Miracle-dz/UniFAR-FTUs huggingface dataset:
Reporting Metabolite Data

The last decade has witnessed dramatic advances in our ability to determine the levels of an ever broadening range of metabolites. That said, while transcriptomic approaches provide almost complete coverage (González-Ballester et al., 2010) and proteomics approaches are now capable of detecting upwards of 45% of the cellular protein complement (Baerenfaller et al., 2008), metabolomics is currently capable of determining only a small fraction of the metabolites found in any one cell (Saito and Matsuda, 2010). It has been estimated that at least 200,000 different metabolites occur across the plant kingdom, and between 7000 and 15,000 within an individual species (D’Auria and Gershenzon, 2005; Fernie, 2007). However, to date, even the most comprehensive methods detect only between 1000 and 2000 molecular features, which are putatively indicative of bona fide chemical entities synthesized in plant tissues (Aharoni et al., 2002; Giavalisco et al., 2009; Iijima et al., 2008). Metabolite measurements are further complicated by the chemical diversity of metabolites and their broad dynamic range in cellular abundance. These features currently prohibit the possibility of extracting and measuring all metabolites using single extraction and analytical procedures. Consequently, many different extraction techniques and combinations of analytical methods are employed in attempts to achieve adequate metabolite coverage (Lisec et al., 2006; De Vos et al., 2007; Kruger et al., 2008; Tohge and Fernie, 2010). This, in turn, renders the establishment of good working practices more difficult than those, for example, associated with quantitative RT-PCR (Udvardi et al., 2008). This is exacerbated by the breadth of aims of metabolite analyses that encompass targeted metabolite analysis, metabolite profiling, metabolomic-scale analyses, and metabolite fingerprinting techniques (Fiehn, 2002). As a result of the diversity in aims and methodologies of metabolite analyses, it is particularly important to define guidelines for obtaining and reporting metabolite data, since there are so many potential sources of error or misinterpretation. Our aim is to highlight potential sources of error and provide recommendations to ensure the robustness of the metabolite data obtained and reported. Our recommendations includemethods for sampling, extraction and storage, metabolite identification, processing of large sample numbers, and recommendations for reporting the methods of metabolite identification and the levels of certainty in metabolite quantification. Good suggestions for standards in reporting chemical ontology and supporting metadata have already been made by Sumner et al. (2007) and Bais et al. (2010).
Virtual supply chain re-intermediation through multi-agent systems

Abstract : Intelligent agent technology offers good potential to integrate supply chain processes and other business-to-business exchanges more closely than current Web technologies and without the rigid inflexibility associated with EDI applications. Viewing respective procurement and order fulfillment processes of buyer and seller as an integrated whole along the supply chain, we identify opportunities for virtual supply chain re-intermediation through multi-agent systems. Using a proof-concept agent federation, called the Intelligent Mall, we examine the feasibility and performance implications of this approach for a software supply chain. A long-lived, autonomous agent federation is observed performing delegated supply chain tasks as prescribed and in an intelligent, context-sensitive and policy-conforming manner. The kind of agent-integrated supply chain investigated through this work appears to be inherently scalable yet customizable to the level of a specific individual in the organization. This approach to supply chain re-intermediation also appears to generalize well to a wide variety of corporations, universities and other enterprise forms, and the intelligent mall examined in this report appears to offer good potential for improved process performance. This report summarizes exploratory results from experience with the Intelligent Mall and outlines a number of lessons learned and guidelines for implementing multi-agent systems. The conclusions drawn from this investigation are important for executives and managers considering leading adoption of agent technology, and the research agenda is intended to help guide and stimulate continued work along these lines.
Specification and Application of a Taxonomy for Task Models in Model-Based User Interface Development Environments

—This paper presents a taxonomy allowing for the evaluation of task models with a focus on their applicability in model-based user interface development processes. Task models are explicit representations of all user tasks which can be achieved through a user interface. It further supports the verification and improvement of existing task models, and provides developers with a decision-making aid for the selection of the most suitable task model for their development process or project. The taxonomy is applied on the Useware Markup Language 1.0, the ConcurTaskTrees notation and the AMBOSS notation. The results of the application are briefly described in this paper which led to the identification of substantial improvement potentials for the Useware Markup Language.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology

In this project, the Delay-Sensitive data capacity of the Wireless fading channel is explored under two distinct suppositions on the accessible transmitter channel state data (CSI). The principal situation accept consummate earlier learning of both the fundamental and spy channel picks up. Here, upper and lower limits on the protected delay constrained limit are inferred and appeared to be tight in the high flag to-clamor proportion (SNR) administration (for a wide class of channel circulations). In the second situation, just the principle channel CSI is thought to be accessible at the transmitter. Amazingly, under this suspicion, I build up the achievability of non-zero secure rate (for a wide class of channel circulations) under a strict delay limitation. In the two cases, our achievability contentions depend on a novel two phase approach that defeats the mystery blackout wonder saw in before works.
Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments

Microblogs have become a social platform for people to express their emotions in real-time, and it is a trend to analyze user emotional tendencies from the information on Microblogs. The dynamic features of emojis can affect the sentiment polarity of microblog texts. Since existing models seldom consider the diversity of emoji sentiment polarity, the paper propose a microblog sentiment classification model based on ALBERT-FAET. We obtain text embedding via ALBERT pretraining model and learn the inter-emoji embedding with an attention-based LSTM network. In addition, a fine-grained attention mechanism is proposed to capture the word-level interactions between plain text and emoji. Finally, we concatenate these features and feed them into a CNN classifier to predict the sentiment labels of the microblogs. To verify the effectiveness of the model and the fine-grained attention network, we conduct comparison experiments and ablation experiments. The comparison experiments show that the model outperforms previous methods in three evaluation indicators (accuracy, precision, and recall) and the model can significantly improve sentiment classification. The ablation experiments show that compared with ALBERT-AET, the proposed model ALBERT-FAET is better in the metrics, indicating that the fine-grained attention network can understand the diversified information of emoticons.
Ju n 20 22 Dissolution Flow Instabilities in Coffee Brewing

A recent experiment showed that, contrary to theoretical predictions, beyond a cutoﬀ point grinding coﬀee more ﬁnely results in lower extraction. One potential explanation for this is that ﬁne grinding promotes nonuniform extraction in the coﬀee bed. We investigate the possibility that this could occur due to an instability involving dissolution and ﬂow. A low dimensional model in which there are two possible pathways for ﬂow is formulated and analysed. This model reproduces the qualitative trend observed due to combination of a ﬂow instability and saturation of extraction in one pathway. This strongly suggests that a ﬂow instability can explain the observed decrease in extraction.
Multiagent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing

This paper proposes an effective and novel multi-agent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), where multiple service requests with differentiated demands are delivered at the same time. The differentiated demands of the service requests are reflected by their delay- and cost-sensitive factors. We first construct a VNF P&R problem to jointly minimize a weighted sum of service delay and resource consumption cost, which is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative subtasks: placement subtask and routing subtask. Each subtask consists of multiple concurrent parallel sequential decision processes. By invoking the deep deterministic policy gradient method and multi-agent technique, an MADRL-P&R framework is designed to perform the two subtasks. The new joint reward and internal rewards mechanism is proposed to match the goals and constraints of the placement and routing subtasks. We also propose the parameter migration-based model-retraining method to deal with changing network topologies. Corroborated by experiments, the proposed MADRL-P&R framework is superior to its alternatives in terms of service cost and delay, and offers higher flexibility for personalized service demands. The parameter migration-based model-retraining method can efficiently accelerate convergence under moderate network topology changes.
Improved bounds for 1-independent percolation on $\mathbb{Z}^n$

A 1-independent bond percolation model on a graph G is a probability distribution on the spanning subgraphs of G in which, for all vertex-disjoint sets of edges S 1 and S 2 , the states of the edges in S 1 are independent of the states of the edges in S 2 . Such a model is said to percolate if the random subgraph has an inﬁnite component with positive probability. In 2012 the ﬁrst author and Bollobás deﬁned p max ( G ) to be the supremum of those p for which there exists a 1-independent bond percolation model on G in which each edge is present in the random subgraph with probability at least p but which does not percolate. A fundamental and challenging problem in this area is to determine the value of p max ( G ) when G is the lattice graph Z 2 . Since p max ( Z n ) ≤ p max ( Z n − 1 ) , it is also of interest to establish the value of lim n →∞ p max ( Z n ) . In this paper we signiﬁcantly improve the best known upper bound on this limit and obtain better upper and lower bounds on p max ( Z 2 ) . In proving these results, we also give an upper bound on the critical probability for a 1-independent model on the hypercube graph to contain a giant component asymptotically almost surely.
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias

Deep learning models are able to approximate one specific dynamical system but struggle at learning generalisable dynamics, where dynamical systems obey the same laws of physics but contain different numbers of elements (e.g., double- and triple-pendulum systems). To relieve this issue, we proposed the Modular Lagrangian Network (ModLaNet), a structural neural network framework with modularity and physical inductive bias. This framework models the energy of each element using modularity and then construct the target dynamical system via Lagrangian mechanics. Modularity is beneficial for reusing trained networks and reducing the scale of networks and datasets. As a result, our framework can learn from the dynamics of simpler systems and extend to more complex ones, which is not feasible using other relevant physics-informed neural networks. We examine our framework for modelling double-pendulum or three-body systems with small training datasets, where our models achieve the best data efficiency and accuracy performance compared with counterparts. We also reorganise our models as extensions to model multi-pendulum and multi-body systems, demonstrating the intriguing reusable feature of our framework.
Unsupervised Sentence Simplification via Dependency Parsing

Text simplification is the task of rewriting a text so that it is readable and easily understood. In this paper, we propose a simple yet novel unsupervised sentence simplification system that harnesses parsing structures together with sentence embeddings to produce linguistically effective simplifications. This means our model is capable of introducing substantial modifications to simplify a sentence while maintaining its original semantics and adequate fluency. We establish the unsupervised state-of-the-art at 39.13 SARI on TurkCorpus set and perform competitively against supervised baselines on various quality metrics. Furthermore, we demonstrate our framework's extensibility to other languages via a proof-of-concept on Vietnamese data. Code for reproduction is published at \url{https://github.com/isVy08/USDP}.
Affinity-Aware Graph Networks

Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. Owing to the relatively limited number of message passing steps they perform -- and hence a smaller receptive field -- there has been significant interest in improving their expressivity by incorporating structural aspects of the underlying graph. In this paper, we explore the use of affinity measures as features in graph neural networks, in particular measures arising from random walks, including effective resistance, hitting and commute times. We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks. Our architecture has lower computational complexity, while our features are invariant to the permutations of the underlying graph. The measures we compute allow the network to exploit the connectivity properties of the graph, thereby allowing us to outperform relevant benchmarks for a wide variety of tasks, often with significantly fewer message passing steps. On one of the largest publicly available graph regression datasets, OGB-LSC-PCQM4Mv1, we obtain the best known single-model validation MAE at the time of writing.
Ion-native variational ansatz for quantum approximate optimization

Variational quantum algorithms involve training parameterized quantum circuits using a classi-cal co-processor. An important variational algorithm, designed for combinatorial optimization, is the quantum approximate optimization algorithm. Realization of this algorithm on any modern quantum processor requires either embedding a problem instance into a Hamiltonian or emulating the corresponding propagator by a gate sequence. For a vast range of problem instances this is impossible due to current circuit depth and hardware limitations. Hence we adapt the variational approach—using ion native Hamiltonians—to create ansatze families that can prepare the ground states of more general problem Hamiltonians. We analytically determine symmetry protected classes that make certain problem instances inaccessible unless this symmetry is broken. We exhaustively search over six qubits and consider upto twenty circuit layers, demonstrating that symmetry can be broken to solve all problem instances of the Sherrington-Kirkpatrick Hamiltonian. Going further, we numerically demonstrate training convergence and level-wise improvement for up to twenty qubits. Speciﬁcally these ﬁndings widen the class problem instances which might be solved by ion based quantum processors. Generally these results serve as a test-bed for quantum approximate optimization approaches based on system native Hamiltonians and symmetry protection.
VIP Hashing - Adapting to Skew in Popularity of Data on the Fly

All data is not equally popular. Often, some portion of data is more frequently accessed than the rest, which causes a skew in popularity of the data items. Adapting to this skew can improve performance, and this topic has been studied extensively in the past for disk-based settings. In this work, we consider an in-memory data structure, namely hash table , and show how one can leverage the skew in popularity for higher performance. Hashing is a low-latency operation, sensitive to the effects of caching and code complexity, among other factors. These factors make learning in-the-loop challenging as the overhead of performing additional operations can have significant impact on performance. In this paper, we propose VIP hashing, a hash table method that uses lightweight mechanisms for learning the skew in popularity and adapting the hash table layout on the fly. These mechanisms are non-blocking, i.e, the hash table is operational at all times. The overhead is controlled by sensing changes in the popularity distribution to dynamically switch-on/off the mechanisms as needed. We ran extensive tests against a host of workloads generated by Wiscer , a homegrown benchmarking tool, and we find that VIP hashing improves performance in the presence of skew (22% increase in fetch operation throughput for a hash table with 1M keys under low skew) while adapting to insert and delete operations, and changing popularity distribution of keys on the fly. Our experiments on DuckDB show that VIP hashing reduces the end-to-end execution time of TPC-H query 9 by 20% under low skew.
On the Optimisation of the GSACA Suffix Array Construction Algorithm

The suffix array is arguably one of the most important data structures in sequence analysis and consequently there is a multitude of suffix sorting algorithms. However, to this date the GSACA algorithm introduced in 2015 is the only known non-recursive linear-time suffix array construction algorithm (SACA). Despite its interesting theoretical properties, there has been little effort in improving the algorithm's subpar real-world performance. There is a super-linear algorithm DSH which relies on the same sorting principle and is faster than DivSufSort, the fastest SACA for over a decade. This paper is concerned with analysing the sorting principle used in GSACA and DSH and exploiting its properties in order to give an optimised linear-time algorithm. Our resulting algorithm is not only significantly faster than GSACA but also outperforms DivSufSort and DSH.
Mathematical modeling of multilane vehicular traffic by discrete kinetic theory approach

This paper deals with the modeling and numerical simulations of multilane vehicular traffic according to the discrete kinetic theory approach. The nonlinear additive interactions and external actions such as tollgates as well traffic signs are considered and modeled. The well-posedness of the related Cauchy problem for the spatially homogeneous case has been proved by using Banach fixed-point theory. Numerical simulations are carried out to validate the improved model in the cases of spatially homogeneous and inhomogeneous problems.
PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis

This paper presents a new multimodal interventional radiology dataset, called PoCaP (Port Catheter Placement) Corpus. This corpus consists of speech and audio signals in German, X-ray images, and system commands collected from 31 PoCaP interventions by six surgeons with average duration of 81 . 4 ± 41 . 0 minutes. The corpus aims to provide a resource for developing a smart speech assistant in operating rooms. In particular, it may be used to develop a speech-controlled system that enables surgeons to control the operation parameters such as C-arm movements and table positions. In order to record the dataset, we acquired consent by the institutional review board and workers’ council in the University Hospital Erlangen and by the patients for data privacy. We describe the recording set-up, data structure, workﬂow and preprocessing steps, and report the ﬁrst PoCaP Corpus speech recognition analysis results with 11.52% word error rate using pretrained models. The ﬁndings suggest that the data has the potential to build a robust command recognition system and will allow the development of a novel intervention support systems using speech and image in the medical
The SPHINX M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive Fundamental M-dwarf Properties

About 70%–80% of stars in our solar and Galactic neighborhood are M dwarfs. They span a range of low masses and temperatures relative to solar-type stars, facilitating molecule formation throughout their atmospheres. Standard stellar atmosphere models primarily designed for FGK stars face challenges when characterizing broadband molecular features in spectra of cool stars. Here, we introduce SPHINX—a new 1D self-consistent radiative–convective thermochemical equilibrium chemistry model grid of atmospheres and spectra for M dwarfs in low resolution (R ∼ 250). We incorporate the latest precomputed absorption cross sections with pressure broadening for key molecules dominant in late-K, early/main-sequence-M stars. We then validate our grid models by determining fundamental properties (T eff, log g, [M/H], radius, and C/O) for 10 benchmark M+G binary stars with known host metallicities and 10 M dwarfs with interferometrically measured angular diameters. Incorporating the Gaussian process inference tool Starfish, we account for correlated and systematic noise in low-resolution (spectral stitching of SpeX, SNIFS, and STIS) observations and derive robust estimates of fundamental M-dwarf atmospheric parameters. Additionally, we assess the influence of photospheric heterogeneity on inferred [M/H] and find that it could explain some deviations from observations. We also probe whether the adopted convective mixing length parameter influences inferred radii, effective temperature, and [M/H] and again find that may explain discrepancies between interferometric observations and model-derived parameters for cooler M dwarfs. Mainly, we show the unique strength in leveraging broadband molecular absorption features occurring in low-resolution M dwarf spectra and demonstrate the ability to improve constraints on fundamental properties of exoplanet hosts and brown-dwarf companions.