LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset

As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/anonymous1113243/LeCaRDv2

An Intent Taxonomy of Legal Case Retrieval

Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' information needs in legal case retrieval could be significantly different from those in Web search and traditional ad-hoc retrieval tasks. While there are several studies that retrieve legal cases based on text similarity, the underlying search intents of legal retrieval users, as shown in this paper, are more complicated than that yet mostly unexplored. To this end, we present a novel hierarchical intent taxonomy of legal case retrieval. It consists of five intent types categorized by three criteria, i.e., search for Particular Case(s), Characterization, Penalty, Procedure, and Interest. The taxonomy was constructed transparently and evaluated extensively through interviews, editorial user studies, and query log analysis. Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval. Furthermore, we apply the proposed taxonomy to various downstream legal retrieval tasks, e.g., result ranking and satisfaction prediction, and demonstrate its effectiveness. Our work provides important insights into the understanding of user intents in legal case retrieval and potentially leads to better retrieval techniques in the legal domain, such as intent-aware ranking strategies and evaluation methodologies.Comment: 28 pages, work in proces

Your "Flamingo" is My "Bird": Fine-Grained, or Not

Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the majority of us non-experts just "bird" would probably suffice. The real question is therefore -- how can we tailor for different fine-grained definitions under divergent levels of expertise. For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes "bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo". To approach this new problem, we first conduct a comprehensive human study where we confirm that most participants prefer multi-granularity labels, regardless whether they consider themselves experts. We then discover the key intuition that: coarse-level label prediction exacerbates fine-grained feature learning, yet fine-level feature betters the learning of coarse-level classifier. This discovery enables us to design a very simple albeit surprisingly effective solution to our new problem, where we (i) leverage level-specific classification heads to disentangle coarse-level features with fine-grained ones, and (ii) allow finer-grained features to participate in coarser-grained label predictions, which in turn helps with better disentanglement. Experiments show that our method achieves superior performance in the new FGVC setting, and performs better than state-of-the-art on traditional single-label FGVC problem as well. Thanks to its simplicity, our method can be easily implemented on top of any existing FGVC frameworks and is parameter-free.Comment: Accepted as an oral of CVPR2021. Code: https://github.com/PRIS-CV/Fine-Grained-or-No

Combination of Chinese herbal medicine and conventional western medicine for coronavirus disease 2019: a systematic review and meta-analysis

ObjectiveThis study aimed to assess the efficacy and safety of Chinese herbal medicine (CHM) plus conventional western medicine (CWM) in comparison with CWM against COVID-19.MethodsWe searched eight electronic databases and three trial registers spanning from January 1, 2020 to May 18, 2023. We included randomized controlled trials (RCTs) comparing the effectiveness and safety of CHM plus CWM and CWM against COVID-19 in our study. The Cochrane Risk of Bias tool 2.0 (RoB2) was applied to evaluate the methodological quality of the included RCTs. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system was employed to assess the certainty of evidence. Statistical analysis was implemented in R version 4.1.2.ResultsOur study included 50 RCTs involving 11,624 patients. In comparison with sole CWM, CHM plus CWM against COVID-19 significantly enhanced clinical effective rate (RR = 1.18, 95% CI [1.13, 1.22]), improved chest image (RR = 1.19, 95% CI [1.11, 1.28]), inhibited clinical deterioration (RR = 0.45, 95% CI [0.33, 0.60]), lowered mortality (RR = 0.53, 95% CI [0.40, 0.70]), and reduced the total score of TCM syndrome (SMD = −1.24, 95% CI [−1.82, −0.66]). SARS-CoV-2 nucleic acid conversion time (MD = −2.66, 95% CI [−3.88, −1.44]), duration of hospitalization (MD = −2.36, 95% CI [−3.89, −0.82]), and clinical symptom (fever, cough, fatigue, and shortness of breath) recovery times were shorter in CHM plus CWM groups than in CWM groups. Further, CHM plus CWM treatment was more conducive for some laboratory indicators returning to normal levels. No statistical difference was found in the incidence of total adverse reactions between the two groups (RR = 0.97, 95% CI [0.88, 1.07]). We assessed the risk of bias for 246 outcomes, and categorized 55 into “low risk”, 151 into “some concerns”, and 40 into “high risk”. Overall, the certainty of the evidence ranged from moderate to very low.ConclusionsPotentially, CHM listed in this study, as an adjunctive therapy, combining with CWM is an effective and safe therapy mode for COVID-19. However, more high-quality RCTs are needed to draw more accurate conclusions.Clinical trial registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=293963

Big Data Analytics of City Wide Building Energy Declarations

This thesis explores the building energy performance of the domestic sector in the city of Stockholm based on the building energy declaration database. The aims of this master thesis are to analyze the big data sets of around 20,000 buildings in Stockholm region, explore the correlation between building energy performance and different internal and external affecting factors on building energy consumption, such as building energy systems, building vintages and etc. By using clustering method, buildings with different energy consumptions can be easily identified. Thereafter, energy saving potential is estimated by setting step-by-step target, while feasible energy saving solutions can also be proposed in order to drive building energy performance at city level. A brief introduction of several key concepts, energy consumption in buildings, building energy declaration and big data, serves as the background information, which helps to clarify the necessity of conducting this master thesis. The methods used in this thesis include data processing, descriptive analysis, regression analysis, clustering analysis and energy saving potential analysis. The provided building energy declaration data is firstly processed in MS Excel then reorganized in MS Access. As for the data analysis process, IBM SPSS is further introduced for the descriptive analysis and graphical representation. By defining different energy performance indicators, the descriptive analysis presents the energy consumption and composition for different building classifications. The results also give the application details of different ventilation systems in different building types. Thereafter, the correlation between building energy performance and five different independent variables is analyzed by using a linear regression model. Clustering analysis is further performed on studied buildings for the purpose of targeting low energy efficiency groups, and the buildings with various energy consumptions are well identified and grouped based on their energy performance. It proves that clustering method is quite useful in the big data analysis, however some parameters in the process of clustering needs to be further adjusted in order to achieve more satisfied results. Energy saving potential for the studied buildings is calculated as well. The conclusion shows that the maximal potential for energy savings in the studied buildings is estimated at 43% (2.35 TWh) for residential buildings and 54% (1.68 TWh) for non-residential premises, and the saving potential is calculated for different building categories and different clusters as well.

Estimating window dimensions of residential buildings in district energy models

This work focuses on the impact of the window-to-wall ratio (WWR) on the energy demand for space heating and the building overheating through the evaluation of three WWR acquisition methods and four WWR allocation levels for a virtual district, containing 174 single-family dwellings. A new WWR acquisition method, based on a database, is proposed and evaluated. The impact of the WWR on the energy demand for space heating is limited (root mean square percentage errors up to 0.07), whereas the impact on the overheating is significantly higher (root mean square percentage errors up to 12.4). Both errors are higher for newer buildings compared to older buildings.status: publishe

Effect of a Hypoxia-Controlled Atmosphere Box on Egg Respiration Intensity and Quality

Egg preservation is an important factor during storage and transportation. Fresh eggs were stored in boxes in a controlled atmosphere with an O2 concentration of 0% O2 + 100% nitrogen (N2), 5% O2 + 95% N2, 10% O2 + 90% N2, 15% O2 + 85% N2, and 20% O2 + 80% N2, and the effects of these storage conditions on large quantities of eggs were studied. The respiratory intensity and quality of eggs during storage were measured. We chose the weight loss rate of eggs, Haugh unit, pH, and the egg white total plate count as the characteristic indices of egg quality. We compared the changes in egg quality during and after storage at different O2 concentrations versus that at 25 °C. The stages were evaluated using the TOPSIS method to sort egg quality, and the optimal O2 concentration was selected. FLUENT was used to simulate and control the atmospheric requirements. Our findings showed that eggs stored in an air-conditioning chamber with O2 concentration ≤10% exhibited weak respiratory intensity (0–1 mg/(kg·h)). The rates of decrease in loss of egg weight and Haugh units were smaller. There were significant differences in the pH of egg white stored in different O2 concentrations (p 2 concentration in the egg-storage environment reduced the number of colonies in eggs and had a positive effect on egg preservation. Simulations using FLUENT revealed that only 1200 s were required to achieve the low-oxygen environment in the controlled atmosphere box (1.5 m × 1 m × 1 m). The storage environment of 5% O2 + 95% N2 had the best preservation effect on eggs. This approach is associated with low costs in practical application and can potentially be used for egg storage and transport

Effect of a Hypoxia-Controlled Atmosphere Box on Egg Respiration Intensity and Quality

Egg preservation is an important factor during storage and transportation. Fresh eggs were stored in boxes in a controlled atmosphere with an O2 concentration of 0% O2 + 100% nitrogen (N2), 5% O2 + 95% N2, 10% O2 + 90% N2, 15% O2 + 85% N2, and 20% O2 + 80% N2, and the effects of these storage conditions on large quantities of eggs were studied. The respiratory intensity and quality of eggs during storage were measured. We chose the weight loss rate of eggs, Haugh unit, pH, and the egg white total plate count as the characteristic indices of egg quality. We compared the changes in egg quality during and after storage at different O2 concentrations versus that at 25 °C. The stages were evaluated using the TOPSIS method to sort egg quality, and the optimal O2 concentration was selected. FLUENT was used to simulate and control the atmospheric requirements. Our findings showed that eggs stored in an air-conditioning chamber with O2 concentration ≤10% exhibited weak respiratory intensity (0–1 mg/(kg·h)). The rates of decrease in loss of egg weight and Haugh units were smaller. There were significant differences in the pH of egg white stored in different O2 concentrations (p < 0.05). Reducing the O2 concentration in the egg-storage environment reduced the number of colonies in eggs and had a positive effect on egg preservation. Simulations using FLUENT revealed that only 1200 s were required to achieve the low-oxygen environment in the controlled atmosphere box (1.5 m × 1 m × 1 m). The storage environment of 5% O2 + 95% N2 had the best preservation effect on eggs. This approach is associated with low costs in practical application and can potentially be used for egg storage and transport