Anharmonic Properties of the Vibrational Two-Qubit System

My thesis focuses on theoretical studies in the molecular quantum computing with vibrational qubits. We studied how the molecular vibrational properties affect the fidelity of the logic gates. Our approach is to encode states of the quantum information register into the vibrational eigenstates of a molecule and use the shaped laser pulse (femtosecond, infrared) to control the vibrational state-to-state transition. By using the Optimal Control Theory to shape a femtosecond laser and numerical propagation of laser-driven vibrational wave packets, we analyzed how the vibrational properties of a two-qubit system affect the accuracy of the quantum gates and how to effectively control the vibrational state-to-state transitions. From these studies, we found the anharmonicities are very important for the control of the vibrational state-to-state transitions and an intricate interplay between the frequencies and anharmonicities in the two-qubit system leads to the occurrence of resonances between different transitions in the vibrational manifold. Such resonances cause leakage of population into other vibrational states and hinder the control. From theoretical analysis of resonances, we formulate several criteria for selection of molecule for implementation of the vibrational two-qubit system. These criteria can help experimentalists to choose the best molecules to achieve accurate qubit transformations in the experiment

Bayesian Methods and Machine Learning for Processing Text and Image Data

Classification/clustering is an important class of unstructured data processing problems. The classification (supervised, semi-supervised and unsupervised) aims to discover the clusters and group the similar data into categories for information organization and knowledge discovery. My work focuses on using the Bayesian methods and machine learning techniques to classify the free-text and image data, and address how to overcome the limitations of the traditional methods. The Bayesian approach provides a way to allow using more variations(numerical or categorical), and estimate the probabilities instead of explicit rules, which will benefit in the ambiguous cases. The MAP(maximum a posterior) estimation is used to deal with the local maximum problems which the ML(maximum likelihood) method gives inaccurate estimates. The EM(expectation-maximization) algorithm can be applied with MAP estimation for the incomplete/missing data problems. Our proposed framework can be used in both supervised and unsupervised classification. For natural language processing(NLP), we applied the machine learning techniques for sentence/text classification. For 3D CT image segmentation, MAP EM clustering approach is proposed to auto-detect the number of objects in the 3D CT luggage image, and the prior knowledge and constraints in MAP estimation are used to avoid/improve the local maximum problems. The algorithm can automatically determine the number of classes and find the optimal parameters for each class. As a result, it can automatically detect the number of objects and produce better segmentation for each object in the image. For segmented object recognition, we applied machine learning techniques to classify each object into targets or non-targets. We have achieved the good results with 90% PD(probability of detection) and 6% PFA(probability of false alarm). For image restoration, in X-ray imaging, scatter can produce noise, artifacts, and decreased contrast. In practice, hardware such as anti-scatter grid is often used to reduce scatter. However, the remaining scatter can still be significant and additional software-based correction is desirable. Furthermore, good software solutions can potentially reduce the amount of needed anti-scatter hardware, thereby reducing cost. In this work, the scatter correction is formulated as a Bayesian MAP (maximum a posteriori) problem with a non-local prior, which leads to better textural detail preservation in scatter reduction. The efficacy of our algorithm is demonstrated through experimental and simulation results

A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation

Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks. However, the development of supervised learning methods based on EEG signals has been hindered by the high cost and significant label discrepancies to manually label large-scale EEG datasets. Self-supervised frameworks are adopted in vision and language fields to solve this issue, but the lack of EEG-specific theoretical foundations hampers their applicability across various tasks. To solve these challenges, this paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2), which integrates neurological theory to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. Sequentially, inter-view and cross-view contrastive learning pipelines in combination with various augmentation methods are applied to capture neural features from different views. By modeling prior neural knowledge based on homologous neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations. Experimental results on different downstream tasks demonstrate that our method outperforms state-of-the-art methods, highlighting the superior generalization of neural knowledge-supported EEG representations across various brain tasks.Comment: 14pages,7 figure

AnomalyGPT: Detecting Industrial Anomalies using Large Vision-Language Models

Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have demonstrated the capability of understanding images and achieved remarkable performance in various visual tasks. Despite their strong abilities in recognizing common objects due to extensive training datasets, they lack specific domain knowledge and have a weaker understanding of localized details within objects, which hinders their effectiveness in the Industrial Anomaly Detection (IAD) task. On the other hand, most existing IAD methods only provide anomaly scores and necessitate the manual setting of thresholds to distinguish between normal and abnormal samples, which restricts their practical implementation. In this paper, we explore the utilization of LVLM to address the IAD problem and propose AnomalyGPT, a novel IAD approach based on LVLM. We generate training data by simulating anomalous images and producing corresponding textual descriptions for each image. We also employ an image decoder to provide fine-grained semantic and design a prompt learner to fine-tune the LVLM using prompt embeddings. Our AnomalyGPT eliminates the need for manual threshold adjustments, thus directly assesses the presence and locations of anomalies. Additionally, AnomalyGPT supports multi-turn dialogues and exhibits impressive few-shot in-context learning capabilities. With only one normal shot, AnomalyGPT achieves the state-of-the-art performance with an accuracy of 86.1%, an image-level AUC of 94.1%, and a pixel-level AUC of 95.3% on the MVTec-AD dataset. Code is available at https://github.com/CASIA-IVA-Lab/AnomalyGPT.Comment: Project page: https://anomalygpt.github.i

Membrane Reactor Based on Hybrid Nanomaterials for Process Intensification of Catalytic Hydrogenation Reaction: an Example of Reduction of the Environmental Footprint of Chemical Synthesis from a Batch to a Continuous Flow Chemistry Process

Membrane processes represent a well matured technology for water treatment with low environmental footprints compared to other type of processes. We have now combined this technology with nanomaterials, ionic liquids (negligible vapor pressure), and poly(ionic liquids) in order to enlarge the field of applications while benefiting from the advantages of membranes. We have modified flat sheet water filtration membrane and used it as both catalytic support and reactor with the advantages to make the reaction and the separation of products in only one step. For this purpose, catalytic metallic nanoparticles of palladium (diameter of ca. 2 nm) were synthesized in a gel-poly(ionic liquid) layer grafted at the surface of polymeric filtration membranes by UV-photografting method. The so obtained catalytic membrane was successfully applied in the hydrogenation of trans-4-phenyl-3-buten-2-one in forced flow-through configuration, which gave full conversion in a few seconds (2.6 s) showing advantages over the batch reactor process (in that case, palladium nanoparticles were synthesized in the ionic liquid [MMPIM][NTf2] (1,2-dimethyl-3-propylimidazolium bis-(trifluoromethylsulfonyl)imide)). Nevertheless, the catalytic membrane used in submerged mode no more prevailed over the batch reactor. Catalytic nanoparticles remain highly active in the membrane after 12 cycles of reaction without need of recuperation. Results were compared to one obtains with a similar system in batch reactor conditions, showing high efficiency of our process in term of selectivity and reactivity, combined to an important compactness, the productivity of the catalytic hollow fiber membrane reactor and permitting to operate at larger scale with promising results in an environmental friendly way in term of energy and product (metal, solvent) consuming

Prognostic value of the neutrophil to lymphocyte ratio in lung cancer: A meta-analysis

Recently, a series of studies explored the correlation between the neutrophil to lymphocyte ratio and the prognosis of lung cancer. However, the current opinion regarding the prognostic role of the neutrophil to lymphocyte ratio in lung cancer is inconsistent. We performed a meta-analysis of published articles to investigate the prognostic value of the neutrophil to lymphocyte ratio in lung cancer. The hazard ratio (HR) and its 95% confidence interval (CI) were calculated. An elevated neutrophil to lymphocyte ratio predicted worse overall survival, with a pooled HR of 1.243 (95%CI: 1.106-1.397; Pheterogeneity=0.001) from multivariate studies and 1.867 (95%CI: 1.487-2.344; Pheterogeneity=0.047) from univariate studies. Subgroup analysis showed that a high neutrophil to lymphocyte ratio yielded worse overall survival in non-small cell lung cancer (NSCLC) (HR=1.192, 95%CI: 1.061-1.399; Pheterogeneity=0.003) as well as small cell lung cancer (SCLC) (HR=1.550, 95% CI: 1.156-2.077; Pheterogeneity=0.625) in multivariate studies. The synthesized evidence from this meta-analysis of published articles demonstrated that an elevated neutrophil to lymphocyte ratio was a predictor of poor overall survival in patients with lung cancer

Intravenous flurbiprofen axetil can increase analgesic effect in refractory cancer pain

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to investigate the analgesic effects of intravenous flurbiprofen axetil for the refractory pain in cancer patients.</p> <p>Methods</p> <p>2109 patients were screened from the department of medical oncology, the first affiliated hospital of Anhui medical university in China between October of 2007 and October of 2008. Thirty-seven cases of cancer patients who had bad effect from anaesthetic drugs were received administration of intravenous flurbiprofen axetil with dose of 50 mg/5 ml/day. The pain score was evaluated for pre- and post- treatment by Pain Faces Scale criteria, and the side effects were also observed.</p> <p>Results</p> <p>Intravenous flurbiprofen axetil increased the analgesic effects. The total effective rate was 92%. The side effects, such as abdominal pain, alimentary tract bleeding which were found in using NSAIDs or constipation, nausea, vomit, sleepiness which were found in using opioid drugs did not be found.</p> <p>Conclusion</p> <p>Intravenous flurbiprofen axetil could provide better analgesia effects and few side effects to patients with refractory cancer pain. It could also increase analgesia effects when combining with anesthetic drugs in treatment of moderate or severe pain, especially breakthrough pain, and suit to patients who can not take oral drugs for the reason of constipation and psychosomatic symptoms.</p

Réacteur membranaire catalytique appliqué à la dépollution d’effluents

Si la filtration membranaire présente de hautes performances pour la rétention de virus ou de bactéries, elle s’avère limitée pour retenir de plus petites molécules. Il peut être alors nécessaire de les dégrader pendant la filtration. Ces travaux ont pour objectif le développement d’un réacteur membranaire catalytique permettant la dépollution d’effluents contaminés. Ce procédé est aussi développé en tant que réacteur membranaire catalytique pour la production de produits à fortes valeurs ajoutées dans l’industrie de la chimie fine. Le choix des catalyseurs s’est porté sur les nanoparticules du groupe des métaux de transition (NPM). En raison de leurs grandes surfaces spécifiques, les NPM possèdent des propriétés physico-chimiques uniques, en particulier en matière de réactivité catalytique. Néanmoins, en raison de leur grande énergie de surface, les NPM ont tendance à s’agréger et perdent ainsi leurs performances. Il est alors important de maitriser leur stabilité en évitant leurs agrégations. Nous avons développé une stratégie visant à fonctionnaliser des membranes polymères afin de permettre la synthèse et le maintien des NPM sur ce support. Cette fonctionnalisation a été réalisée par polymérisation radicalaire photo-amorcée. Ce procédé nous a permis d’élaborer des membranes possédant en surface une couche de polymère plus ou moins dense. Nous avons alors pu incorporer des NPM de faible diamètre (4nm), dispersées de manière homogène, sans agrégation et maintenues dans la membrane lors du procédé de filtration. Les performances catalytiques des membranes ont été évaluées pour différentes réactions comme la réaction modèle de la réduction du p-nitrophénol en p-aminophénol en présence de borohydrure de sodium en mode contacteur membranaire traversé. Des taux de conversion proches de 100% ont pu être obtenus en un seul passage à travers la membrane pour des temps de séjour de l’ordre de la seconde à la dizaine de seconde

Flow process and heating conditions modulate the characteristics of whey protein aggregates.

Whey protein fractal aggregates reveal different texturizing properties depending on their size. This studycharacterize the effect of three process parameters (flow regime, heating residence time (RTh) and heatingtemperature) on the size and shape of aggregates formed at a semi-industrial scale using a dynamic tubular heatexchanger, and identify the mechanisms involved in their formation. The study showed that physicochemicalparameters are not the unique levers to modulate agregates properties but process parameters are also efficient.Asymetrical-Flow-Field-Flow-Fractionation was used to highlight the significant increase of aggregate sizeproduced under transient regime conditions compared to laminar and turbulent regimes. Even larger aggregateswere obtained while increasing the heating temperature from 80 to 85 °C since the unfolding aggregation ofprotein was controlled by the aggregation step. Moreover, RTh showed no effect on aggregate formation. Thisstudy paves the way to the control of aggregate properties obtained in a continuous dynamic mode

High catalytic efficiency of palladium nanoparticles immobilized in a polymer membrane containing poly(ionic liquid) in Suzuki–Miyaura cross-coupling reaction

The elaboration of a polymeric catalytic membrane containing palladium nanoparticles is presented. The membrane was prepared using a photo-grafting process with imidazolium-based ionic liquid monomers as modifying agent and microPES® as support membrane. Ionic liquid serves as a stabilizer and immobilizer for the catalytic species, i.e. palladium nanoparticles. The Suzuki–Miyaura cross-coupling reaction was carried out on the catalytic membrane in flow-through configuration. Complete conversion was achieved in 10 s through one single filtration, without formation of byproducts. The apparent reaction rate constant was three orders of magnitude greater than in a batch reactor. No catalyst leaching was detected. This membrane offers the possibility of continuous production with no need for a separation step of the catalyst from the reaction medium