Structure, Thermophysical Properties of Liquids, and their Connection with Glass Formability

Metallic glasses have drawn significant attention due to their unique properties, such as high strength, excellent elastic energy storage capacity, and versatile processability. However, why some liquids can easily form metallic glasses while others donմ is still unclear. Since metallic glasses are formed when liquids are cooled fast enough to bypass crystallization, we hope to better understand glass formation by investigating the structural evolution and thermophysical properties of the liquids as they are cooled toward the glass transition. Multiple molecular dynamics simulations suggest a crossover temperature for the dynamics near the liquidus temperature, which corresponds to the onset of cooperative structural rearrangements and may be the beginning of the glass transition. In this dissertation, a possible structural signature of this onset of cooperativity is first identified using high-energy synchrotron X-ray scattering studies and viscosity measurements on electrostatically levitated liquids. We also address the practical question of how to predict glass formation from properties of the high temperature liquids. A method to accurately predict the glass transition temperature in metallic glasses from properties of the equilibrium liquids is proposed. It uses the viscosity and the thermal expansion coefficient for the equilibrium liquid. Using the predicted glass transition temperature and a fragility parameter developed from the liquid properties, a new prediction formula is generated, which only uses the liquid properties. While the prediction formula works for most cases, in some cases, it fails. The analysis of these anomalous cases demonstrates that the structural similarity between the liquid and crystal phases plays an important role in the glass formability. This is the first demonstration of this important controlling factor for glass formability. We also used machine learning (Lasso regression and Random Forest) to predict the glass formability and identify important predictors. The identified important predictors are in good agreement with those from the empirical rules. Finally, the evolution of the Cu46Zr54 liquid structure is investigated by elastic neutron scattering (with isotopic substitution) and synchrotron X-ray scattering studies. The experimental results show that the number of Cu-Cu and Zr-Zr atom pairs increases as the temperature decreases, while the number of Cu-Zr atom pairs decreases on cooling. This result disagrees with predictions from previous molecular dynamics studies, suggesting that the potentials used in the molecular dynamics simulations should be reassessed

Dynamic Prototype Mask for Occluded Person Re-Identification

Although person re-identification has achieved an impressive improvement in recent years, the common occlusion case caused by different obstacles is still an unsettled issue in real application scenarios. Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. Nevertheless, the inevitable domain gap between the assistant model and the ReID datasets has highly increased the difficulty to obtain an effective and efficient model. To escape from the extra pre-trained networks and achieve an automatic alignment in an end-to-end trainable network, we propose a novel Dynamic Prototype Mask (DPM) based on two self-evident prior knowledge. Specifically, we first devise a Hierarchical Mask Generator which utilizes the hierarchical semantic to select the visible pattern space between the high-quality holistic prototype and the feature representation of the occluded input image. Under this condition, the occluded representation could be well aligned in a selected subspace spontaneously. Then, to enrich the feature representation of the high-quality holistic prototype and provide a more complete feature space, we introduce a Head Enrich Module to encourage different heads to aggregate different patterns representation in the whole image. Extensive experimental evaluations conducted on occluded and holistic person re-identification benchmarks demonstrate the superior performance of the DPM over the state-of-the-art methods. The code is released at https://github.com/stone96123/DPM.Comment: Accepted by ACM MM 202

Comparison of the efficacy of lamivudine and telbivudine in the treatment of chronic hepatitis B: a systematic review

<p>Abstract</p> <p>Background</p> <p>Chronic viral hepatitis B remains a global public health concern. Currently, several drugs, such as lamivudine and telbivudine, are recommended for treatment of patients with chronic hepatitis B. However, there are no conclusive results on the comparison of the efficacy of lamivudine (LAM) and telbivudine (LdT) in the treatment of chronic hepatitis B.</p> <p>Results</p> <p>To evaluate the comparison of the efficacy of LAM and LdT in the treatment of chronic hepatitis B by a systematic review and meta-analysis of clinical trials, we searched PUBMED (from 1990 to April 2010), Web of Science (from 1990 to April 2010), EMBASE (from 1990 to April 2010), CNKI (National Knowledge Infrastructure) (from 1990 to April 2010), VIP database (from 1990 to April 2010), WANFANG database (from 1990 to April 2010), the Cochrane Central Register of Controlled Trials and the Cochrane Database of Systematic Review. At the end of one-year treatment, LdT was better than LAM at the biochemical response, virological response, HBeAg loss, therapeutic response, while less than at the viral breakthrough and viral resistance, but there was no significant difference in the HBeAg seroconversion and HBsAg response. LdT was better than LAM at the HBeAg seroconversion with prolonged treatment to two years.</p> <p>Conclusions</p> <p>In summary, LdT was superior in inhibiting HBV replication and preventing drug resistance as compared to LAM for CHB patients. But LdT may cause more nonspecific adverse events and can lead to more CK elevation than LAM. It is thus recommended that the LdT could be used as an option for patients but adverse events, for example CK elevation, must be monitored.</p

Approximated Prompt Tuning for Vision-Language Pre-trained Models

Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP) models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks, which greatly exacerbates the already high computational overhead. In this paper, we revisit the principle of prompt tuning for Transformer-based VLP models and reveal that the impact of soft prompt tokens can be actually approximated via independent information diffusion steps, thereby avoiding the expensive global attention modeling and reducing the computational complexity to a large extent. Based on this finding, we propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning. To validate APT, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of downstream tasks. Meanwhile, the generalization of APT is also validated on CLIP for image classification. The experimental results not only show the superior performance gains and computation efficiency of APT against the conventional prompt tuning methods, e.g., +6.6% accuracy and -64.62% additional computation overhead on METER, but also confirm its merits over other parameter-efficient transfer learning approaches

Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment

Image Quality Assessment (IQA) with reference images have achieved great success by imitating the human vision system, in which the image quality is effectively assessed by comparing the query image with its pristine reference image. However, for the images in the wild, it is quite difficult to access accurate reference images. We argue that it is possible to learn reference knowledge under the No-Reference Image Quality Assessment (NR-IQA) setting, which is effective and efficient empirically. Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images. And then, to achieve fast convergence and avoid overfitting, we further propose an inductive bias regularization. Such a framework not only solves the congenital defects of NR-IQA but also improves the feature extraction framework, enabling it to express more abundant quality information. Surprisingly, our method utilizes less input while obtaining a more significant improvement compared to the teacher models. Extensive experiments on eight standard NR-IQA datasets demonstrate the superior performance to the state-of-the-art NR-IQA methods, i.e., achieving the PLCC values of 0.917 (vs. 0.884 in LIVEC) and 0.686 (vs. 0.661 in LIVEFB)

Determining the 59 Chemical Drugs Illegally Added in Herbal Tea by a Micro-QuEChERS-Based UPLC-MS/MS

Objective: An analysis method for simultaneous determination of 59 kinds of chemical drugs illegally added in herbal tea by micro-QuEChERS method combined with ultra performance liquid chromatography-tandem mass spectrometric (UPLC-MS/MS) was established. Method: The samples were extracted by ultrasonic extraction from acetonitrile solution for 5 min and salted out with sodium chloride (NaCl) and anhydrous sodium sulfate (Na2SO4). Supernatant was purified with C18 sorbent and separated on a ACQUITY HSS T3 column (100 mm×2.1mm, 1.8 μm) by gradient elution using a mixture of 0.01% formic acid and acetonitrile:methanol (8:2) as the mobile phase. Then the analytes were quantified by UHPLC-MS/MS in multiple reaction monitoring mode (MRM) via positive and negative electrospray ionization quantified by external standard method. Result: The coefficient of determination of the standard calibration curves for the 59 analytes were all above 0.9990. The limit of detection (LOD, S/N≥3) and the limit of quantitation (LOQ, S/N≥10) were 5.0~10.0 and 10.0~25 μg/L. At three spiked of 25.0, 50.0 and 100.0 μg/L, the average recoveries of 59 analytes were 60.3%~128.8% (n=6) with the relative standard deviations in the range of 1.0%～13.7%. Conclusion: The method developed was simple, sensitive, and had good purification effect. It could be applied for the rapid determination of 59 chemical drugs illegally added in herbal tea