IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers

Generative training has been demonstrated to be powerful for building visual-language models. However, on zero-shot discriminative benchmarks, there is still a performance gap between models trained with generative and discriminative objectives. In this paper, we aim to narrow this gap by improving the efficacy of generative training on classification tasks, without any finetuning processes or additional modules. Specifically, we focus on narrowing the gap between the generative captioner and the CLIP classifier. We begin by analysing the predictions made by the captioner and classifier and observe that the caption generation inherits the distribution bias from the language model trained with pure text modality, making it less grounded on the visual signal. To tackle this problem, we redesign the scoring objective for the captioner to alleviate the distributional bias and focus on measuring the gain of information brought by the visual inputs. We further design a generative training objective to match the evaluation objective. We name our model trained and evaluated from the novel procedures as Information Gain (IG) captioner. We pretrain the models on the public Laion-5B dataset and perform a series of discriminative evaluations. For the zero-shot classification on ImageNet, IG captioner achieves $> 18\%$ improvements over the standard captioner, achieving comparable performances with the CLIP classifier. IG captioner also demonstrated strong performance on zero-shot image-text retrieval tasks on MSCOCO and Flickr30K. We hope this paper inspires further research towards unifying generative and discriminative training procedures for visual-language models

REVO-LION: Evaluating and Refining Vision-Language Instruction Tuning Datasets

There is an emerging line of research on multimodal instruction tuning, and a line of benchmarks have been proposed for evaluating these models recently. Instead of evaluating the models directly, in this paper we try to evaluate the Vision-Language Instruction-Tuning (VLIT) datasets themselves and further seek the way of building a dataset for developing an all-powerful VLIT model, which we believe could also be of utility for establishing a grounded protocol for benchmarking VLIT models. For effective analysis of VLIT datasets that remains an open question, we propose a tune-cross-evaluation paradigm: tuning on one dataset and evaluating on the others in turn. For each single tune-evaluation experiment set, we define the Meta Quality (MQ) as the mean score measured by a series of caption metrics including BLEU, METEOR, and ROUGE-L to quantify the quality of a certain dataset or a sample. On this basis, to evaluate the comprehensiveness of a dataset, we develop the Dataset Quality (DQ) covering all tune-evaluation sets. To lay the foundation for building a comprehensive dataset and developing an all-powerful model for practical applications, we further define the Sample Quality (SQ) to quantify the all-sided quality of each sample. Extensive experiments validate the rationality of the proposed evaluation paradigm. Based on the holistic evaluation, we build a new dataset, REVO-LION (REfining VisiOn-Language InstructiOn tuNing), by collecting samples with higher SQ from each dataset. With only half of the full data, the model trained on REVO-LION can achieve performance comparable to simply adding all VLIT datasets up. In addition to developing an all-powerful model, REVO-LION also includes an evaluation set, which is expected to serve as a convenient evaluation benchmark for future research

Characterization of xylose reductase from Candida tropicalis immobilized on chitosan bead

A xylose reductase (XR) with high activity and dual coenzyme activity from Candida tropical was purified to homogeneity by Ni2+-chelating column, and immobilized on chitosan bead. Studies on free and immobilized XR systems for determination of optimum temperature, optimum pH, thermal stability, pH stability, operational stability, and kinetic parameters were carried out. Free and immobilized XR showed higher activity at 45 and 50°C, respectively. The optimum pH for free and immobilized XR were 4.5, but immobilized XR had higher activity with a broader pH range of 4.0-6.0. Thermal and pH stability of immobilized XR were higher than that of free XR. The residual activity of immobilized XR was about 40% after 7 cycles of batch operation. The Km value of free XR was 30.3 mM, and that of immobilized XR was 20.1 mM, which indicated that the affinity of xylose was increased for immobilized XR. The immobilized XR activity was stimulated by MnSO4, and inhibited by NaCl,βME,Glu. In addition, catalytic efficiency with NADH as cofactor of immobilized XR was better enhanced than free XR. It is the first report on immobilizing XR with chitosan, with a relative high activity.Key words: Xylose reductase, crosslinked chitosan bead, immobilization, catalytic property, NADH, NADPH

The effect of multiple thermal cycles on Ti-6Al-4V deposits fabricated by wire-arc directed energy deposition:Microstructure evolution, mechanical properties, and corrosion resistance

Thermal cycles have an important effect on the microstructure and properties of the components fabricated by wire-arc directed energy deposition (wire-arc DED). In this study, a Gleeble thermal-mechanical simulator was adopted to create closer-to-reality thermal cycles with the assistance of a numerical simulation model and experimental Ti-6Al-4V deposition. Step-by-step microstructure evolution, including αm, retained β, and GB α, microhardness gradual variation, and the corrosion resistance change before and after the entire thermal cycle were investigated. Therefore, combining phase orientation and high-magnification morphology, transformed and untransformed α that occurred in low- and medium-temperature thermal cycles can be distinguished. After the entire thermal cycle, αm laths coarsened from ∼1 µm to ∼1.2 µm, and the content of retained β phase became more and more. The αm formed around grain boundaries partially disappeared and was occupied by α laths from the inner grain. GB α was more continuously distributed along prior β grain boundaries due to its lower formation temperature during the subsequent thermal cycles that were occurring incomplete α→β transformation. The severe preferential orientation of α phases formed after the deposition and high-temperature thermal cycle was also alleviated through the twice low-temperature thermal cycles. Besides, the microhardness decreased from 318.78 ± 7.5 HV to 285.17 ± 5.3 HV after the high-temperature thermal cycle but eventually increased significantly to 330.5 ± 6.4 HV after experiencing the final low-temperature thermal cycle. The corrosion resistance decreased after the entire thermal cycle, indicating a performance difference between the top and bottom regions of the Ti-6Al-4 V component fabricated by wire-arc DED.</p

Double band inversion in the topological phase transition of Ge1-xSnx alloys

We use first-principles simulation and virtual crystal approximation to reveal the unique double band inversion and topological phase transition in Ge1-xSnx alloys. Wavefunction parity, spatial charge distribution and surface state spectrum analyses suggest that the band inversion in Ge1-xSnx is relayed by its first valence band. As the system evolves from Ge to {\alpha}-Sn, its conduction band moves down, and inverts with the first and the second valence bands consecutively. The first band inversion makes the system nontrivial, while the second one does not change the topological invariant of the system. Both the band inversions yield surface modes spanning the individual inverted gaps, but only the surface mode in the upper gap associates with the nontrivial nature of tensile-strained {\alpha}-Sn.Comment: 5 pages, 6 figure

Traditional Chinese Medicine for HIV-Associated Acute Herpes Zoster: A Systematic Review and Meta-Analysis of Randomized Trials

Background. Herpes zoster (HZ) is a common infection in individuals with acquired immunodeficiency syndrome (AIDS) patients. Traditional Chinese medicine (TCM) has been used widely in clinical practice for HZ, which remains not supportive of evidence. This review aimed to evaluate the effectiveness and safety of TCM in treating HIV-associated HZ. Methods. Nine electronic databases were searched for randomized controlled trials (RCTs) testing TCM in treating HIV-associated HZ. Data were extracted on citations, interventions, and outcomes, by two authors independently. For the quality evaluation, Cochrane risk-of-bias tool 2.0 was used. Meta-analyses were performed by Revman5.3 software. Effect estimation presented as risk ratio (RR) for dichotomous data and mean difference (MD) for continuous data with their 95% confidence interval (CI). Results. Twelve RCTs (n = 644) were included; the majority of them had a high or unclear risk of bias. Meta-analysis showed that pain intensity (VAS 0–5) in the Chinese herbal medicine (CHM) group was lower than it in the drugs group (MD = −0.87, 95% CI [−1.69, −0.04], two trials, n = 93). Duration of herpes-related pain (days) of patients in the combination group was shorter than those in the drugs group (MD = −9.19, 95% CI [−16.73, −1.65], n = 144). The incidence of postherpetic neuralgia (PHN) in the combination group was lower than in the drugs group (RR = 0.49, 95% CI [0.25, 0.99], n = 202). As for cure rate (complete absence of pain and herpes), two trials showed that CHM was better than drugs (RR = 1.58, 95% CI [1.13, 2.22], n = 93), five trials showed combination treatment was better than drugs (RR = 1.40, 95% CI [1.08, 1.82], n = 224). The cure rate in the acupuncture group was more than that in the drugs group (RR = 1.99, 95% CI [1.18, 3.36], n = 120). Four trials reported adverse effects and found no serious adverse events occurred. Conclusion. CHM and acupuncture demonstrate more benefits than drugs in pain relief, cure rate improvement, and incidence reduction of PHN. However, given the data limitation and TCM therapies’ diversity, the conclusions need to be verified in future trials

Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations