Efficiency of Public Educational Expenditure in China

The study investigates the efficiency of local public educational expenditure of 31 provinces in China during 2005-2010, using the Slack-based Measurement (SBM) directional distance function. The results show that public educational expenditure is the most efficient in eastern China, followed by middle and western areas. The inefficiency can be explained mostly by the number of master graduates, while the impacts of the number of undergraduates and graduates from secondary school are also significant. Additionally, bootstrap method is applied to explore the contextual factors influencing the efficiency. The results suggest that economic development and urbanization process increase the efficiency, while the state-owned industry obstructs the development

Non-parametric analysis of yield risk in Lithuanian crop farming

Socioeconomic development requires meeting the goals of food security. Yield risk constitutes an important factor of farming business viability. As the Central and Eastern European countries have been affected by both economic and environmental transformations, there is a need to develop a robust methodology for assessment of yield risks in order to propose convincing guidelines for both farmers and government institutions in regards to risk management and viability of agricultural business in general. This paper attempts to devise non-parametric measures of yield risk for Lithuanian crop farming. The research covers the period of 2000–2015. County-level data from Statistics Lithuania are employed for the analysis. The non-parametric analysis of yield risk relies on information diffusion theory and linear moving average. The results indicate that there exist differences in yield trends, yield loss rates and yield risk among crops and regions. Maize, buckwheat and winter rape exhibited the highest yield risk. These results shed light on the extent of yield risks underlying crop farming in Lithuania and, to a certain extent, can be contrasted to situation in Central and Eastern European countries. Indeed, the obtained results can be applied in decision making at different levels of management

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements. Although recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM, they hand-craft quantization parameters, which leads to low performance and fails to deal with extremely low-bit quantization. To tackle this issue, we introduce an Omnidirectionally calibrated Quantization (OmniQuant) technique for LLMs, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters. OmniQuant comprises two innovative components including Learnable Weight Clipping (LWC) and Learnable Equivalent Transformation (LET). LWC modulates the extreme values of weights by optimizing the clipping threshold. Meanwhile, LET tackles activation outliers by shifting the challenge of quantization from activations to weights through a learnable equivalent transformation. Operating within a differentiable framework using block-wise error minimization, OmniQuant can optimize the quantization process efficiently for both weight-only and weight-activation quantization. For instance, the LLaMA-2 model family with the size of 7-70B can be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using 128 samples. Extensive experiments validate OmniQuant's superior performance across diverse quantization configurations such as W4A4, W6A6, W4A16, W3A16, and W2A16. Additionally, OmniQuant demonstrates effectiveness in instruction-tuned models and delivers notable improvements in inference speed and memory reduction on real devices. Codes and models are available at \url{https://github.com/OpenGVLab/OmniQuant}.Comment: Updated result with 2-bit quantization. A differentiable quantization method for LL

Final Energy Consumption Trends and Drivers in Czech Republic and Latvia

This paper analyses the trends of final energy consumption in Latvia and Czech Republic. Analysis of final energy consumption during 2000-2013 period indicated the main driving forces of final energy consumption during and after world financial crisis of 2008. The paper aimed to evaluate the impact of economic activity and other factors on final energy consumption. The decomposition of the final energy consumption is assessed by analyzing effect of different drivers by the main end-users sector (industry, transport, households, agriculture, services), activity, demography, lifestyles, structural effects, energy savings etc. The results show that the reduction in final energy consumption in most EU members states before and after year 2008 can be related to the decline in energy intensities within endusers sectors. At the same time, the increase in final energy intensity after the year 2008 is attributed to expansion of energy demand sectors. Comparison of final energy consumption trends and drivers in Latvia and Czech Republic indicated that Czech Republic implemented more policies and measures in industry and tertiary sector and this provided for final energy consumption decreased and huge energy savings in these sectors

EVALUATION OF AN END-TO-END RADIOTHERAPY TREATMENT PLANNING PIPELINE FOR PROSTATE CANCER

Radiation treatment planning is a crucial and time-intensive process in radiation therapy. This planning involves carefully designing a treatment regimen tailored to a patient’s specific condition, including the type, location, and size of the tumor with reference to surrounding healthy tissues. For prostate cancer, this tumor may be either local, locally advanced with extracapsular involvement, or extend into the pelvic lymph node chain. Automating essential parts of this process would allow for the rapid development of effective treatment plans and better plan optimization to enhance tumor control for better outcomes. The first objective of this work, to automate the treatment planning process, was the automatic segmentation of critical structures. Delineation of both target and normal tissue structures was necessary to establish the foundation for identifying where radiation must be delivered and what should be spared from excess radiation. Deep learning segmentation models were developed from retrospective CT simulation imaging data and clinical contours to delineate intact, postoperative, and nodal treatment structures for prostate cancer to accomplish this objective. Quality contours were extracted per established contouring guidelines in the literature. Model refinement on a holdout fine-tune dataset was used to verify model contours before quantitative and qualitative evaluation on the holdout test set. Predicted contours resulted in contours comparable in quantitative Dice-Similarity-Coefficient (DSC) and 95% Hausdorff Distance (HD95) to proposed models in literature and clinically usable contours with no more than minor edits upon physician review. The second objective was the automation of Volumetric Modulated Arc Therapy (VMAT) planning for a breadth of prostate treatment scenarios. Development of VMAT plans for intact, postoperative, and nodal involvement treatment cases was necessary for the sequence in daily treatment delivery and the prospective distribution of radiation dose to target and normal tissues. To accomplish this objective, knowledge-based planning models were separately developed to estimate patient-specific DVHs to guide plan optimization for radiation delivery. These two models were then used in this work for end-to-end testing of cases with and without lymph node involvement, including determining if the prostate target is intact or postoperative with or without treatment devices such as hydrogel spacers and rectal balloons. A sequence of iterative optimization runs was created to ensure hotspot reduction and target conformality. The findings demonstrated that plans developed from automatically generated contours were clinically usable with minor edits for intact and postoperative treatments without lymph node involvement. For treatments with lymph node involvement, dose constraints were met for a select set of cases without excessive rectum curvature or excessive bladder descension into the postoperative treatment bed. When comparing auto-segmented to clinical contours, clinical contours experienced similar pass rates as those achieved by auto-segmented contours

Discovering the structure and organization of a free Cantonese emotion-label word association graph to understand mental lexicons of emotions

Emotions are not necessarily universal across different languages and cultures. Mental lexicons of emotions depend strongly on contextual factors, such as language and culture. The Chinese language has unique linguistic properties that are different from other languages. As a main variant of Chinese, Cantonese has some emotional expressions that are only used by Cantonese speakers. Previous work on Chinese emotional vocabularies focused primarily on Mandarin. However, little is known about Cantonese emotion vocabularies. This is important since both language variants might have distinct emotional expressions, despite sharing the same writing system. To explore the structure and organization of Cantonese-label emotion words, we selected 79 highly representative emotion cue words from an ongoing large-scale Cantonese word association study (SWOW-HK). We aimed to identify the categories of these emotion words and non-emotion words that related to emotion concepts. Hierarchical cluster analysis was used to generate word clusters and investigate the underlying emotion dimensions. As the cluster quality was low in hierarchical clustering, we further constructed an emotion graph using a network approach to explore how emotions are organized in the Cantonese mental lexicon. With the support of emotion knowledge, the emotion graph defined more distinct emotion categories. The identified network communities covered basic emotions such as love, happiness, and sadness. Our results demonstrate that mental lexicon graphs constructed from free associations of Cantonese emotion-label words can reveal fine categories of emotions and their relevant concepts

Effect of Chitosan Coating with Different Molecular Weights on the Storage Quality of Postharvest Passion Fruit (Passiflora edulis Sims)

To study the preservation effect of chitosan coating with different molecular weights on postharvest passion fruit, the "Qinmi No.9" was coated with chitosan of molecular weights of 30, 50, 100, 150 and 200 kDa (1.5%, w/v) to determine the quality of passion fruit during storage. The results showed that chitosan coating with different molecular weights was able to delay the shrinkage and yellowing, reduce the weight loss rate and inhibit the decay of passion fruit. Moreover, chitosan with a larger molecular weight was more conducive to delaying the ripening and senescence of passion fruit, as well as reducing shrinkage, and decay. At the end of storage, the weight loss of fruits coated with 200 kDa chitosan was nearly 10% less than that coated with 30 kDa chitosan, and the fruits coated with 150 and 200 kDa chitosan did not decay. The lower molecular weight (30 and 50 kDa) and higher molecular weight (150 kDa) chitosan were more effective in inhibiting weight loss, total soluble solids and soluble sugar metabolism, and maintaining titratable acid, flavonoid and total phenol contents of fruit during storage. The chitosan with 150 kDa had the best effect in maintaining the vitamin C content, which was 1.12 times higher than the control group at the end of storage. In conclusion, chitosan with different molecular weights was effective to delay senescence, slow down water loss and shrink of passion fruit and maintain the quality, chitosan with 150 kDa was more suitable to maintain the quality of postharvest passion fruit

The molecular mechanism for inhibiting the growth of nasopharyngeal carcinoma cells using polymethoxyflavonoids purified from pericarp of Citrus reticulata ‘Chachi’ via HSCCC

Polymethoxyflavonoids (PMFs), the main bioactive compounds naturally occurring in the pericarp of Citrus reticulata ‘Chachi’ (CRCP), possess significant antitumor action. However, the action of PMFs in nasopharyngeal carcinoma (NPC) is currently unknown. The present research study was conducted to investigate the inhibitory mechanisms of PMFs from CRCP on NPC growth in vivo and in vitro. In our research, we used high-speed counter-current chromatography (HSCCC) to separate four PMFs (nobiletin (NOB), 3,5,6,7,8,3′,4′-heptamethoxyflavone (HMF), tangeretin (TGN), and 5-hydroxy-6,7,8,3′,4′-pentamethoxyflavone (5-HPMF)) from CRCP. CCK-8 assay was used to preliminarily screen cell viability following exposure to the four PMFs. Colony formation, Hoechst-33258 staining, transwell, and wound scratch assays were performed to assess the anti-proliferation, invasion, migration, and apoptosis-inducing effects of HMF on NPC cells. NPC tumors in xenograft tumor transplantation experiments were also established to explore the effect of HMF (100 and 150 mg/kg/day) on NPC. The histopathological changes in the treated rats were observed by H&E staining and Ki-67 detection by immunohistochemical techniques. The expressions of P70S6K, p-P70S6K, S6, p-S6, COX-2, p53, and p-p53 were measured by Western blot. The four PMFs were obtained with high purity (>95.0%). The results of the preliminary screening by CCK-8 assay suggested that HMF had the strongest inhibitory effect on NPC cell growth. The results of the colony formation, Hoechst-33258 staining, transwell, and wound scratch assays indicated that HMF had significant anti-proliferation, invasion, migration, and apoptosis-inducing ability in NPC cells. Moreover, HMF suppressed NPC tumor growth in xenograft tumor transplantation experiments. Further investigation suggested that HMF regulated NPC cells proliferation, apoptosis, migration, and invasion by activating AMPK-dependent signaling pathways. In conclusion, HMF-induced AMPK activation inhibited NPC cell growth, invasion, and metastatic potency by downregulating the activation of the mTOR signaling pathway and COX-2 protein levels, as well as enhancing the p53 phosphorylation level. Our study provides a crucial experimental basis for the clinical treatment of NPC, as well as the development and utilization of PMFs from CRCP

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001