Miracle-dz/UniFAR-FTUs huggingface dataset:
Adrenal Suppression in Children During the Treatment for Acute Lymphoblastic Leukemia Beyond Induction

Background: Adrenal suppression (AS) is an iatrogenic, life-threatening condition that can occur after glucocorticoid exposure. Despite recognition that AS occurs after induction phase treatment in children with acute lymphoblastic leukemia (ALL), the risk of AS in phases beyond induction is unknown. We conducted a pilot study in pediatric patients with ALL to ascertain whether the risk of AS persists in post-induction phases of treatment. Procedure: Patients diagnosed between 12 months to younger than 18 years with B or T-ALL and starting any new phase of treatment were eligible for the study. Relapsed or infant ALL were excluded. Low dose ACTH stimulation testing (LDST), measurement of albumin and cortisol binding globulin were performed in all patients. Screening for symptoms of AS was done. Results: Twenty-four patients enrolled in the study. One was diagnosed with clear AS. Five others had a borderline cortisol peak, representing possible mild AS. Symptoms were nonspecific and did not help distinguish patients with normal LDST from those with borderline or abnormal results. Conclusion: Patients on treatment for ALL continue to be at risk of AS beyond induction treatment. Although this risk appears small, physicians must be vigilant as patients may be asymptomatic but could develop adrenal crisis during treatment.
Economic Valuation for Cidanau Watershed Area, Indonesia

The paper describes economic valuation for the Cidanau watershed area of West Java in Indonesia. In this area natural resources deterioration has occurred even faster after the Asian Financial Crisis. The deforestation area and pronounced soil erosion seems to go unhindered because of land use competition among the residents for agricultural space, housing, etc. In order to prevent the area from further degradation, the purpose of this paper is to carry out quantitative evaluation which also attempts to raise the environmental awareness of residents, as well as visitors to the area. Questionnaire surveys were conducted and analyzed according to the Contingent Valuation Method (CVM) and the Travel Cost Method (TCM). The results show all respondents held good attitudes towards the efforts of environmental conservation, but responded negatively if they had to contribute to the environmental service payment. Visitors to the Anyer Beach acted differently because most of them come from faraway locations and have little knowledge of the watershed. However, the Anyer Beach recorded an environmental valuation of about Rp840 billion, which is a potential source for the service payment of Cidanau watershed.
3-min All-out Test to Evaluate Aerobic and Anaerobic Indexes in Court Team Sports

Abstract This study aimed to test the reproducibility of the 3-min all-out effort applied using shuttle running and compare its values to aerobic parameters. On the first day, 14 futsal players underwent an exhaustive test to determine the maximal incremental speed (MIS) and anaerobic threshold (AnT). On the second day, the participants performed the 3-min all-out effort (n=14), which was repeated after 48 h (third day) to test its reproducibility (n=11). Peak oxygen consumption (V̇ O 2PEAK ) and peak blood lactate concentrations ([La-]) were determined from 3-min all-out efforts performed through a 20-m shuttle run on the official court. The distance covered, mean speed, and critical speed (CS) during the 3-min all-out presented direct relationships with aerobic parameters determined through the incremental test (r>0.62). The distance covered above CS (D’) presented a direct relationship with peak lactate concentrations induced by a 3-min all-out effort (r=0.81). Despite the acceptable levels of reproducibility observed for most of the 3-min all-out variables, the minimal detectable change for D’ was high (72%). Our results demonstrated the potential use of mean speed to evaluate aerobic fitness. However, the applicability of the 3-min all-out shuttle run test to monitor training adaptations should be avoided, at least in nonexperienced athletes.
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks

The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary wireless networks, however: (i) the network may contain heterogeneous communication/computation resources, and (ii) there may be significant overlaps in devices' local data distributions. In this work, we develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading. Our optimization methodology aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy while minimizing data processing and D2D communication resource consumption subject to realistic constraints on the network topology and device capabilities. Theoretical analysis of the D2D offloading subproblem leads to new FedL convergence bounds and an efficient sequential convex optimizer. Using these results, we develop a sampling methodology based on graph convolutional networks (GCNs) which learns the relationship between network attributes, sampled nodes, and D2D data offloading to maximize FedL accuracy. Through evaluation on popular datasets and real-world network measurements from our edge testbed, we find that our methodology outperforms popular device sampling methodologies from literature in terms of ML model performance, data processing overhead, and energy consumption.
From Input to Output: A Multi-layer Knowledge Distillation Framework for Compressing Recommendation Models

To reduce the size of recommendation models, there have been many studies on compressing recommendation models using knowledge distillation. In this paper, we decompose recommendation models into three layers, i.e., the input layer, the intermediate layer, and the output layer, and address deficiencies layer by layer. First, previous methods focus only on two layers, neglecting the input layer. Second, in the intermediate layer, existing methods ignore the inconsistency of user preferences induced by the projectors. Third, in the output layer, existing methods use only hard labels rather than soft labels from the teacher. To address these deficiencies, we propose \textbf{M}ulti-layer \textbf{K}nowledge \textbf{D}istillation (MKD), which consists of three components: 1) Distillation with Neighbor-based Knowledge (NKD) utilizes the teacher's knowledge about entities with similar characteristics in the input layer to enable the student to learn robust representations. 2) Distillation with Consistent Preference (CPD) reduces the inconsistency of user preferences caused by projectors in the intermediate layer by two regularization terms. 3) Distillation with Soft Labels (SLD) constructs soft labels in the output layer by considering the predictions of both the teacher and the student. Our extensive experiments show that MKD even outperforms the teacher with one-tenth of the model size.
Uniqueness and statistical properties of the Gibbs state on general one-dimensional lattices with markovian structure

Let M be a compact metric space and X = M N , we consider a set of admissible sequences X A,I ⊂ X determined by a continuous function A : M × M → R and a compact set I ⊂ R . Given a Lipschitz continuous potential ϕ : X A,I → R , we prove uniqueness of the Gibbs state µ ϕ and we show that µ ϕ satisﬁes the so called Gibbs-Bowen property and a central limit theorem.
A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems

Spatial synchronization in roadside scenarios is essential for integrating data from multiple sensors at different locations. Current methods using cascading spatial transformation (CST) often lead to cumulative errors in large-scale deployments. Manual camera calibration is insufficient and requires extensive manual work, and existing methods are limited to controlled or single-view scenarios. To address these challenges, our research introduces a parallel spatial transformation (PST)-based framework for large-scale, multi-view, multi-sensor scenarios. PST parallelizes sensor coordinate system transformation, reducing cumulative errors. We incorporate deep learning for precise roadside monocular global localization, reducing manual work. Additionally, we use geolocation cues and an optimization algorithm for improved synchronization accuracy. Our framework has been tested in real-world scenarios, outperforming CST-based methods. It significantly enhances large-scale roadside multi-perspective, multi-sensor spatial synchronization, reducing deployment costs.
Gaussian Rough Paths Lifts via Complementary Young Regularity

Inspired by recent advances in singular SPDE theory, we use the Poincar\'e inequality on Wiener space to show that controlled complementary Young regularity is sufficient to obtain Gaussian rough paths lifts. This allows us to completely bypass assumptions on the 2D variation regularity of the covariance and, as a consequence, we obtain cleaner proofs of approximation statements (with optimal convergence rates) and show the convergence of random Fourier series in rough paths metrics under minimal assumptions on the coefficients (which are sharper than those in the existent literature).
DAG-Sword: A Simulator of Large-Scale Network Topologies for DAG-Oriented Proof-of-Work Blockchains

The blockchain brought interesting properties for many practical applications. However, some properties, such as the transaction processing throughput remained limited, especially in Proof-of-Work blockchains. Therefore, several promising directions, such as sharding designs and DAG-based protocols emerged. In this paper, we focus on DAG-based consensus protocols and present a discrete-event simulator for them. Our simulator can simulate realistic blockchain networks created from data of a Bitcoin network, while its network configuration and topology can be customized. The simulated network consists of honest and malicious miners. Malicious miners do not make any attack on consensus itself. Instead, they use a different transaction selection strategy than honest miners (who select transactions randomly) with the intention to earn unfairly more profits than honest miners at the cost of downgrading the protocol performance by duplicate transactions. As a consequence, this harms the performance of some DAG-based protocols (e.g., PHANTOM and GHOSTDAG) in terms of transaction processing throughput, which we demonstrate in our experiments and extend the results of the related work that contains a small-scale network of 10 nodes by the results obtained on a large-scale network with 7000 nodes. Next, we empirically compare different algorithms for the mempool structure, and we propose a composite mempool structure that is memory-efficient and thus convenient for simulations of resource-demanding large-scale networks.
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching

Semi-supervised entity alignment (EA) is a practical and challenging task because of the lack of adequate labeled mappings as training data. Most works address this problem by generating pseudo mappings for unlabeled entities. However, they either suffer from the erroneous (noisy) pseudo mappings or largely ignore the uncertainty of pseudo mappings. In this paper, we propose a novel semi-supervised EA method, termed as MixTEA, which guides the model learning with an end-to-end mixture teaching of manually labeled mappings and probabilistic pseudo mappings. We firstly train a student model using few labeled mappings as standard. More importantly, in pseudo mapping learning, we propose a bi-directional voting (BDV) strategy that fuses the alignment decisions in different directions to estimate the uncertainty via the joint matching confidence score. Meanwhile, we also design a matching diversity-based rectification (MDR) module to adjust the pseudo mapping learning, thus reducing the negative influence of noisy mappings. Extensive results on benchmark datasets as well as further analyses demonstrate the superiority and the effectiveness of our proposed method.
Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox

In the modern era, malware is experiencing a significant increase in both its variety and quantity, aligning with the widespread adoption of the digital world. This surge in malware has emerged as a critical challenge in the realm of cybersecurity, prompting numerous research endeavors and contributions to address the issue. Machine learning algorithms have been leveraged for malware detection due to their ability to uncover concealed patterns within vast datasets. However, deep learning algorithms, characterized by their multi-layered structure, surpass the limitations of traditional machine learning approaches. By employing deep learning techniques such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network), this study aims to classify and identify malware extracted from a dataset containing API call sequences. The performance of these algorithms is compared with that of conventional machine learning methods, including SVM (Support Vector Machine), RF (Random Forest), KNN (K-Nearest Neighbors), XGB (Extreme Gradient Boosting), and GBC (Gradient Boosting Classifier), all using the same dataset. The outcomes of this research demonstrate that both deep learning and machine learning algorithms achieve remarkably high levels of accuracy, reaching up to 99% in certain cases.
Beyond Tradition: Evaluating Agile feasibility in DO-178C for Aerospace Software Development

Domain-specific standards and guidelines play a crucial role in regulating safety-critical systems, with one notable example being the DO-178C document for the aerospace industry. This document provides guidelines for organisations seeking to ensure the safety and certification of their software systems. This paper analyses the DO-178C document within the context of software development for safety-critical aerospace systems focusing on Agile software development, aiming to assess its feasibility. Unlike restricting specific development methods, DO-178C offers indispensable support that upholds confidence in safety, aligning seamlessly with the objectives of aerospace industries. Our analysis reveals that there are no limitations or restrictions within the DO-178C that inhibit the adoption of Agile and provides guidelines and objectives for achieving suitable evidence, allowing for various working methods, including Agile methods, contrary to the overall opinion in the industry that the traditional waterfall method is mandatory. Additionally, we emphasise that the guidelines explanation is explicitly tailored to software professionals using Agile methods, giving it a much more specific focus than publications that only provide a generic overview of the standard.
Practical Parallel Algorithms for Near-Optimal Densest Subgraphs on Massive Graphs

The densest subgraph problem has received significant attention, both in theory and in practice, due to its applications in problems such as community detection, social network analysis, and spam detection. Due to the high cost of obtaining exact solutions, much attention has focused on designing approximate densest subgraph algorithms. However, existing approaches are not able to scale to massive graphs with billions of edges. In this paper, we introduce a new framework that combines approximate densest subgraph algorithms with a pruning optimization. We design new parallel variants of the state-of-the-art sequential Greedy++ algorithm, and plug it into our framework in conjunction with a parallel pruning technique based on $k$-core decomposition to obtain parallel $(1+\varepsilon)$-approximate densest subgraph algorithms. On a single thread, our algorithms achieve $2.6$--$34\times$ speedup over Greedy++, and obtain up to $22.37\times$ self relative parallel speedup on a 30-core machine with two-way hyper-threading. Compared with the state-of-the-art parallel algorithm by Harb et al. [NeurIPS'22], we achieve up to a $114\times$ speedup on the same machine. Finally, against the recent sequential algorithm of Xu et al. [PACMMOD'23], we achieve up to a $25.9\times$ speedup. The scalability of our algorithms enables us to obtain near-optimal density statistics on the hyperlink2012 (with roughly 113 billion edges) and clueweb (with roughly 37 billion edges) graphs for the first time in the literature.
On-the-Fly Static Analysis via Dynamic Bidirected Dyck Reachability

Dyck reachability is a principled, graph-based formulation of a plethora of static analyses. Bidirected graphs are used for capturing dataflow through mutable heap data, and are usual formalisms of demand-driven points-to and alias analyses. The best (offline) algorithm runs in O(m+n· α(n)) time, where n is the number of nodes and m is the number of edges in the flow graph, which becomes O(n2) in the worst case. In the everyday practice of program analysis, the analyzed code is subject to continuous change, with source code being added and removed. On-the-fly static analysis under such continuous updates gives rise to dynamic Dyck reachability, where reachability queries run on a dynamically changing graph, following program updates. Naturally, executing the offline algorithm in this online setting is inadequate, as the time required to process a single update is prohibitively large. In this work we develop a novel dynamic algorithm for bidirected Dyck reachability that has O(n· α(n)) worst-case performance per update, thus beating the O(n2) bound, and is also optimal in certain settings. We also implement our algorithm and evaluate its performance on on-the-fly data-dependence and alias analyses, and compare it with two best known alternatives, namely (i) the optimal offline algorithm, and (ii) a fully dynamic Datalog solver. Our experiments show that our dynamic algorithm is consistently, and by far, the top performing algorithm, exhibiting speedups in the order of 1000X. The running time of each update is almost always unnoticeable to the human eye, making it ideal for the on-the-fly analysis setting.
3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud

Greenhouse production of fruits and vegetables in developed countries is challenged by labor 12 scarcity and high labor costs. Robots offer a good solution for sustainable and cost-effective 13 production. Acquiring accurate spatial information about relevant plant parts is vital for 14 successful robot operation. Robot perception in greenhouses is challenging due to variations in 15 plant appearance, viewpoints, and illumination. This paper proposes a keypoint-detection-based 16 method using data from an RGB-D camera to estimate the 3D pose of peduncle nodes, which 17 provides essential information to harvest the tomato bunches. 18 19 Specifically, this paper proposes a method that detects four anatomical landmarks in the color 20 image and then integrates 3D point-cloud information to determine the 3D pose. A 21 comprehensive evaluation was conducted in a commercial greenhouse to gain insight into the 22 performance of different parts of the method. The results showed: (1) high accuracy in object 23 detection, achieving an Average Precision (AP) of AP@0.5=0.96; (2) an average Percentage of 24 Detected Joints (PDJ) of the keypoints of PhDJ@0.2=94.31%; and (3) 3D pose estimation 25 accuracy with mean absolute errors (MAE) of 11.38o and 9.93o for the relative upper and lower 26 angles between the peduncle and main stem, respectively. Furthermore, the capability to handle 27 variations in viewpoint was investigated, demonstrating the method was robust to view changes. 28 However, canonical and higher views resulted in slightly higher performance compared to other 29 views. Although tomato was selected as a use case, the proposed method is also applicable to 30 other greenhouse crops like pepper.
Fusionize++: Improving Serverless Application Performance Using Dynamic Task Inlining and Infrastructure Optimization

The Function-as-a-Service (FaaS) execution model increases developer productivity by removing operational concerns such as managing hardware or software runtimes. Developers, however, still need to partition their applications into FaaS functions, which is error-prone and complex: Encapsulating only the smallest logical unit of an application as a FaaS function maximizes flexibility and reusability. Yet, it also leads to invocation overheads, additional cold starts, and may increase cost due to double billing during synchronous invocations. Conversely, deploying an entire application as a single FaaS function avoids these overheads but decreases flexibility. In this paper we present Fusionize, a framework that automates optimizing for this trade-off by automatically fusing application code into an optimized multi-function composition. Developers only need to write fine-grained application code following the serverless model, while Fusionize automatically fuses different parts of the application into FaaS functions, manages their interactions, and configures the underlying infrastructure. At runtime, it monitors application performance and adapts it to minimize request-response latency and costs. Real-world use cases show that Fusionize can improve the deployment artifacts of the application, reducing both median request-response latency and cost of an example IoT application by more than 35%.
Forecasting volatility with machine learning and rough volatility: example from the crypto-winter

We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility. Our asset class of interest is cryptocurrencies, at the beginning of the “crypto-winter” in 2022. We first show that to forecast volatility, a universal LSTM approach trained on a pool of assets outperforms traditional models. We then consider a parsimonious parametric model based on rough volatility and Zumbach effect. We obtain similar prediction performances with only five parameters whose values are non-asset-dependent. Our findings provide further evidence on the universality of the mechanisms underlying the volatility formation process.
Validating ChatGPT Facts through RDF Knowledge Graphs and Sentence Similarity

Since ChatGPT offers detailed responses without justifications, and erroneous facts even for popular persons, events and places, in this paper we present a novel pipeline that retrieves the response of ChatGPT in RDF and tries to validate the ChatGPT facts using one or more RDF Knowledge Graphs (KGs). To this end we leverage DBpedia and LODsyndesis (an aggregated Knowledge Graph that contains 2 billion triples from 400 RDF KGs of many domains) and short sentence embeddings, and introduce an algorithm that returns the more relevant triple(s) accompanied by their provenance and a confidence score. This enables the validation of ChatGPT responses and their enrichment with justifications and provenance. To evaluate this service (such services in general), we create an evaluation benchmark that includes 2,000 ChatGPT facts; specifically 1,000 facts for famous Greek Persons, 500 facts for popular Greek Places, and 500 facts for Events related to Greece. The facts were manually labelled (approximately 73% of ChatGPT facts were correct and 27% of facts were erroneous). The results are promising; indicatively for the whole benchmark, we managed to verify the 85.3% of the correct facts of ChatGPT and to find the correct answer for the 58% of the erroneous ChatGPT facts.