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Introduction. The countries using English as a foreign language have invested many efforts to improve the effectiveness of English teaching and learning for the communicative purposes. Currently, English for specific purposes (ESP) have been integrated and categorized in the programs at the tertiary level in Vietnam. The aim of the present study is to determine four major demotivating factors affecting Vietnamese tertiary students’ ESP competence to meet the requirements of their future jobs. Methods. A mixed-methods approach was employed at a higher education institution to ascertain the liability of the findings. Particularly, the study involved 357 tertiary students participating in the survey questionnaire, 14 teachers of English and 85 students took part in semi-structured interviews. The researcher-made instruments met the high range of Cronbach confidence level (α = 0.76 – 0.95). Results. The results indicate that students have a high motivation in learning ESP for their prospective careers; however, they have disappointed feelings about the teacher-related factors, infrastructure-related factors, and university/faculty-related factors according to the outcomes from the student survey questionnaires. Besides, the findings from teachers’ and student semi-structured interviews also assert that ESP language teaching and learning does not match their expectations. Discussion. It is implied that this research results might be beneficial for school administrators to formulate ESP supportive policies, for teachers to change their pedagogical methodology, and for students to develop their learner autonomy in ESP acquisition.

In this work, we study the charge transfer improvement by the combination of two semiconductors of SERS.

Laparoscopic gastrectomy for advanced gastric cancer (GC) has been applied more frequently worldwide but is still controversial for patients with serosal invasion (T4a). This study compared short- and long-term outcomes of laparoscopic distal radical gastrectomy (LDG) with open distal gastrectomy (ODG) for T4a GC.We retrospectively studied 472 patients with T4a gastric adenocarcinoma in the lower or middle third of the stomach: 231 underwent LDG and 241 underwent ODG between 2013 and 2020. Short-term outcomes included operative characteristics and complications. Long-term outcomes included overall survival (OS) and disease-free survival (DFS). Propensity score-matched (PSM) analysis was used to adjust for imbalances in baseline characteristics between groups.The PSM strategy resulted in 294 patients (147 in each group). The LDG group had a significantly longer operating time (mean: 200 vs 190 min, p = 0.001) but reduced blood loss (mean: 50 vs 100 ml, p = 0.001). The LDG group had a higher rate of any postoperative complication (23.1% vs 12.2%, p = 0.021) but most were classified as grades I-II according to Clavien-Dindo classification. Grade III-V complications were similar between groups. Five-year OS was 69% versus 60% (p = 0.109) and 5-year DFS was 58% vs 53% (p = 0.3) in LDG and ODG groups, respectively. For tumor size5 cm, LDG was better in reduction of blood loss, postoperative hospital length of stay, and OS.LDG is feasible and safe for patients with T4a GC and is comparable to ODG regarding short- and long-term outcomes. Furthermore, LDG can be a favorable option for T4a GC smaller than 5 cm.

In this paper we ask the question of whether remittance inflows may have played some role in the premature deindustrialization observed in many developing countries over the past few decades. We analyze the question using data for the Philippines, a country that has experienced both large inflows of remittances and deindustrialization. We also document a number of important facts about remittances and deindustrialization in the Philippines. We find evidence of the Dutch disease and deindustrialization associated with remittance inflows. Specifically, an increase in remittance inflows raises the price level, appreciates the home currency in real terms, and reduces the share of value added of industry in GDP of the home country in the medium run. In the short run, however, the effects of the remittance shock are somewhat more favorable: output increases and industrialization is promoted.

When an inverse problem is solved by a gradient-based optimization algorithm, the corresponding forward and adjoint problems, which are introduced to compute the gradient, can be also solved iteratively. The idea of iterating at the same time on the inverse problem unknown and on the forward and adjoint problem solutions yields to the concept of one-shot inversion methods. We are especially interested in the case where the inner iterations for the direct and adjoint problems are incomplete, that is, stopped before achieving a high accuracy on their solutions. Here, we focus on general linear inverse problems and generic fixed-point iterations for the associated forward problem. We analyze variants of the so-called multi-step one-shot methods, in particular semi-implicit schemes with a regularization parameter. We establish sufficient conditions on the descent step for convergence, by studying the eigenvalues of the block matrix of the coupled iterations. Several numerical experiments are provided to illustrate the convergence of these methods in comparison with the classical gradient descent, where the forward and adjoint problems are solved exactly by a direct solver instead. We observe that very few inner iterations are enough to guarantee good convergence of the inversion algorithm, even in the presence of noisy data.

We propose a variational approach for preparing entangled quantum states on quantum computers. The methodology involves training a unitary operation to match with a target unitary using the Fubini-Study distance as a cost function. We employ various gradient-based optimization techniques to enhance performance, including Adam and quantum natural gradient. Our investigation showcases the versatility of different ansatzes featuring a hypergraph structure, enabling the preparation of diverse entanglement target states such as GHZ, W, and absolutely maximally entangled states. Remarkably, the circuit depth scales efficiently with the number of layers and does not depend on the number of qubits. Moreover, we explore the impacts of barren plateaus, readout noise, and error mitigation techniques on the proposed approach. Through our analysis, we demonstrate the effectiveness of the variational algorithm in maximizing the efficiency of quantum state preparation, leveraging low-depth quantum circuits., 8 pages, 5 figures

Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low lighting conditions, and abstract queries. To address these challenges, we present MarineVRS, a novel and flexible video retrieval system designed explicitly for the marine domain. MarineVRS integrates state-of-the-art methods for visual and linguistic object representation to enable efficient and accurate search and analysis of vast volumes of underwater video data. In addition, unlike the conventional video retrieval system, which only permits users to index a collection of images or videos and search using a free-form natural language sentence, our retrieval system includes an additional Explainability module that outputs the segmentation masks of the objects that the input query referred to. This feature allows users to identify and isolate specific objects in the video footage, leading to more detailed analysis and understanding of their behavior and movements. Finally, with its adaptability, explainability, accuracy, and scalability, MarineVRS is a powerful tool for marine researchers and scientists to efficiently and accurately process vast amounts of data and gain deeper insights into the behavior and movements of marine species., Accepted to OCEANS 2023 Limerick. Website: https://marinevrs.hkustvgd.com/

International audience; In recent years, interest in adhesive bonding has grown as it can be considered an efficient solution in terms of strength-to-mass ratio when applied to lightweight structures or an interesting solution to complement conventional technologies. The study of bonded adhered joints is carried out using a technique based on the macro-element method (ME), the ME shape functions are modelled using Taylor expansion power and Fourier expansion series. The purpose of this work is to compare the stiffness matrix obtained first from the entire Taylor series and then with the entire Fourier series. The method has been developed so far for 1D-bar and 1D-beam kinematics frameworks.

Federated learning (FL) has emerged as a machine learning (ML) paradigm for distributed training without requiring trainers to share private data. Coevally, unmanned aerial vehicles (UAVs) become sufficiently powerful that they can contribute to training sophisticated ML models. Direct application of the conventional FL framework to a UAV swarm, however, could result in unnecessarily high communication and computation complexity. This paper aims to address this challenge by proposing a scalable and clustered FL framework for such large UAV swarms. Specifically, we develop a clustering scheme based on which the UAV-based wireless network is partitioned into different clusters coordinated by corresponding cluster-head UAVs forming a connected graph. While the cluster-head UAVs coordinate the ML model updates as in the conventional FL framework, we propose two intercluster model aggregation strategies to produce the final global model in each training round. Extensive numerical studies demonstrate the convergence and desirable trade-offs between training performance and communication efficiency of our proposed framework.

Innovation management plays an important role in enhancing the competitive position of science and technology-based enterprises, especially for enterprises with scientific and technological activities. This is the foundation and driving force for sustainable economic and social development. In Vietnam, innovation management activities of enterprises have made certain progress in recent years; however, improving the innovation management capacity of enterprises through learning from experience in the form of training (short-term training) is necessary to come up with appropriate solutions to improve capacity. The goal of this article: (i) providing a way to identify enterprises with scientific and technological activities in Vietnam; (ii) on the basis of studying the experience of China and Thailand on innovation management in enterprises, the article gives lessons for Vietnam in developing and implementing a training program (short-term training) on ​​innovation management at enterprises with science and technology activities in the context of the strong fourth industrial revolution. Keywords: Innovation management, enterprise, science and technology

In several machine learning applications, image classification is a crucial problem. Numerous classification techniques based on quantum machine learning have been presented recently. In this article, we investigate the features of the quanvolutional neural network - a hybrid quantum-classical image classification technique inspired by the convolutional neural network which has the potential to outperform current image processing techniques. We improve the training strategy and evaluate the classification tasks on traditional datasets by different topologies, sizes, and depth of filters. Finally, we propose an efficient configuration for the quanvolutional neural network, making it a promising solution for image classification problems.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (Screening for the Presence Of Tumor by Methylation And Size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (∼0.55X) of cell-free DNA. We applied SPOT-MAS to 738 nonmetastatic patients with breast, colorectal, gastric, lung and liver cancer, and 1,550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 62.3% and 73.9% for stage I and II, respectively, increasing to 88.3% for nonmetastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

International audience; Chromium coated zirconium based nuclear fuel claddings are among the most promising evolutive concepts in the field of Enhanced Accident Tolerant Fuel (EATF) developments [1-2]. The development of these new claddings has been accelerated since 2011 and the Fukushima-DAICHI nuclear accident. Among some other objectives, one of the main ideas is to develop new concepts of nuclear fuel claddings able to delay the High Temperature (HT) steam oxidation kinetics and the associated hydrogen production (explosion risk) and to improve the resultant Post-Quenching (PQ) cladding strength and ductility upon and following hypothetical accident conditions such as Loss-of-Coolant-Accident (LOCA). In this context, Cr-coated Zr based alloys, with 10-20µm thick Cr coating (PVD deposition), show promising behaviour .

The article deals with anomaly detection of Juniper router logs. Abnormal Juniper router logs include logs that are usually different from the normal operation, and they often reflect the abnormal operation of router devices. To prevent router devices from being damaged and help administrator to grasp the situation of error quickly, detecting abnormal operation soon is very important. In this work, we present a new way to get important features from log data of Juniper router devices and use machine learning method (basing on One-Class SVM model) for anomaly detection. One-Class SVM model requires some knowledge and comprehension about logs of Juniper router devices so that it can analyze, interpret, and test the knowledge ac-quired. We collect log data from a lot of real Juniper router devices and clas-sify them based on our knowledge. Before these logs are used for training and testing the One-Class SVM model, the feature extraction phase for these data was carried out. Finally, with the proposed method, the system errors of the routers were dectected quickly and accurately. This may help our com-pany to reduce the operation cost for the router systems.

Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains. This is notably the case for lidar data, for which models can exhibit large performance discrepancies due for instance to different lidar patterns or changes in acquisition conditions. This paper addresses the corresponding Unsupervised Domain Adaptation (UDA) task for semantic segmentation. To mitigate this problem, we introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data. As both domains share the same latent representation, the model is forced to accommodate discrepancies between the two sources of data. This novel strategy differs from classical minimization of statistical divergences or lidar-specific state-of-the-art domain adaptation techniques. Our experiments demonstrate that our method achieves a better performance than the current state of the art in synthetic-to-real and real-to-real scenarios., Comment: Project repository: github.com/valeoai/SALUDA

Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some uncommon conditions. In this paper, we propose the task of `Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a single general textual description of the target domain, i.e., a prompt. First, we leverage a pretrained contrastive vision-language model (CLIP) to optimize affine transformations of source features, steering them towards target text embeddings, while preserving their content and semantics. Second, we show that augmented features can be used to perform zero-shot domain adaptation for semantic segmentation. Experiments demonstrate that our method significantly outperforms CLIP-based style transfer baselines on several datasets for the downstream task at hand. Our prompt-driven approach even outperforms one-shot unsupervised domain adaptation on some datasets, and gives comparable results on others. Our code is available at https://github.com/astra-vision/PODA., Comment: Project page: https://astra-vision.github.io/PODA/

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi., MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCVi

In this study, a surface-enhanced Raman scattering (SERS) substrate based on uniform silver nanoparticles/ZnO nanorods arrays (Ag–ZnO NRs) is prepared by combining of hydrothermal synthesis and magnetron sputtering method. The Ag sputtering time and ZnO sol concentrations in seed layer preparation affect significantly on SERS enhancement. At the optimal condition, the average size of the Ag NPs is about 30 nm and uniform distribution on the surface of the ZnO NRs which are beneficial to the sensitivity of the SERS signal. ZnO NRs arrays which have the uniform size, vertical orientation, and evenly distribution also contribute to an enhancement of Raman signal. The electromagnetic hot spots originated from individual Ag particles, the nanogaps spacing between the Ag particles on the single ZnO nanorod, the gaps between the Ag particles on the adjacent ZnO nanorods, and Ag/ZnO interface are main reasons determining the sensitivity of SERS substrate. For abamectin detection, the Ag–ZnO NRs substrate has exhibited sensitivity as low concentration as 1 ppm. The vibrations peaks of abamectin molecules are identified and investigated.

This study investigates potential impact of business environment factors toward investment efficiency of foreign direct investment (FDI) enterprises with the context of Vietnamese agriculture sector. The authors combined qualitative research methods (expert interviews) to develop research and quantitative models (EFA, CFA and SEM) to test research hypotheses. The findings revealed a direct influence of business environment among a sample of 68 enterprises, including: (1) Macro environment; (2) Intra-sectorial factors and (3) Inter-sectorial factors on business performance (profit, cost, export revenue, market share). In conclusion, some implications are proposed for more appropriate mechanisms and policies to improve the efficiency of business environment and at the same time increase the attraction of FDI for the Vietnamese agriculture.

The aim of this paper is twofold. First, we show that if G is a smooth nilpotent group acting on an algebraic variety V defined over an admissible valued field k and v∈V(k), then the Zariski closed...

PurposeThis paper focuses on the evolution of and changes in French colonial architectural designs over time in terms of the use of vernacular architectural features of Vietnam.Design/methodology/approachIt first examines the underlying principles of spatial compositions behind traditional vernacular architecture, thus revealing vernacular architectural values that have survived from the past. Styles of vernacular houses differ by region; thus, their unique spatial characteristics are analyzed by selecting the typical houses in each region. The study also illuminates ecological features and technical norms tailored to the local climate and Vietnamese traditional architecture. It examines how local and regional characters of vernacular houses affect the developments and changes in the French colonial public buildings in Vietnam, forming an identity that represents an eclectic Indochina style with the adaptation of local discourses on climate.FindingsThe Vietnamese have developed unique architectural styles that are inextricably linked to their identities, cultures, climates and livelihoods – for example, vernacular houses. Although at the beginning of the colonial period, the French imported their styles, decorations and details, which were foreign to the Vietnamese, later, they perhaps realized that such direct import was no longer suitable for the natural and socio-cultural situation of the colony. Toward the end of the colonial period, it is noticeable that French colonial architects gradually started using local design principles.Research limitations/implicationsAlthough many existing buildings can be further added for the discussion, we limit a few cases, due to the length of the paper.Social implicationsThis paper proves that toward the end of the colonial period, colonial architecture in Vietnam became increasingly localized, adapting to the local environment and climate. The buildings during the period were gradually associated with Vietnamese culture and sentiment.Originality/valueAlthough few papers deal with the vernacular architecture in Vietnam with regard to local climate and regional characteristics, there are no paper that related the vernacular Vietnamese houses to influence the French colonial designs in Vietnam. Therefore the paper has value and significance.

This study aims to estimating the prevalence of type 2 diabetes and prediabetes among adult from 30 to 69 years old and assess the association of risk factor with the conditions. A total of 5244 aged 30 to 69 years old were participated in this cross-sectional study, using nationally representative sampling frame. All participants were taking blood sample to measure fasting blood glucose level and 2-hour postload oral glucose tolerance test by National Hospital of Endocrinology, Vietnam. Multinomial logistic regressions with baseline-category logit models were conducted to identify factors associated with diabetes and prediabetes among respondents. The prediabetes prevalence was in 17.9% and diabetes in 7.3%. Patients who were male (reference group vs female OR = 0.79; 95% CI: 0.64, 0.97), in the 50 to 59 years old group (OR = 1.60; 95% CI: 1.28, 2.00), have hypertension and WHR risk have higher prevalence to have prediabetes (OR = 1.31; 95% CI: 1.12, 1.53; OR = 1.37; 95% CI: 1.11, 1.70, respectively). Male patients (reference group vs female OR = 0.62; 95% CI: 0.45, 0.84), patients who were in 40 to 49; 50 to 59; 60 to 69 years old, those who were housewife (OR = 2.17; 95% CI: 1.43, 3.28; OR = 2.85; 95% CI: 1.91, 4.27; OR = 3.12; 95% CI: 2.08, 4.69; OR = 1.71; 95% CI: 1.22, 2.40, respectively). Diabetes have significant associated with participants have hypertension (OR = 1.19; 95% CI: 1.72, 2.70). The common factor directly related to prediabetes and diabetes in both genders is age. Other factors directly associated with prediabetes and diabetes include BMI, WHR, hypertension, educational level, and job.

BACKGROUND: School-based visual acuity screening activities calculate the rates of students with possible refractive errors (REs). School staff informs the results of the children’s parents and the need for an in-depth examination to assist students with RE. AIM: The study aimed to identify the prevalence of REs among secondary students and REs based school screening in some provinces in Vietnam. METHODS: There are 4838 secondary students participating in the survey. All of them were screened for visual acuity by trained school staff including teachers and nurses. Then, a sub-sample of 1404 students was randomly selected for examination by ophthalmologists. There is a set of criteria to identify cases of low vision. We compared the school staff’s visual acuity screening results with ophthalmologists’ results to identify the difference in visual screening methods. RESULTS: The proportion of students with untreated eye problems and that of students with an uncorrected refractive error are currently quite high (at, respectively, 18.5% and 24.6%), especially in urban areas in Da Nang and Hai Duong provinces. Two of three children with RE have not received the appropriate correction. The results of visual acuity screenings were conducted by school staff in the target area have a sensitivity of 60.9% and a specificity of 93.8%. CONCLUSIONS: This indicates that efforts toward detecting students with RE need investments to mitigate the current issues in secondary school. The results suggest that school staffs including teachers and nurses need to be trained with better knowledge and skills in performing screening to improve their screening performance.

Effective analysis of unusual domain specific video collections represents an important practical problem, where state-of-the-art general purpose models still face limitations. Hence, it is desirable to design benchmark datasets that challenge novel powerful models for specific domains with additional constraints. It is important to remember that domain specific data may be noisier (e.g., endoscopic or underwater videos) and often require more experienced users for effective search. In this paper, we focus on single-shot videos taken from moving cameras in underwater environments, which constitute a nontrivial challenge for research purposes. The first shard of a new Marine Video Kit dataset is presented to serve for video retrieval and other computer vision challenges. Our dataset is used in a special session during Video Browser Showdown 2023. In addition to basic meta-data statistics, we present several insights based on low-level features as well as semantic annotations of selected keyframes. The analysis also contains experiments showing limitations of respected general purpose models for retrieval. Our dataset and code are publicly available at https://hkust-vgd.github.io/marinevideokit., Camera Ready for MMM 2023, Bergen, Norway

The digital economy has become the top concern of economic scientists around the world when there are global upheavals and crises that change the nature of many economic structures and forms of economic operation. In Vietnam, the digital economy is not only formed by the practical needs of the general global context but also reflected in the government's policies or action plans. In general, the digital economy is an inevitable and growing trend and is gradually being institutionalized in the state's policy system. However, there is a need for more in-depth studies on each aspect or large-scale research programs on the digital economy in Vietnam with both positive and negative aspects now and in the future.

In this study, a surface-enhanced Raman scattering (SERS) substrate based on uniform silver nanoparticles/pyramidal silicon arrays (Ag NPs/PSi) is prepared by combining of wet etching processes and magnetron sputtering method. The average size of the Ag NPs is about 30–50 nm and uniform distribution on the surface of the PSi which are beneficial to the sensitivity and stability of the SERS signal. For R6G probe, a significant enhancement has been obtained even for the lowest concentration of 10−10 M. For abamectin detection, the Ag NPs/PSi substrate has exhibited sensitivity as low concentration as 1 ppm. The vibration peaks of abamectin molecules are firstly identified and investigated.

Genetic diversity plays an important role in diversity conservation at multiple levels and supports to monitor and assess genetic variation. In plants, genetic diversity provides the ability to adapt and respond to environmental conditions that helps plants to survive through changing environments. Genetic diversity analyses based on molecular genetic markers are effective tools for conservation and reintroduction of rare and endangered species. In recent years, the development of various chemical and molecular techniques for studying genetic diversity has received great attention. While biochemical markers are primarily used in the diagnosis of pathogens, DNA markers have been developed and widely applied for identification of species and population based on the genotype of an organism that is more stable and not easily affected by the environmental factors. PCR-based molecular marker tools, such as restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNA (RAPD), simple sequence repeats (SSRs) are used for analysing the difference in the targeted DNA sequences. With the rapid and robust development of genomic sequencing technology it is now possible to obtain and analyse DNA sequences of the whole genome of studied organisms. However, each type of DNA markers has different principles, as well as the pros and cons of specificity. In this article, we review methods and point out DNA markers, which are considered as reliable and widely used tools for the detection of genetic variation. In addition, we present the application of DNA marker in analysing genetic diversity of wild, domestic and medicinal plants, as well as some perspectives on the future of DNA marker’s application in the analysis of genetic diversity.

Most studies on the effect of the role of institutional quality on the relationship between foreign investment and economic growth have been carried out in Western countries. Very few studies on the above-mentioned relationships have been done in Asian countries during. This paper will be conducted in Asian countries using the following three models: Pooled OLS, Fixed effect model, and Random effect model. This paper uses secondary data from 10 Asian countries from 2011 to 2018. The empirical results show that (1): FDI has a positive effect on the economies of the countries. Asia between 2002 and 2018 (2) The quality of the state strengthens the impact of FDI on the economies of Asian countries between 2011 and 2018. These findings imply that if improving the quality of institutions, the state will attract more FDI and economic development The research paper is based on the scientific approach of quantitative methods to solve the problems posed, practical and effective service for the completion of the research purpose. The secondary data collected from the worldbank.org to create asymmetric data tables will be processed on STATA software.

During the past few years, semi-supervised clustering has emerged as a new interesting direction in machine learning research. In a semi-supervised clustering algorithm, the clustering results can be significantly improved by using side information, which is available or collected from users. There are two main kinds of side information that can be learned in semi-supervised clustering algorithms: the class labels - called seeds or the pairwise constraints. The first semi-supervised clustering was introduced in 2000, and since that, many algorithms have been presented in literature. However, it is not easy to use both types of side information in the same algorithm. To address the problem, this paper proposes a semi-supervised graph based clustering algorithm that tries to use seeds and constraints in the clustering process, called MCSSGC. Moreover, we introduces a simple but efficient active learning method to collect the constraints that can boost the performance of MCSSGC, named KMMFFQS. In order to verify effectiveness of the proposed algorithm, we conducted a series of experiments not only on real data sets from UCI, but also on a document data set applied in an Information Extraction of Vietnamese documents. These obtained results show that the proposed algorithm can significantly improve the clustering process compared to some recent algorithms.

National audience; Nous présentons en premier lieu des travaux visant au développement d’horloges atomiques microondes à microcellule de haute stabilité, basées sur le phénomène de piégeage cohérent de population (CPT). Nous démontrons l’implémentation de séquences d’interrogation impulsionnelles avancées permettant une réduction drastique des effets de déplacement lumineux, combinés à la poursuite d’efforts technologiques pour l’aboutissement d’une technologie de microcellule avancée. En second lieu, nous démontrons des références de fréquence optiques, lasers stabilisés sur microcellule par spectroscopie sub-Doppler bi-fréquence, présentant une stabilité relative de fréquence court terme de 1.5 10-12 à 1 s, 100 fois supérieure à celle des micro-horloges atomiques commerciales actuelles.

In this work we are interested in general linear inverse problems where the corresponding forward problem is solved iteratively using fixed point methods. Then one-shot methods, which iterate at the same time on the forward problem solution and on the inverse problem unknown, can be applied. We analyze two variants of the so-called multi-step one-shot methods and establish sufficient conditions on the descent step for their convergence, by studying the eigenvalues of the block matrix of the coupled iterations. Several numerical experiments are provided to illustrate the convergence of these methods in comparison with the classical usual and shifted gradient descent. In particular, we observe that very few inner iterations on the forward problem are enough to guarantee good convergence of the inversion algorithm.; Dans ce travail nous nous intéressons à des problèmes inverses linéaires généraux où le problème direct correspondant est résolu de façon itérative en utilisant des méthodes de point fixe. Ainsi, les méthodes de type one-shot, qui itèrent en même temps sur la solution du problème direct et l’inconnue du problème inverse, peuvent être appliquées. Nous considérons deux variantes des méthodes multi-étapes de type one-shot et nous établissons des conditions suffisantes et nécessaires sur le pas de descente pour leur convergence, en étudiant les valeurs propres de la matrice par blocs des itérations couplées. Plusieurs tests numériques sont présentés pour illustrer la convergence de ces méthodes par rapport aux méthodes de descente de gradient usuelle et décentrée. En particulier, nous observons que très peu d’itérations internes pour le problème direct sont suffisantes pour garantir une bonne convergence de l’algorithme d’inversion.

Universal compilation is a training process that compiles a trainable unitary into a target unitary. It has vast potential applications from depth-circuit compressing to device benchmarking and quantum error mitigation. Here we propose a universal compilation algorithm for quantum state tomography in low-depth quantum circuits. We apply the Fubini-Study distance as a trainable cost function and employ various gradient-based optimizations. We evaluate the performance of various trainable unitary topologies and the trainability of different optimizers for getting high efficiency and reveal the crucial role of the circuit depth in robust fidelity. The results are comparable with the shadow tomography method, a similar fashion in the field. Our work expresses the adequate capability of the universal compilation algorithm to maximize the efficiency in the quantum state tomography. Further, it promises applications in quantum metrology and sensing and is applicable in the near-term quantum computers for various quantum computing tasks.

Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional scope of UDA with a single source domain and a single target domain, real-world perception systems face a variety of scenarios to handle, from varying lighting conditions to many cities around the world. In this context, UDAs with several domains increase the challenges with the addition of distribution shifts within the different target domains. This work focuses on a novel framework for learning UDA, continuous UDA, in which models operate on multiple target domains discovered sequentially, without access to previous target domains. We propose MuHDi, for Multi-Head Distillation, a method that solves the catastrophic forgetting problem, inherent in continual learning tasks. MuHDi performs distillation at multiple levels from the previous model as well as an auxiliary target-specialist segmentation head. We report both extensive ablation and experiments on challenging multi-target UDA semantic segmentation benchmarks to validate the proposed learning scheme and architecture., Comment: Published at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022 Workshop on Continual Learning

PurposeThe purposes of this paper are twofold. First, it analyses the income structure in cooperative financial institutions and examines how traditional and non-traditional incomes are related. Second, it evaluates whether increasing diversification towards non-traditional incomes facilitates or hampers the benefits of financial cooperative owners.Design/methodology/approachData are collected from over 3,100 US credit unions over the period of 1994–2016. A number of modern econometric techniques are employed throughout the analysis, including the use of panel fixed effect, generalised method of moments (GMM) and two-stage least square (2SLS) methodologies.FindingsUsing US credit unions as the empirical setting, the empirical results reveal that the expansion of traditional income leads to a corresponding increase in income from non-traditional activities. However, an increasing reliance on non-traditional income causes a significant drop in interest margins. The authors also find that the extent to which income diversification affects owner benefit varies across credit union types and period of time. While income diversification negatively affects owners' benefits in single common bond credit unions, it has no significant influence on multiple common bond and community credit union owners' benefits. Third, diversification can be beneficial during crisis time, but can be detrimental to owner benefit during normal time.Originality/valueThis paper provides some of the first empirical investigations on the diversification strategy of cooperative financial institutions. Therefore, the results offer significant policy implications for policymakers and market participants on whether financial cooperatives should diversify or specialise.

This paper presents information on the gross alpha and gross beta activity concentrations of two hundred twenty-six groundwater samples collected by gas flow proportional counters in southern Vietnam. The gross alpha results in the water samples ranged from 0.024 to 0.748 Bq L−1 with a mean of 0.183 ± 0.034 Bq L−1, and the gross beta results in the water samples ranged from 0.027–0.632 Bq L−1 with a mean of 0.152 ± 0.015 Bq L−1. The values obtained in this work were compared with those previously published for various regions or countries. Next, untreated and treated groundwater samples were analyzed to assess their influences on the treatment process. The results showed that there were differences in the minimum detection concentrations and the mean activity values between the untreated and treated groundwater samples (The p-value of the mean comparison tests is significant with p 0.6). This means that among the radionuclides, the major sources of beta radiation are uranium and thorium decay series radionuclides. Finally, the annual effective dose for adults (>17 years) was calculated based on the assumption that major radionuclides have the highest effective dose conversion factors. In general, the results for Pb-210, Ra-226, and Ra-228 were observed to be lower than the recommended reference values established by the World Health Organization and the International Atomic Energy Agency, except for the value of Po-210.

Object reconstruction from 3D point clouds has achieved impressive progress in the computer vision and computer graphics research field. However, reconstruction from time-varying point clouds (a.k.a. 4D point clouds) is generally overlooked. In this paper, we propose a new network architecture, namely RFNet-4D, that jointly reconstruct objects and their motion flows from 4D point clouds. The key insight is that simultaneously performing both tasks via learning spatial and temporal features from a sequence of point clouds can leverage individual tasks, leading to improved overall performance. To prove this ability, we design a temporal vector field learning module using unsupervised learning approach for flow estimation, leveraged by supervised learning of spatial structures for object reconstruction. Extensive experiments and analyses on benchmark dataset validated the effectiveness and efficiency of our method. As shown in experimental results, our method achieves state-of-the-art performance on both flow estimation and object reconstruction while performing much faster than existing methods in both training and inference. Our code and data are available at https://github.com/hkust-vgd/RFNet-4D, ECCV 2022

Classroom management incorporates academic achievement, as well as social, emotional, collaborative, and characteristic development, which requires teachers to make good intellectual work possible. This study was aimed at investigating differences between novice and experienced teachers��� perceptions of classroom management in terms of person, instruction, and discipline dimensions. The descriptive, quantitative research was conducted at a higher education institution as a case study, which involved 175 teachers basing on Slovin���s formula when selecting the population. The results indicate that there is no difference when compared the respondents��� age with the classroom management styles. However, novice and experienced teachers do not have their perceptions in common in the ways they manage their classes. The findings would supplement some implications for adjusting the teacher training programs in that novice teachers should spend more time on teaching practicum, internship, apprentice, and classroom observation to gather more hand-on experience from experienced teachers., Academia, No 26 (2022)

Laparoscopic proximal gastrectomy (LPG) has been a standard surgery for early gastric cancer in the upper third of the stomach and large esophagogastric junction gastrointestinal stromal tumor. However, how to reconstruct the stomach after LPG is still debated. This study aimed to evaluate the results of LPG with double-flap reconstruction.A retrospective study was performed with 14 patients undergoing LPG with double-flap reconstruction for early gastric cancer or large tumors in the upper third of the stomach from 2018 to 2021. We evaluated postoperative complications, gastroesophageal reflux and the gastric remnant's function using endoscopy in accordance with the Los Angeles and Residue-Gastritis-Bile classifications, and patients' quality of life by the Gastrointestinal Symptom Rating Scale (GSRS) questionnaire.Median age was 54 years and 10 patients were male. There were 7 patients with gastrointestinal stromal tumor, 4 with leiomyoma and 3 with early-stage adenocarcinoma. No patient had major complications or required conversion to open surgery. During a median follow-up period of 24.6 months, 1 patient had late anastomotic stricture, 2 had metastasis, and 1 died. Endoscopic evaluation at 6 and 12 months showed good function of the gastric remnant in most patients. Patients' quality of life improved over time: mean GSRS score was 26.9±12.6, 20.3±7.2, and 18.8±4.2 at 6, 12, and 24 months, respectively.LPG with double-flap reconstruction is feasible and safe for early gastric cancer or large tumors in the upper third of the stomach. The long-term functional outcomes and patients' quality of life were acceptable.

This study aims to predict Melasma based on users' data combined with medical practice data community by dermatologists to predict the disease and make some necessary recommendations in the patient screening. This study also helps reduce treatment costs and supports remote patient treatment. In this study, we built a machine learning model to assist dermatologists in predicting a person's risk of Melasma after entering his/her community information. People can use this model through an application to track their risk of Melasma. Combining input community data with the expertise of Melasma specialists, we built a dataset with relevant information to predict Melasma. Based on this dataset, we have statistically described the data characteristics as well as the correlated data parameters that may cause Melasma, then we use the XGBoost algorithm to build a machine learning model to predict whether a person is infected to Melasma or not. The obtained results are going to be applied to assist in predicting whether a person may have Melasma with the input of community information combined with medical practice knowledge about the disease. From this result, it is possible to continue researching and applying artificial intelligence to support diagnosis and treatment of Melasma. Key words: XGBoost algorithm, Melasma disease, machine learning, Melasma prediction.