Online Scheduling with Predictions

Online scheduling is the process of allocating resources to tasks to achieve objectives with uncertain information about future conditions or task characteristics. This thesis presents a new online scheduling framework named online scheduling with predictions. The framework uses predictions about unknowns to manage uncertainty in decision-making. It considers that the predictions may be imperfect and include errors, surpassing the traditional assumptions of either complete information in online clairvoyant scheduling or zero information in online non-clairvoyant scheduling. The goal is to create algorithms with predictions that perform better with quality predictions while having bounded performance with poor predictions. The framework includes metrics such as consistency, robustness, and smoothness to evaluate algorithm performance. We prove the fundamental theorems that give tight lower bounds for these metrics. We apply the framework to central scheduling problems and cyber-physical system applications, including minimizing makespan in uniform machine scheduling with job size predictions, minimizing mean response time in single and parallel identical machine scheduling with job size predictions, and maximizing energy output in pulsed power load scheduling with normal load predictions. Analysis and simulations show that this framework outperforms state-of-the-art methods by leveraging predictions

Some identities related to reciprocal functions

AbstractThe concept of Riordan array is used on reciprocal functions, and some identities involving binomial numbers, Stirling numbers and many other special numbers are obtained

Optimization in Mobile Augmented Reality Systems for the Metaverse over Wireless Communications

As the essential technical support for Metaverse, Mobile Augmented Reality (MAR) has attracted the attention of many researchers. MAR applications rely on real-time processing of visual and audio data, and thus those heavy workloads can quickly drain the battery of a mobile device. To address such problem, edge-based solutions have appeared for handling some tasks that require more computing power. However, such strategies introduce a new trade-off: reducing the network latency and overall energy consumption requires limiting the size of the data sent to the edge server, which, in turn, results in lower accuracy. In this paper, we design an edge-based MAR system and propose a mathematical model to describe it and analyze the trade-off between latency, accuracy, server resources allocation and energy consumption. Furthermore, an algorithm named LEAO is proposed to solve this problem. We evaluate the performance of the LEAO and other related algorithms across various simulation scenarios. The results demonstrate the superiority of the LEAO algorithm. Finally, our work provides insight into optimization problem in edge-based MAR system for Metaverse.Comment: This paper appears in IEEE Global Communications Conference (GLOBECOM) 202

Chemical and biological characterisation of extracts from forgotten or underutilised medicinal and aromatic plants from Midi-Pyrénées (France) and Chongqing (China) regions

In both Midi-Pyrénées region (France) and Chongqing region (China), there are rich and underutilized medicinal and aromatic plants (MAP). Aiming at fully exploiting different molecules in these plants, the concept of MAP-refinery was developed and applied to several underutilized medicinal and aromatic plants in these two regions. Several water-based green extraction technologies of natural products (e.g. hydrodistillation, steam distillation and subcritical water extraction) were also investigated to look at their effects on essential oil composition and antioxidants recovery from selected plants. Firstly, lists of forgotten or underutilized medicinal and aromatic plants in both regions were established according to the rules of selection. From the lists, six plants in the Midi-Pyrénées region (Tussilago farfara L., Calendula arvensis L., Robinia pseudoacacia L., Geranium robertianum L., Cytisus scoparius L. and Spartium junceum L.) and three plants in the Chongqing region (Tussilago farfara L., Citrum aurantium L. and Saussurea costus) were finally selected for investigations. Then the MAP-refinery was applied to the selected plants in two regions in order to realise their global valorisation. Volatile extracts composition in the roots of Tussilago farfara L. and Calendula arvensis L., as well as flower buds of Spartium junceum L. were firstly investigated. The main chemical compounds in volatile extract from Tussilago farfara L. roots were sesquiterpene hydrocarbons and aliphatic compounds while main chemical compounds in volatile extract from Calendula arvensis L. roots were oxygenated sesquiterpenes, oxygenated monoterpenes and oxygenated diterpenes. The volatile extract from flower buds of Spartium junceum L. was mainly composed of aliphatic compounds. Antioxidant capacity evaluation results (by DPPH, ABTS, FRAC, ORAC and Folin-Ciocalteu tests) showed that several plant samples like Cytisus scoparius L., Tussilago farfara L., Citrum aurantium L. and Robinia pseudoacacia L. could be potential sources of natural antioxidants. Comparisons of hydrodistillation (HD), steam distillation (SD) and subcritical water extraction (SWE) showed that HD and SD had limited effects on essential oil composition but HD, SD and SWE had significant impacts on the recovery of antioxidants. Hydrodistillation seemed to be a better method for recovery of antioxidant compounds from residues of distillation than steam distillation. However, SWE appeared to be a more efficient method for direct extraction of antioxidant molecules (or phenolic compounds) from plants. In the hydrodistillation process, mineral contents in water were found to have very limited effects on yields of extracts but calcium and bicarbonate ions, had significant decreasing effects on antioxidant capacity and total phenolic content of both aqueous and methanolic extracts. Finally, an improved MAP-refinery was developed. Subcritical water was used for further extraction of antioxidant compounds from residues in original MAP-refinery. In this way, five parts could be obtained from plant materials: volatile extract, aqueous extract, methanolic extract, subcritical water extract and the final residue. The results showed that the improved MAP-refinery significantly increased the recovery of antioxidants compared with original MAP-refinery. This promising process will also allow a better valorisation of the final solid residue due to the lower content of residual water

Caractérisations chimiques et biologiques d’extraits de plantes aromatiques et médicinales oubliées ou sous-utilisées de Midi-Pyrénées (France) et de Chongqing (Chine)

Les régions de Midi-Pyrénées (France) et de Chongqing (Chine) sont riches en plantes aromatiques et médicinales dites oubliées (ou médiévales). Afin de valoriser pleinement les différentes bio-molécules extractibles de ces plantes, le concept de MAP-raffinerie a été créé et appliqué à une sélection de plantes issues de ces deux régions. Plusieurs technologies d’extraction utilisant l’eau comme solvant vert (hydrodistillation, distillation à la vapeur et extraction par eau sub-critique) ont ainsi été employées et leur impact tant sur la composition des huiles essentielles que sur la récupération des molécules anti-oxydantes a été évalué. Dans un premier temps, une liste de plantes aromatiques et médicinales oubliées, voire sous-utilisées dans les deux régions a été établie selon des règles de sélection prédéfinies. Six plantes modèles de la région de Midi-Pyrénées (Tussilago farfara L., Calendula arvensis L., Robinia pseudoacacia L., Geranium robertianum L., Cytisus scoparius L. et Spartium junceum L.) et trois plantes de la région de Chongqing (Tussilago farfara L., Citrus aurantium L. et Saussurea costus) ont finalement été retenues. Puis, le concept de MAP-raffinerie a été appliqué à ces plantes afin d’étudier leur possible valorisation globale. L’étude des compositions chimiques des extraits volatils des racines de Tussilago farfara L. et de Calendula arvensis L., ainsi que des boutons de fleurs de Spartium junceum L. a été réalisée par GC et GC-MS pour la première fois. Les principaux composés chimiques dans l’extrait volatil de racines de Tussilago farfara L. étaient des hydrocarbures sesquiterpéniques et des composés aliphatiques tandis que les principaux composés chimiques dans l’extrait volatil de racines de Calendula arvensis L. étaient des sesquiterpènes oxygénés, des monoterpènes oxygénés et des diterpènes oxygénés. L’extrait volatil de boutons de fleurs de Spartium junceum L. était principalement composé de composés aliphatiques. Par ailleurs, les résultats de l’évaluation des capacités anti-oxydantes des extraits (par les tests DPPH, ABTS, FRAP, ORAC et Folin-Ciocalteu) ont montrés que plusieurs plantes comme Cytisus scoparius L., Tussilago farfara L., Citrus aurantium L. ou Robinia pseudoacacia L. pourraient être des sources potentielles d’anti-oxydants naturels. D’un point de vue technologique, les comparaisons de l’utilisation de l’hydrodistillation (HD), de la distillation à la vapeur (SD) et de l’extraction par eau sub-critique (SWE) ont montrées que si la HD et la SD ont des effets limités sur la composition des huiles essentielles, la HD semble être une méthode plus efficace pour la récupération des composés anti-oxydants à partir des résidus de distillation que la SD tandis que la SWE s’avère être une technologie prometteuse pour l’extraction directe de ces molécules à partir des plantes. Si la composition minérale de l’eau lors de l’hydrodistillation n’a que des effets très limités sur les rendements d’extraction, les teneurs en ions calcium et bicarbonate des eaux ont par contre des effets décroissants significatifs sur la capacité anti-oxydante et sur la teneur phénolique totale des extraits aqueux et méthanoliques. Au vue de ces résultats, un concept amélioré de MAP-raffinerie a été développé en intégrant une extraction à l’eau sub-critique pour l’extraction des composés anti-oxydants des résidus d’extraction primaire. Selon ce nouveau concept, cinq extraits peuvent être obtenus à partir des matières végétales: un extrait volatil, un extrait aqueux, un extrait méthanolique, un extrait à l’eau sub-critique et in fine un résidu solide. Les premiers résultats ont montrés que la "MAP-raffinerie améliorée" augmente de manière significative la récupération des antioxydants par rapport à la MAP-raffinerie originale et permet d’envisager une valorisation plus facile du résidu solide en agro-matériaux du fait de sa faible teneur en eau résiduelle. ABSTRACT : In both Midi-Pyrénées region (France) and Chongqing region (China), there are rich and underutilized medicinal and aromatic plants (MAP). Aiming at fully exploiting different molecules in these plants, the concept of MAP-refinery was developed and applied to several underutilized medicinal and aromatic plants in these two regions. Several water-based green extraction technologies of natural products (e.g. hydrodistillation, steam distillation and subcritical water extraction) were also investigated to look at their effects on essential oil composition and antioxidants recovery from selected plants. Firstly, lists of forgotten or underutilized medicinal and aromatic plants in both regions were established according to the rules of selection. From the lists, six plants in the Midi-Pyrénées region (Tussilago farfara L., Calendula arvensis L., Robinia pseudoacacia L., Geranium robertianum L., Cytisus scoparius L. and Spartium junceum L.) and three plants in the Chongqing region (Tussilago farfara L., Citrum aurantium L. and Saussurea costus) were finally selected for investigations. Then the MAP-refinery was applied to the selected plants in two regions in order to realise their global valorisation. Volatile extracts composition in the roots of Tussilago farfara L. and Calendula arvensis L., as well as flower buds of Spartium junceum L. were firstly investigated. The main chemical compounds in volatile extract from Tussilago farfara L. roots were sesquiterpene hydrocarbons and aliphatic compounds while main chemical compounds in volatile extract from Calendula arvensis L. roots were oxygenated sesquiterpenes, oxygenated monoterpenes and oxygenated diterpenes. The volatile extract from flower buds of Spartium junceum L. was mainly composed of aliphatic compounds. Antioxidant capacity evaluation results (by DPPH, ABTS, FRAC, ORAC and Folin-Ciocalteu tests) showed that several plant samples like Cytisus scoparius L., Tussilago farfara L., Citrum aurantium L. and Robinia pseudoacacia L. could be potential sources of natural antioxidants. Comparisons of hydrodistillation (HD), steam distillation (SD) and subcritical water extraction (SWE) showed that HD and SD had limited effects on essential oil composition but HD, SD and SWE had significant impacts on the recovery of antioxidants. Hydrodistillation seemed to be a better method for recovery of antioxidant compounds from residues of distillation than steam distillation. However, SWE appeared to be a more efficient method for direct extraction of antioxidant molecules (or phenolic compounds) from plants. In the hydrodistillation process, mineral contents in water were found to have very limited effects on yields of extracts but calcium and bicarbonate ions, had significant decreasing effects on antioxidant capacity and total phenolic content of both aqueous and methanolic extracts. Finally, an improved MAP-refinery was developed. Subcritical water was used for further extraction of antioxidant compounds from residues in original MAP-refinery. In this way, five parts could be obtained from plant materials: volatile extract, aqueous extract, methanolic extract, subcritical water extract and the final residue. The results showed that the improved MAP-refinery significantly increased the recovery of antioxidants compared with original MAP-refinery. This promising process will also allow a better valorisation of the final solid residue due to the lower content of residual water

Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain

Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models. Mappings of an NLP model's representations of and the brain activities evoked by linguistic input are typically deployed to reveal this symbiosis. However, two critical problems limit its advancement: 1) The model's representations (artificial neurons, ANs) rely on layer-level embeddings and thus lack fine-granularity; 2) The brain activities (biological neurons, BNs) are limited to neural recordings of isolated cortical unit (i.e., voxel/region) and thus lack integrations and interactions among brain functions. To address those problems, in this study, we 1) define ANs with fine-granularity in transformer-based NLP models (BERT in this study) and measure their temporal activations to input text sequences; 2) define BNs as functional brain networks (FBNs) extracted from functional magnetic resonance imaging (fMRI) data to capture functional interactions in the brain; 3) couple ANs and BNs by maximizing the synchronization of their temporal activations. Our experimental results demonstrate 1) The activations of ANs and BNs are significantly synchronized; 2) the ANs carry meaningful linguistic/semantic information and anchor to their BN signatures; 3) the anchored BNs are interpretable in a neurolinguistic context. Overall, our study introduces a novel, general, and effective framework to link transformer-based NLP models and neural activities in response to language and may provide novel insights for future studies such as brain-inspired evaluation and development of NLP models

Water-Soluble N-Acetyl-L-cysteine-Capped CdTe Quantum Dots Application for Hg(II) Detection

A simple, rapid, and specific method for Hg(II) detection has been proposed based on the fluorescence change of N-acetyl-Lcysteine-capped CdTe quantum dots (QDs). The presence of Hg(II) ions could quench the fluorescence of QDs at 565 nm and meanwhile produce new peak in 700-860 nm wavelength range. The linear response range is 20-430 nM with the detection limit at 8.0 nM Hg(II). It was found that the position of the new peak was irrelevant to the size of QDs. Furthermore, the mechanism of the quenching of QDs fluorescence by Hg(II) and the appearance of new peak in near-infrared area were also discussed and deduced through ultraviolet absorption spectrum, fluorescence spectrum, and X-ray photoelectron spectrum

Core-Periphery Principle Guided Redesign of Self-Attention in Transformers

Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques. Previous studies, by post-hoc analysis, have found that the best-performing ANNs surprisingly resemble biological neural networks (BNN), which indicates that ANNs and BNNs may share some common principles to achieve optimal performance in either machine learning or cognitive/behavior tasks. Inspired by this phenomenon, we proactively instill organizational principles of BNNs to guide the redesign of ANNs. We leverage the Core-Periphery (CP) organization, which is widely found in human brain networks, to guide the information communication mechanism in the self-attention of vision transformer (ViT) and name this novel framework as CP-ViT. In CP-ViT, the attention operation between nodes is defined by a sparse graph with a Core-Periphery structure (CP graph), where the core nodes are redesigned and reorganized to play an integrative role and serve as a center for other periphery nodes to exchange information. We evaluated the proposed CP-ViT on multiple public datasets, including medical image datasets (INbreast) and natural image datasets. Interestingly, by incorporating the BNN-derived principle (CP structure) into the redesign of ViT, our CP-ViT outperforms other state-of-the-art ANNs. In general, our work advances the state of the art in three aspects: 1) This work provides novel insights for brain-inspired AI: we can utilize the principles found in BNNs to guide and improve our ANN architecture design; 2) We show that there exist sweet spots of CP graphs that lead to CP-ViTs with significantly improved performance; and 3) The core nodes in CP-ViT correspond to task-related meaningful and important image patches, which can significantly enhance the interpretability of the trained deep model.Comment: Core-periphery, functional brain networks, Vi