DILF: Differentiable Rendering-Based Multi-View Image-Language Fusion for Zero-Shot 3D Shape Understanding

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model’s ability to fully comprehend 3D shapes and adversely impacts the text–image fusion in a shared latent space. To this end, we propose a novel approach called Differentiable rendering-based multi-view Image–Language Fusion (DILF) for zero-shot 3D shape understanding. Specifically, DILF leverages large-scale language models (LLMs) to generate textual prompts enriched with 3D semantics and designs a differentiable renderer with learnable rendering parameters to produce representative multi-view images. These rendering parameters can be iteratively updated using a text–image fusion loss, which aids in parameters’ regression, allowing the model to determine the optimal viewpoint positions for each 3D object. Then a group-view mechanism is introduced to model interdependencies across views, enabling efficient information fusion to achieve a more comprehensive 3D shape understanding. Experimental results can demonstrate that DILF outperforms state-of-the-art methods for zero-shot 3D classification while maintaining competitive performance for standard 3D classification. The code is available at https://github.com/yuzaiyang123/DILP

A Survey on Few-Shot Class-Incremental Learning

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta-learning based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective

A Survey on Few-Shot Class-Incremental Learning

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective

Solvent-vapour-assisted pathways and the role of pre-organization in solid-state transformations of coordination polymers

A family of one-dimensional coordination polymers, [Ag4(O2C(CF2)2CF3)4(phenazine)2(arene)n]·m(arene), 1 (arene = toluene or xylene), have been synthesized and crystallographically characterized. Arene guest loss invokes structural transformations to yield a pair of polymorphic coordination polymers [Ag4(O2C(CF2)2CF3)4(phenazine)2], 2a and/or 2b, with one- and two-dimensional architectures, respectively. The role of pre-organization of the polymer chains of 1 in the selectivity for formation of either polymorph is explored, and the templating effect of toluene and p-xylene over o-xylene or m-xylene in the formation of arene-containing architecture 1 is also demonstrated. The formation of arene-free phase 2b, not accessible in a phase-pure form through other means, is shown to be the sole product of loss of toluene from 1-tol·tol [Ag4(O2C(CF2)2CF3)4(phenazine)2(toluene)]·2(toluene), a phase containing toluene coordinated to Ag(I) in an unusual μ:η1,η1 manner. Solvent-vapour-assisted conversion between the polymorphic coordination polymers and solvent-vapour influence on the conversion of coordination polymers 1 to 2a and 2b is also explored. The transformations have been examined and confirmed by X-ray diffraction, NMR spectroscopy and thermal analyses, including in situ diffraction studies of some transformations

Arene Selectivity by a Flexible Coordination Polymer Host.

The coordination polymers [Ag4 (O2 CCF3 )4 (phen)3 ]⋅phen⋅arene (1⋅phen⋅arene) (phen=phenazine; arene=toluene, p-xylene or benzene) have been synthesised from the solution phase in a series of arene solvents and crystallographically characterised. By contrast, analogous syntheses from o-xylene and m-xylene as the solvent yield the solvent-free coordination polymer [Ag4 (O2 CCF3 )4 (phen)2 ] (2). Toluene, p-xylene and benzene have been successfully used in mixed-arene syntheses to template the formation of coordination polymers 1⋅phen⋅arene, which incorporate o- or m-xylene. The selectivity of 1⋅phen⋅arene for the arene guests was determined, through pairwise competition experiments, to be p-xylene>toluene≈benzene>o-xylene>m-xylene. The largest selectivity coefficient was determined as 14.2 for p-xylene:m-xylene and the smallest was 1.0 for toluene:benzene

Coordination Polymer Flexibility Leads to Polymorphism and Enables a Crystalline Solid-Vapour Reaction: A Multi-technique Mechanistic Study.

Despite an absence of conventional porosity, the 1D coordination polymer [Ag4 (O2 C(CF2 )2 CF3 )4 (TMP)3 ] (1; TMP=tetramethylpyrazine) can absorb small alcohols from the vapour phase, which insert into AgO bonds to yield coordination polymers [Ag4 (O2 C(CF2 )2 CF3 )4 (TMP)3 (ROH)2 ] (1-ROH; R=Me, Et, iPr). The reactions are reversible single-crystal-to-single-crystal transformations. Vapour-solid equilibria have been examined by gas-phase IR spectroscopy (K=5.68(9)×10(-5) (MeOH), 9.5(3)×10(-6) (EtOH), 6.14(5)×10(-5) (iPrOH) at 295 K, 1 bar). Thermal analyses (TGA, DSC) have enabled quantitative comparison of two-step reactions 1-ROH→1→2, in which 2 is the 2D coordination polymer [Ag4 (O2 C(CF2 )2 CF3 )4 (TMP)2 ] formed by loss of TMP ligands exclusively from singly-bridging sites. Four polymorphic forms of 1 (1-A(LT) , 1-A(HT) , 1-B(LT) and 1-B(HT) ; HT=high temperature, LT=low temperature) have been identified crystallographically. In situ powder X-ray diffraction (PXRD) studies of the 1-ROH→1→2 transformations indicate the role of the HT polymorphs in these reactions. The structural relationship between polymorphs, involving changes in conformation of perfluoroalkyl chains and a change in orientation of entire polymers (A versus B forms), suggests a mechanism for the observed reactions and a pathway for guest transport within the fluorous layers. Consistent with this pathway, optical microscopy and AFM studies on single crystals of 1-MeOH/1-A(HT) show that cracks parallel to the layers of interdigitated perfluoroalkyl chains develop during the MeOH release/uptake process

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Is alignment enough? Investigating the effects of state policies and professional development on science curriculum implementation

Implementation of science curriculum materials has been a fundamental challenge in science education for decades. Policy researchers have argued that alignment of standards, curriculum, and assessment are the key to supporting implementation. This paper focuses on teachers' perceptions of curricular alignment and on curriculum implementation using empirical data from a statewide systemic inquiry science reform effort targeting students from kindergarten to eighth grade. We find that the success of alignment policies depends on teachers' construal of the relationship between standards and curriculum materials and on allocation of time for planning at the school level. © 2008 Wiley Periodicals, Inc. Sci Ed 93: 656–677, 2009Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63036/1/20321_ftp.pd

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication