks, and policy-based reinforcement learning (RL). In this paper, we study the stochastic homogeneous second-order descent method (SHSODM) for gradient-dominated optimization with $\alpha \in [1, 2]$ based on a recently proposed homogenization approach. Theoretically, we show that SHSODM achieves a sample complexity of $O(\epsilon^{-7/(2 \alpha) +1})$ for $\alpha \in [1, 3/2)$ and $\tilde{O}(\epsilon^{-2/\alpha})$ for $\alpha \in [3/2, 2]$. We further provide a SHSODM with a variance reduction technique enjoying an improved sample complexity of $O( \epsilon ^{-( 7-3\alpha ) /( 2\alpha )})$ for $\alpha \in [1,3/2)$. Our results match the state-of-the-art sample complexity bounds for stochastic gradient-dominated optimization without \emph{cubic regularization}. Since the homogenization approach only relies on solving extremal eigenvector problems instead of Newton-type systems, our methods gain the advantage of cheaper iterations and robustness in ill-conditioned problems. Numerical experiments on several RL tasks demonstrate the efficiency of SHSODM compared to other off-the-shelf methods

A Homogeneous Second-Order Descent Method for Nonconvex Optimization

In this paper, we introduce a Homogeneous Second-Order Descent Method (HSODM) using the homogenized quadratic approximation to the original function. The merit of homogenization is that only the leftmost eigenvector of a gradient-Hessian integrated matrix is computed at each iteration. Therefore, the algorithm is a single-loop method that does not need to switch to other sophisticated algorithms and is easy to implement. We show that HSODM has a global convergence rate of $O(\epsilon^{-3/2})$ to find an $\epsilon$-approximate second-order stationary point, and has a local quadratic convergence rate under the standard assumptions. The numerical results demonstrate the advantage of the proposed method over other second-order methods.Comment: Add inexactness, significantly improve the pape

Tempol Protects Against Acetaminophen Induced Acute Hepatotoxicity by Inhibiting Oxidative Stress and Apoptosis

Acetaminophen (APAP)-induced acute hepatotoxicity is the leading cause of drug-induced acute liver failure. The aim of this study was to evaluate the effects of 4-hydroxy-2,2,6,6-tetramethylpiperidine-N-oxyl (tempol) on the protection of APAP-induced hepatotoxicity in mice. Mice were pretreated with a single dose of tempol (20 mg/kg per day) orally for 7 days. On the seventh day, mice were injected with a single dose of APAP (300 mg/kg) to induce acute hepatotoxicity. Our results showed that tempol treatment markedly improved liver functions with alleviations of histopathological damage induced by APAP. Tempol treatment upregulated levels of antioxidant proteins, including superoxide dismutase, catalase, and glutathione. Also, phosphorylation of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt) and protein expression of nuclear factor erythroid 2-related factor (Nrf 2) and heme oxygense-1 (HO-1) were all increased by tempol, which indicated tempol protected against APAP-induced hepatotoxicity via the PI3K/Akt/Nrf2 pathway. Moreover, tempol treatment decreased pro-apoptotic protein expressions (cleaved caspase-3 and Bax) and increased anti-apoptotic Bcl-2 in liver, as well as reducing apoptotic cells of TUNEL staining, which suggested apoptotic effects of tempol treatment. Overall, we found that tempol normalizes liver function in APAP-induced acute hepatotoxicity mice via activating PI3K/Akt/Nrf2 pathway, thus enhancing antioxidant response and inhibiting hepatic apoptosis

Whole-genome probe capture sequencing reveals genomic diversity and characteristics of Mycoplasma pneumoniae in Nanjing, China

Mycoplasma pneumoniae (M. pneumoniae), a slow-growing, fastidious Gram-negative bacterium and a leading cause of community-acquired pneumonia globally, remains understudied and underreported across numerous geographical areas in China despite its worldwide significance. This study employed probe capture sequencing for targeted enrichment and direct sequencing of M. pneumoniae from clinical samples, combined with comparative genomic analyses of contemporary and historical global genomes. Core genome and pan-genome revealed that the M. pneumoniae genomes were classified into two distinct clades, P1-I and P1-II, each associated with a specific sequence type (ST). Most of the genomes sequenced in this study were identified as P1-I (86.96%, 20/23), contrasting with the previously reported predominance of P1-II in the area. A limited number of single-nucleotide variations were identified in the virulence-associated genes between P1-I and P1-II, leading to amino acid substitutions. The A2063G point mutation in the 23S rRNA gene was detected in all sequenced genomes (23/23), demonstrating a 100% mutation rate. This study provides the first reported application of probe capture methodology for M. pneumoniae, highlighting the critical importance of sustained surveillance efforts to monitor the evolution and epidemiology of this pathogen

Navigating the OverKill in Large Language Models

Large language models are meticulously aligned to be both helpful and harmless. However, recent research points to a potential overkill which means models may refuse to answer benign queries. In this paper, we investigate the factors for overkill by exploring how models handle and determine the safety of queries. Our findings reveal the presence of shortcuts within models, leading to an over-attention of harmful words like 'kill' and prompts emphasizing safety will exacerbate overkill. Based on these insights, we introduce Self-Contrastive Decoding (Self-CD), a training-free and model-agnostic strategy, to alleviate this phenomenon. We first extract such over-attention by amplifying the difference in the model's output distributions when responding to system prompts that either include or omit an emphasis on safety. Then we determine the final next-token predictions by downplaying the over-attention from the model via contrastive decoding. Empirical results indicate that our method has achieved an average reduction of the refusal rate by 20\% while having almost no impact on safety

An assessment of hepatitis E virus (HEV) in US blood donors and recipients: No detectable HEV RNA in 1939 donors tested and no evidence for HEV transmission to 362 prospectively followed recipients.

BACKGROUND: Hepatitis E virus (HEV) infection has become relevant to blood transfusion practice because isolated cases of blood transmission have been reported and because HEV has been found to cause chronic infection and severe liver disease in immunocompromised patients. STUDY DESIGN AND METHODS: We tested for immunoglobulin (Ig)G and IgM antibodies to the HEV and for HEV RNA in 1939 unselected volunteer US blood donors. Subsequently, we tested the same variables in pre- and serial posttransfusion samples from 362 prospectively followed blood recipients to assess transfusion risk. RESULTS: IgG anti-HEV seroprevalence in the total 1939 donations was 18.8%: 916 of these donations were made in 2006 at which time the seroprevalence was 21.8% and the remaining 1023 donations were in 2012 when the seroprevalence had decreased to 16.0% (p \u3c 0.01). A significant (p \u3c 0.001) stepwise increase in anti-HEV seroprevalence was seen with increasing age. Eight of 1939 donations (0.4%) tested anti-HEV IgM positive; no donation was HEV RNA positive. Two recipients had an apparent anti-HEV seroconversion, but temporal relationships and linked donor testing showed that these were not transfusion-transmitted HEV infections. CONCLUSION: No transfusion-transmitted HEV infections were observed in 362 prospectively followed blood recipients despite an anti-HEV seroprevalence among donations exceeding 16%