Linear and Range Counting under Metric-based Local Differential Privacy

Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector. Error-optimal primitives (for, e.g., estimating means and item frequencies) under LDP have been well studied. For analytical tasks such as range queries, however, the best known error bound is dependent on the domain size of private data, which is potentially prohibitive. This deficiency is inherent as LDP protects the same level of indistinguishability between any pair of private data values for each data downer. In this paper, we utilize an extension of $\epsilon$-LDP called Metric-LDP or $E$-LDP, where a metric $E$ defines heterogeneous privacy guarantees for different pairs of private data values and thus provides a more flexible knob than $\epsilon$ does to relax LDP and tune utility-privacy trade-offs. We show that, under such privacy relaxations, for analytical workloads such as linear counting, multi-dimensional range counting queries, and quantile queries, we can achieve significant gains in utility. In particular, for range queries under $E$-LDP where the metric $E$ is the $L^1$-distance function scaled by $\epsilon$, we design mechanisms with errors independent on the domain sizes; instead, their errors depend on the metric $E$, which specifies in what granularity the private data is protected. We believe that the primitives we design for $E$-LDP will be useful in developing mechanisms for other analytical tasks, and encourage the adoption of LDP in practice

Unicron: Economizing Self-Healing LLM Training at Scale

Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current failure recovery methods in cloud-based settings inadequately address the diverse and complex scenarios that arise, focusing narrowly on erasing downtime for individual tasks without considering the overall cost impact on a cluster. We introduce Unicron, a workload manager designed for efficient self-healing in large-scale language model training. Unicron optimizes the training process by minimizing failure-related costs across multiple concurrent tasks within a cluster. Its key features include in-band error detection for real-time error identification without extra overhead, a dynamic cost-aware plan generation mechanism for optimal reconfiguration, and an efficient transition strategy to reduce downtime during state changes. Deployed on a 128-GPU distributed cluster, Unicron demonstrates up to a 1.9x improvement in training efficiency over state-of-the-art methods, significantly reducing failure recovery costs and enhancing the reliability of large-scale language model training

Evaluation of gliadins-diglycosylated cyanidins interaction from litchi pericarp through ultraviolet and fluorescence measurements

The low stability of anthocyanins limits their use in industry, which can be surpassed by gliadins linkage. This work was aimed to study the anthocyanins-gliadins bonding properties. HPLC-DAD-ESI-M..

Recovery of oil with unsaturated fatty acids and polyphenols from chaenomelessinensis (Thouin) Koehne: Process optimization of pilot-scale subcritical fluid assisted extraction

The potential effects of three modern extraction technologies (cold-pressing, microwaves and subcritical fluids) on the recovery of oil from Chaenomelessinensis (Thouin) Koehne seeds have been evaluated and compared to those of conventional chemical extraction methods (Soxhlet extraction). This oil contains unsaturated fatty acids and polyphenols. Subcritical fluid extraction (SbFE) provided the highest yield—25.79 g oil/100 g dry seeds—of the three methods. Moreover, the fatty acid composition in the oil samples was analysed using gas chromatography–mass spectrometry. This analysis showed that the percentages of monounsaturated (46.61%), and polyunsaturated fatty acids (42.14%), after applying SbFE were higher than those obtained by Soxhlet, cold-pressing or microwave-assisted extraction. In addition, the oil obtained under optimized SbFE conditions (35 min extraction at 35 °C with four extraction cycles), showed significant polyphenol (527.36 mg GAE/kg oil), and flavonoid (15.32 mg RE/kg oil), content, had a good appearance and was of high quality

Anthocyanin-rich edible flowers, current understanding of a potential new trend in dietary patterns.

Funding Information: The authors would like to thank the following institutions: FCT- Fundação para a Ciência e a Tecnologia through the unit funding UIDB/50006/2020 and project AnthoE.Flos - 2022.01014. PTDC; European Regional Development Fund ( ERDF ), through the NORTE 2020 (Programa Operacional Regional do Norte 2014/2020) for the AgriFood XXI I&D&I project (NORTE-01-0145-FEDER-000041). H.O. and A.L.F. would like to also acknowledge their CEEC contracts 2021.00002. CEECIND and CEECIND/00029/2018, respectively. Funding Information: The authors would like to thank the following institutions: FCT- Fundação para a Ciência e a Tecnologia through the unit funding UIDB/50006/2020 and project AnthoE.Flos - 2022.01014. PTDC; European Regional Development Fund (ERDF), through the NORTE 2020 (Programa Operacional Regional do Norte 2014/2020) for the AgriFood XXI I&D&I project (NORTE-01-0145-FEDER-000041). H.O. and A.L.F. would like to also acknowledge their CEEC contracts 2021.00002. CEECIND and CEECIND/00029/2018, respectively. Publisher Copyright: © 2023 Elsevier LtdBackground: Among the many sources of anthocyanins, edible flowers are regaining interest for both consumers and researchers due to their nutritional profile and the need for even more healthy dietary alternatives. In such context, anthocyanin-rich edible flowers may be one of the most interesting groups of such cultivars but also of anthocyanins source. Scope and approach: In this review, we discuss the latest findings regarding such type of edible flowers, from their consumption patterns to their nutritional and anthocyanins composition, their reported health benefits, the challenges about the consumption of edible flowers and the future research necessities on this promising thematic. Key findings and conclusions: Anthocyanins have become a key group of natural compounds during the last years due to their broad applications in different areas. From a nutritional and health perspective, these compounds have been showing potential roles against different pathologies. The excellent aroma, taste and appearance of anthocyanin-rich edible flowers turns meals more appealing to consumers. Moreover, their nutritional profile, bioactive properties, and health benefits, encourages the development of functional foods with nutraceutical purposes, thus promoting the consumption of these type of edible flowers worldwide. Further knowledge in food processing methods is a key factor on the comeback and the addition of anthocyanin-rich edible flowers to our dietary habits.publishersversionpublishe

Effects of Tea Residue Extracts with Different Molecular Weight on the Pasting Characteristics of Potato Starch

Tea residues are the remaining residue of tea after processing and utilization, which are rich in multiple active components. To investigate the effects of different types and molecular weights of tea residue extracts on the pasting characteristics of potato starch (PS), the ethanol extract (TRE), water extract (TRW) and alkali extract (TRA) of tea residue were obtained by continuous extraction method. On this basis, the different molecular weights of ethanol extract (TRE-1, 30 kDa) and water extract (TRW-1, 100 kDa) were prepared by a membrane separation. The effects of different tea residue extracts on the viscosity properties were investigated, and the microstructure of potato starch added with tea residue extract was observed by scanning electron microscopy (SEM). The results showed that different types and molecular weights of tea residue extracts could significantly (PTRW-2>TRE-2>TRW-1>TRE-1. The peak viscosity of potato starch was gradually decreased with the increase of different extracts. After adding 10% TRA, TRW-2, TRE-2, TRW-1 and TRE-1, the peak viscosity of potato starch was 4624, 5013, 5431, 5911 and 6195 cP, respectively. TRE-2, TRW-2 and TRA could better promote the link between potato starch fragments and result in a more complete and smooth lamellar structure, compared with TRE-1, TRW-1. In summary, the addition of different types and molecular weights of tea residue extracts could effectively inhibit the gelatinization of potato starch, and the inhibitory effect of 10% alkali extract was the best

MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random memory accesses needed to lookup the embedding tables. The inference is also heavily constrained in terms of latency because producing a recommendation for a user must be done in about tens of milliseconds. In this paper, we propose MicroRec, a high-performance inference engine for recommendation systems. MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups. We have implemented the resulting design on an FPGA board including the embedding lookup step as well as the complete inference process. Compared to the optimized CPU baseline (16 vCPU, AVX2-enabled), MicroRec achieves 13.8~14.7x speedup on embedding lookup alone and 2.5$~5.4x speedup for the entire recommendation inference in terms of throughput. As for latency, CPU-based engines needs milliseconds for inferring a recommendation while MicroRec only takes microseconds, a significant advantage in real-time recommendation systems.Comment: Accepted by MLSys'21 (the 4th Conference on Machine Learning and Systems

WordArt Designer API: User-Driven Artistic Typography Synthesis with Large Language Models on ModelScope

This paper introduces the WordArt Designer API, a novel framework for user-driven artistic typography synthesis utilizing Large Language Models (LLMs) on ModelScope. We address the challenge of simplifying artistic typography for non-professionals by offering a dynamic, adaptive, and computationally efficient alternative to traditional rigid templates. Our approach leverages the power of LLMs to understand and interpret user input, facilitating a more intuitive design process. We demonstrate through various case studies how users can articulate their aesthetic preferences and functional requirements, which the system then translates into unique and creative typographic designs. Our evaluations indicate significant improvements in user satisfaction, design flexibility, and creative expression over existing systems. The WordArt Designer API not only democratizes the art of typography but also opens up new possibilities for personalized digital communication and design.Comment: Spotlight Paper at the Workshop on Machine Learning for Creativity and Design, 37th Conference on Neural Information Processing Systems (NeurIPS 2023). 5 pages, 5 figure

WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models

This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM). The system incorporates four key modules: the LLM Engine, SemTypo, StyTypo, and TexTypo modules. 1) The LLM Engine, empowered by the LLM (e.g., GPT-3.5), interprets user inputs and generates actionable prompts for the other modules, thereby transforming abstract concepts into tangible designs. 2) The SemTypo module optimizes font designs using semantic concepts, striking a balance between artistic transformation and readability. 3) Building on the semantic layout provided by the SemTypo module, the StyTypo module creates smooth, refined images. 4) The TexTypo module further enhances the design's aesthetics through texture rendering, enabling the generation of inventive textured fonts. Notably, WordArt Designer highlights the fusion of generative AI with artistic typography. Experience its capabilities on ModelScope: https://www.modelscope.cn/studios/WordArt/WordArt.Comment: Accepted by EMNLP 2023, 10 pages, 11 figures, 1 table, the system is at https://www.modelscope.cn/studios/WordArt/WordAr

GraphScope Flex: LEGO-like Graph Computing Stack

Graph computing has become increasingly crucial in processing large-scale graph data, with numerous systems developed for this purpose. Two years ago, we introduced GraphScope as a system addressing a wide array of graph computing needs, including graph traversal, analytics, and learning in one system. Since its inception, GraphScope has achieved significant technological advancements and gained widespread adoption across various industries. However, one key lesson from this journey has been understanding the limitations of a "one-size-fits-all" approach, especially when dealing with the diversity of programming interfaces, applications, and data storage formats in graph computing. In response to these challenges, we present GraphScope Flex, the next iteration of GraphScope. GraphScope Flex is designed to be both resource-efficient and cost-effective, while also providing flexibility and user-friendliness through its LEGO-like modularity. This paper explores the architectural innovations and fundamental design principles of GraphScope Flex, all of which are direct outcomes of the lessons learned during our ongoing development process. We validate the adaptability and efficiency of GraphScope Flex with extensive evaluations on synthetic and real-world datasets. The results show that GraphScope Flex achieves 2.4X throughput and up to 55.7X speedup over other systems on the LDBC Social Network and Graphalytics benchmarks, respectively. Furthermore, GraphScope Flex accomplishes up to a 2,400X performance gain in real-world applications, demonstrating its proficiency across a wide range of graph computing scenarios with increased effectiveness