Abstract: Language models, such as GPT-3 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks, using instruction fine-tuning. However, when we test language models with a range of basic table-understanding tasks, we observe that today's language models are still sub-optimal in many table-related tasks, likely because they are pre-trained predominantly on one-dimensional natural-language texts, whereas relational tables are two-dimensional objects. In this work, we propose a new "\emphtable fine-tuning '' paradigm, where we continue to train/fine-tune language models like GPT-3.5 and ChatGPT, using diverse table-tasks synthesized from real tables as training data, which is analogous to "instruction fine-tuning'', but with the goal of enhancing language models' ability to understand tables and perform table tasks. We show that our resulting \sys models demonstrate: (1) better table-understanding capabilities, by consistently outperforming the vanilla GPT-3.5 and ChatGPT, on a wide range of table tasks (data transformation, data cleaning, data profiling, data imputation, table-QA, etc.), including tasks that are completely holdout and unseen during training, and (2) strong generalizability, in its ability to respond to diverse human instructions to perform new and unseen table-tasks, in a manner similar to GPT-3.5 and ChatGPT. Our code and data have been released at https://github.com/microsoft/Table-GPT for future research.

Abstract: ChatGPT holds significant potential for enhancing learning through integration into education as an advanced chatbot. With the goal of harnessing this potential, our research focused on exploring the utilization of ChatGPT in designing a course plan for pre-service science teachers. We adopted a qualitative research approach and employed ChatGPT as an assistant to create a course plan for classroom assessment in science education. Our conversation to create this plan served as our data source for document analysis. We conducted interpretive analysis for qualitative data. The findings emphasized the benefits of ChatGPT in developing an implementable course plan, delivering adaptable information, and time saving. However, there were limitations to consider. These challenges encompassed issues such as communicating out of ChatGPT and the possibility of miscommunication. Despite these limitations, the research findings clearly demonstrate that ChatGPT is highly effective in developing a course plan. As researchers who have personally experienced the process of creating a course plan using ChatGPT, we believe that its potential needs to be maximally utilized. We suggest its application across different subjects and disciplines to thoroughly examine its strengths and weaknesses in depth.

Abstract: RAG (Retrieval Augmented Generation) is generally used for generating results from the existing knowledge-base. RAG refers to finding references (R), Adding references (A) and improving generation(i.e, answers to the question) (G). MultiModel-RAGs are used for generation of results over the documents which contain images and texts. There exists multiple different Multimodel-RAGs but these are not still efficient in generation of the results from the documents which contain relationships between images and texts. This study has proposed the solution to enable effective retrieval and generation of results, which includes the relationship between images and texts. The comparison of proposed Multimodal RAG with four different datasets (i.e., Short-form-type-QA, Long-form-type-QA, MCQ-type-QA, True-False-type-QA) shows the proposed solution improves the effectiveness of the existing Multimodal RAGs. Testing of proposed Multimodal RAG over two different other multimodal LLM i.e, Open-AI & Gemini helps in deciding whether the proposed solution fits best with LLM in different cases.

Abstract: The density functional tight-binding (DFTB) approach allows electronic structure-based simulations at length and time scales far beyond what is possible with first-principles methods. This is achieved by using minimal basis sets and empirical approximations. Unfortunately, the sparse availability of parameters across the periodic table is a significant barrier to the use of DFTB in many cases. We therefore propose a workflow that allows the robust and consistent parametrization of DFTB across the periodic table. Importantly, our approach requires no element-pairwise parameters so that the parameters can be used for all element combinations and are readily extendable. This is achieved by parametrizing all elements on a consistent set of artificial homoelemental crystals, spanning a wide range of coordination environments. The transferability of the resulting periodic table baseline parameters to multielement systems and unknown structures is explored and the model is extensively benchmarked against previous specialized and general DFTB parametrizations.

Abstract: During postharvest, table grapes are often spoiled by molds. Aspergillus sp., Alternaria sp., Botrytis sp., Cladosporium sp. and Penicillium sp. are different mold genera frequently related to table grape rot. Fungal spoilage affects nutritional value and organoleptic properties while also producing health hazards, such as mycotoxins. Traditionally, synthetic fungicides have been employed to control fungal diseases. However, possible negative effects on health and the environment are a serious concern for consumers and government entities. This review summarized data on innovative strategies proposed to diminish postharvest losses and extend table grape shelf life. Among physical, chemical, and biological strategies, either alone or in combination, the integrated management of fungal diseases is a sustainable alternative to synthetic fungicides. However, to date, only a few alternative technologies have succeeded on a commercial scale. Recent research aimed at increasing the competitiveness of alternative technologies has led to the development of integrated management strategies to prevent postharvest decay and increase the safety and quality of table grapes.

Abstract: In today's world, agriculture is not only about food production but also a critical factor in global environmental change, economic stability, and human health, among other aspects. With population growth and increasingly scarce resources, exploring sustainable development of food systems has become crucial. Achieving this goal requires striking a delicate balance among food security, economic development, ecological environment, and human health. Traditional approaches to sustainable agricultural development research often focus solely on singular domains, overlooking the inherent connections and interactions among environmental, socioeconomic, and health dimensions. This perspective limits our comprehensive understanding of food systems. Environmental footprint assessments can be integrated with economic, systemic, and decision models to analyze environmental, socioeconomic, and health issues within food systems. This integration accurately captures the diversity, overlap, accumulation, and heterogeneity of environmental pressures resulting from human and natural factors. Therefore, we propose an innovative conceptual framework that considers environmental, socioeconomic, and health dimensions as crucial components, with the environmental footprint indicators at its core, to link various stages from farm to table. This framework constructs an evidence gap map, integrating dispersed data and perspectives from existing literature, thus showing knowledge gaps across these domains. Such an interdisciplinary approach not only provides a more comprehensive perspective on the multidimensional complexity of sustainable food systems but also reveals potential synergies and conflicts among environmental, socioeconomic, and health domains, thereby guiding more comprehensive and cautious policy-making. Importantly, it provides direction for future research to achieve the sustainable development of food systems, emphasizing the necessity of a comprehensive, integrated research perspective, particularly in strengthening studies on composited footprints, viewing the entire farm-to-table continuum holistically. Stakeholders must collaborate and coordinate environmental, socioeconomic, and health objectives to drive the sustainable development of food systems.

Abstract: Abstract Here we address the combined impact of multiple stressors that are becoming more common with climate change. To study the combined effects of a lower water table (WT) and increased frequency of drought periods on the resistance and resilience of peatlands, we conducted a mesocosm experiment. This study evaluated how the photosynthesis of lawn Sphagnum mosses responds to and recovers from an experimental periodic drought after exposure to the stresses of a deep or deepened WT (naturally dry and 17‐year‐long water level drawdown [WLD] in fen and bog environments). We aimed to quantify if deep WTs (1) support acclimation to drought, or (2) increase the base‐level physiological stress of mosses or (3) exacerbate the impact of periodic drought. There was no evidence of acclimation in mosses from drier environments; periodic drought decreased the photosynthesis of all Sphagnum species studied. WLDdecreased the photosynthesis of bog‐originating mosses prior to periodic drought, indicating that these mosses were stressed by the hydrological change. Deep WTs exacerbated Sphagnum vulnerability to periodic drought, indicating that the combination of drying habitats and increasing frequency of periodic drought could lead to a rapid transition in lawn vegetation. Water‐retaining traits may increase Sphagnum resilience to periodic drought. Large capitula size was associated with a higher resistance; the bog originating species studied here lacked large capitula or dense carpet structure and were more vulnerable to drought than the larger fen originating species. Consequently, lawns in bogs may become threatened. Recovery after rewetting was significant for all mosses, but none completely recovered within 3 weeks. The most drought‐resilient species had fen origin, indicating that fens are less likely to undergo a sudden transition due to periodic drought. S ynthesis : Water level drawdown associated with climate change increases the sensitivity of Sphagnum mosses to periods of drought and moves them closer to their tipping point as species on the edge of their ecological envelope rapidly shut down photosynthesis and recover poorly.

Abstract: Microplastics (MPs) have become an emerging contaminant in many environmental compartments. Although numerous studies worldwide have accounted for MP contamination in our primary condiments such as salt, there is limited data available in the Philippines. This study aims to determine MPs in commercially distributed salts in markets in Iligan City, Northern Mindanao, the Philippines. We collected table salts of various brands according to their information on the packaging, weight, and salt type. Salt samples were diluted with ultrapure deionized water to float MPs due to their intrinsic hydrophobicity. After flotation, the solutions were filtrated using a vacuum system. The filters were observed under microscopy analysis and suspected MP particles were verified using ATR-FTIR analysis. A total of 31 MP particles were detected, which averaged 11.27 ± 4.31 particles/kg of salt. The most abundant polymer types of MPs were polypropylene (23%), polyethylene (23%) and polyamide (19%). The shapes of MPs were dominated by fibers (65%), whereas white (45%) was the most prevalent color. This research is the first to provide a snapshot of MPs in salts in the Philippines. This adds knowledge on the extent of MP pollution in the food industries and informs the government’s prioritization of this emerging food contaminant.

Abstract: Recently, an increasing interest has been in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

Abstract: Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables. However, these methods often require the entire table as input, leading to scalability challenges due to the positional bias or context length constraints. In response to these challenges, we introduce TableRAG, a Retrieval-Augmented Generation (RAG) framework specifically designed for LM-based table understanding. TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information before providing it to the LMs. This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss. We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale. Our results demonstrate that TableRAG's retrieval design achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding.

Abstract: Convolutional neural networks (CNNs) have achieved significant success in various applications. Numerous hardware accelerators are introduced to accelerate CNN execution with improved energy efficiency compared to traditional software implementations. Despite the achieved success, deploying traditional hardware accelerators for bulky CNNs on current and emerging smart devices is impeded by limited resources, including memory, power, area, and computational capabilities. Recent works introduced processing-in-memory (PIM), a non-Von-Neumann architecture, which is a promising approach to tackle the problem of data movement between logic and memory blocks. However, as observed from the literature, the existing PIM architectures cannot congregate all the computational operations due to limited programmability and flexibility. Furthermore, the capabilities of the PIM are challenged by the limited available on-chip memory. To enable faster computations and address the limited on-chip memory constraints, this work introduces a novel reconfigurable approximate computing-based PIM, termed ReApprox-PIM. The proposed ReApprox-PIM is capable of addressing the two challenges mentioned above in the following manner: (i) it utilizes a programmable look-up-table (LUT)-based processing architecture that can support different approximate computing techniques via programmability, and (ii) followed by resource-efficient, fast CNN computing via the implementation of highly-optimized approximate computing techniques. This results in improved computing footprint, operational parallelism, and reduced computational latency and power consumption compared to prior PIMs relying on exact computations for CNN inference acceleration at a minimal sacrifice of accuracy. We have evaluated the proposed ReApprox-PIM on various CNN architectures, for inference applications including standard LeNet, AlexNet, ResNet-18, -34, and -50. Our experimental results show that the ReApprox-PIM achieves a speedup of 1.63× with 1.66 × lower area for the processing components compared to the existing PIM architectures. Furthermore, the proposed ReApprox-PIM achieves 2.5× higher energy efficiency and 1.3× higher throughput compared to the state-of-the-art LUT-based PIM architectures.

Abstract: Table summarization is a crucial task aimed at condensing information from tabular data into concise and comprehensible textual summaries. However, existing approaches often fall short of adequately meeting users' information and quality requirements and tend to overlook the complexities of real-world queries. In this paper, we propose a novel method to address these limitations by introducing query-focused multi-table summarization. Our approach, which comprises a table serialization module, a summarization controller, and a large language model (LLM), utilizes textual queries and multiple tables to generate query-dependent table summaries tailored to users' information needs. To facilitate research in this area, we present a comprehensive dataset specifically tailored for this task, consisting of 4909 query-summary pairs, each associated with multiple tables. Through extensive experiments using our curated dataset, we demonstrate the effectiveness of our proposed method compared to baseline approaches. Our findings offer insights into the challenges of complex table reasoning for precise summarization, contributing to the advancement of research in query-focused multi-table summarization.

Abstract: Abstract Objectives The generation of structured documents for clinical trials is a promising application of large language models (LLMs). We share opportunities, insights, and challenges from a competitive challenge that used LLMs for automating clinical trial documentation. Materials and Methods As part of a challenge initiated by Pfizer (organizer), several teams (participant) created a pilot for generating summaries of safety tables for clinical study reports (CSRs). Our evaluation framework used automated metrics and expert reviews to assess the quality of AI-generated documents. Results The comparative analysis revealed differences in performance across solutions, particularly in factual accuracy and lean writing. Most participants employed prompt engineering with generative pre-trained transformer (GPT) models. Discussion We discuss areas for improvement, including better ingestion of tables, addition of context and fine-tuning. Conclusion The challenge results demonstrate the potential of LLMs in automating table summarization in CSRs while also revealing the importance of human involvement and continued research to optimize this technology.

Abstract: Inter-regional input-output (IRIO) tables are essential for socioeconomic and environmental analysis. This paper compiled a continuous time series of Chinese IRIO tables with a detailed regional and sectoral classification, covering a longer period from 1987 to 2017 than existing Chinese IRIO tables. Additionally, we integrated the China provincial CO2 emission inventory data (1987–2017) to analyze trends and identify the main driving factors behind regional economic linkages and carbon emissions changes. Specifically, we adjusted the initial values of the international and interprovincial imports and exports using customs data. Subsequently, we employed the minimum cross-entropy model to balance the adjusted input-output tables (IOTs) from both row and column perspectives. Following this, we estimated the interprovincial trade flow for each sector using the gravity model. Then, we balanced these estimates with the minimum cross-entropy model to form the multi-regional input-output (MRIO) table. Finally, we constructed the IRIO table based on the proportion assumption. The compiled IRIO tables, integrated with carbon emission data, have significant applications in regional economic analysis, policy evaluation, and environmental studies.

Abstract: Security in Software Defined Network (SDN) architecture is becoming the most substantial challenge. This paper introduces a novel threat model focused on flow table modification in the P4-programmable SDN data plane, outlining an attacker’s stochastic manipulation of flow rules from a compromised switch. A detection framework is proposed to identify the malicious switch within the network by utilizing the thrift port. Moreover, a fuzzy-rule-based mitigation strategy has been proposed to identify the severity of attacks. The feasibility and effectiveness of the methodology are evaluated using a developed testbed setup by employing Facebook datacenter fabric topology in a Mininet emulator and BMv2 switch.

Abstract: Action recognition is one of the challenging video understanding tasks in computer vision. Although there has been extensive research in the task of classifying coarse-grained actions, existing methods are still limited in differentiating actions with low inter-class and high intra-class variation. Particularly, the table tennis sport that involves shots of high inter-class similarity, subtle variations, occlusion, and view-point variations. While a few datasets have been available for event spotting and shot recognition, these benchmarks are mostly recorded in a constrained environment with a clear view/perception of shots executed by players. In this paper, we introduce a Table tennis shots 1.0 dataset consisting of 9000 videos of 6 fine-grained actions collected in an unconstrained manner to analyze the performance of both players. To effectively recognise these different types of table tennis shots, we propose an adaptive temporal aggregation method that can handle the temporal interactions concerning the subtle variations among shots and low inter-class variations. Our method consists of two components, namely, (i) feature extraction module and (ii) temporal aggregation network. The feature extraction module is a 3D convolutional neural network (3D-CNN) that captures the spatial and temporal characteristics of table tennis shots. Here we propose to replace the final global average pooling layer (GAP) with the temporal aggregation network to overcome the loss of motion information due to averaging of temporal features. This temporal aggregation network utilizes the attention mechanism of bidirectional encoder representations from Transformers (BERT) to model the significant temporal interactions among the shots effectively. We demonstrate that our proposed approach improves the performance of existing 3D-CNN methods by ≈10% on the Table tennis shots 1.0 dataset.

Abstract: The deployment of Large Language Models (LLMs) on edge devices is increasingly important to enhance on-device intelligence. Weight quantization is crucial for reducing the memory footprint of LLMs on devices. However, low-bit LLMs necessitate mixed precision matrix multiplication (mpGEMM) of low precision weights and high precision activations during inference. Existing systems, lacking native support for mpGEMM, resort to dequantize weights for high precision computation. Such an indirect way can lead to a significant inference overhead. In this paper, we introduce T-MAC, an innovative lookup table(LUT)-based method designed for efficient low-bit LLM (i.e., weight-quantized LLM) inference on CPUs. T-MAC directly supports mpGEMM without dequantization, while simultaneously eliminating multiplications and reducing additions required. Specifically, T-MAC transforms the traditional data-type-centric multiplication to bit-wise table lookup, and enables a unified and scalable mpGEMM solution. Our LUT-based kernels scale linearly to the weight bit-width. Evaluated on low-bit Llama and BitNet models, T-MAC demonstrates up to 4x increase in throughput and 70% reduction in energy consumption compared to llama.cpp. For BitNet-b1.58-3B, T-MAC delivers a token generation throughput of 30 tokens/s with a single core and 71 tokens/s with eight cores on M2-Ultra, and 11 tokens/s on lower-end devices like Raspberry Pi 5, which significantly exceeds the adult average reading speed. T-MAC with LUT-based computing paradigm, paves the way for the practical deployment of low-bit LLMs on resource-constrained edge devices without compromising computational efficiency. The system is open-sourced at https://github.com/microsoft/T-MAC.

Abstract: SDN has evolved to revolutionize next-generation networks, offering programmability for on-the-fly service provisioning, primarily supported by the OpenFlow (OF) protocol. The limited storage capacity of Ternary Content Addressable Memory (TCAM) for storing flow tables in OF switches introduces vulnerabilities, notably the Low-Rate Flow Table Overflow (LOFT) attacks. LOFT exploits the flow table’s storage capacity by occupying a substantial amount of space with malicious flow, leading to a gradual degradation in the flow-forwarding performance of OF switches. To mitigate this threat, we propose FloRa, a machine learning-based solution designed for monitoring and detecting LOFT attacks in SDN. FloRa continuously examines and determines the status of the flow table by closely examining the features of the flow table entries. When suspicious activity is identified, FloRa promptly activates the machine-learning based detection module. The module monitors flow properties, identifies malicious flows, and blacklists them, facilitating their eviction from the flow table. Incorporating novel features such as Packet Arrival Frequency, Content Relevance Score, and Possible Spoofed IP along with Cat Boost employed as the attack detection method. The proposed method reduces CPU overhead, memory overhead, and classification latency significantly and achieves a detection accuracy of 99.49% which is more than the state-of-the-art methods to the best of our knowledge. This approach not only protects the integrity of the flow tables but also guarantees the uninterrupted flow of legitimate traffic. Experimental results indicate the effectiveness of FloRa in LOFT attack detection, ensuring uninterrupted data forwarding and continuous availability of flow table resources in SDN.

Abstract: Abstract Given the significance of amateur sports matches in health promotion and city culture construction. It is essential to systematically analyze the organizational mode of city amateur matches and propose development strategies. This study aimed to investigate the sustainable development strategies for city amateur matches in China. This study adopted a hybrid model of combined SWOT and the AHP analysis, using the Shanghai City Amateur Table Tennis Matches (ATTM) as a case study. Results showed that 20 factors of the SWOT analysis were included, and the ranking of weights of the SWOT group are Strengths, Opportunities, Weaknesses and Threats, respectively, and the strategic vector (θ, ρ) are (74.21°, 0.5861). ATTM should adopt the S–O pioneering strategy and leverages its advantages and opportunities to promote further development. The findings indicate that ATTM with advanced organizational mode, has good internal strengths and external opportunities, which can enlighten the development of amateur table tennis matches for other regions and countries. Future research should apply the hybrid model to a broader range of events and conduct comparative analyses across countries and regions.