Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithm 103: Simpson's rule integrator

procedure ari thmetic (a, b, c, op); in t eger a, b, c, op; ¢ o n l m e n t This procedure will perform different order ar i thmetic operations with b and c, put t ing the result in a. The order of the operation is given by op. For op = 1 addit ion is performed. For op = 2 multiplicaLion, repeated addition, is done. Beyond these the operations are non-commutat ive. For op = 3 exponentiat ion, repeated multiplication, is done, raising b to the power c. Beyond these the question of grouping is important . The innermost implied parentheses are at the right. The hyper-exponent is always c. For op = 4 te t ra t ion, repeated exponentiat ion, is done. For op = 5, 6, 7, etc., the procedure performs pentat ion, hexation, heptat ion, etc., respectively. The routine was originally programmed in FORTRAN for the Control Data 160 desk-size computer. The original program was limited to te t ra t ion because subroutine recursiveness in Control Data 160 FORTRAN has been held down to four levels in the interests of economy. The input parameter , b, c, and op, must be positive integers, not zero; b e g i n own i n t e g e r d, e, f, drop; i f o p = 1 t h e n b e g i n a := h-4c; go t o l e n d i f o p = 2 t h e n d := 0; else d := 1; e := c; drop := op 1; for f := I s t e p 1 u n t i l e do b e g i n ari thmetic (a, b, d, drop);
Neurovascular Coupling: A Unifying Theory for Post-Concussion Syndrome Treatment and Functional Neuroimaging

Post-concussion syndrome (PCS) occurs in a significant percentage of concussion patients and is defined as having a history of traumatic brain injury with persistence of three or more symptoms. Standard structural clinical neuroimaging studies show no abnormal findings for the majority of PCS patients as opposed to functional MRI, which often reveals irregularities in the blood-oxygen level dependent (BOLD) signal. This suggests that dysregulation of neurovascular coupling, which causes abnormal BOLD signals, plays a significant role in PCS pathology. However, compared to the pathophysiologic mechanisms occurring in acute concussion, the underlying neuropathophysiology of chronic concussive sequelaeor PCS is less understood, though becoming clearer with emerging research. We present a treatment approach grounded in the physiological theory presented here called Enhanced Performance in Cognition (EPIC), which has shown strong clinical success. Dysregulation of neurovascular coupling (NVC), along with disruptions in cerebrovascular reactivity (CVR) and autonomic nervous system (ANS) dysregulation are the targets of EPIC treatment. Success of the approach itself tentatively supports the hypothesis that these three features figure prominently in the neuropathophysiology of PCS. The aim of this report is to provide a theory of the underlying mechanisms of PCS pathology and its treatment that is in accord with the current corpus of research and explains the recent therapeutic success seen in PCS patient using the EPIC treatment. We propose a novel theory concerning the mechanisms by which NVC dysregulation is normalized. This includes focused, intense and repetitive neurocognitive challenges during post-exercise cognitive boost and the avoidance of intracerebral steal in the setting of restored and re-regulated CVR and ANS. It is intended that both the theory and treatment approach are presented explicitly enough to generate empirical studies with clear hypothetical predictions from the theory as well as clinical innovations with significant relevance to improve current practices.
4 (cid:22) m spectra of AGB stars I. Observations

We present times series of high resolution spectra of AGB variables at 4 (cid:22) m. Line pro(cid:12)les from the major contributors to the spectra of oxygen-rich stars at 4 (cid:22) m, OH, H 2 O, HCl and SiO, are examined. The velocity as well as shape variations of these pro(cid:12)les with time are discussed. The line pro(cid:12)les investigated frequently have emission and multiple absorption components. The changes with time of the 4 (cid:22) m region lines do not always follow the cyclic variability seen in NIR spectra and in the photometric light curve. We interpret and discuss the results qualitatively considering comparing the spectral variability with that of the well behaved 1.6 (cid:22) m region and of dynamical model atmospheres. Miras and semiregular variables are compared. The origins of non-periodic behavior are discussed, including the role of spatial inhomogeneities in the stellar atmosphere.
Enhancing pose estimation through eﬀicient patch synthesis

Estimating the pose of a camera from a scene model is a challenging problem when the camera is in a position not covered by the views used to build the model, because feature matching is difﬁcult. Several viewpoint simulation techniques have been recently proposed in this context. They generally come with a high computational cost, are limited to speciﬁc scenes such as urban environments or object-centred scenes, or need an initial pose guess. This paper presents a viewpoint simulation method well suited to most scenes and query views. Two major problems are addressed: the positioning of the virtual viewpoints with respect to the scene, and the synthesis of geometrically consistent patches. Experiments show that patch synthesis dramatically improves the accuracy of the pose in case of difﬁcult registration, with a limited computational cost.
Future Timelines: Extraction and Visualization of Future-related Content From News Articles

In today’s rapidly evolving world, maintaining a comprehensive overview of the future landscape is essential for staying competitive and making informed decisions. However, given the large volume of daily news, manually obtaining a thorough overview of an entity’s future prospects is quite challenging. To address this, we present a system designed to automatically extract and summarize future-related information of a queried entity from news articles. Our approach utilizes a novel and publicly accessible multi-source dataset comprising 6,800 annotated sentences to fine-tune a language model to identify future-related sentences. We then use topic modeling to extract the main topics from the data and rank them by relevance as well as present them on an interactive timeline. User evaluations have shown that the timelines and summaries our system produces are useful. The system is available as a web application at: https://chronicle2050.regevson. com.
Eﬀicient Recovery from False State in Distributed Routing Algorithms

Malicious and misconﬁgured nodes can inject incorrect state into a distributed system, which can then be propagated system-wide as a result of normal network operation. Such false state can degrade the performance of a distributed system or render it unusable. For example, in the case of network routing algorithms, false state corresponding to a node incorrectly declaring a cost of 0 to all destinations (maliciously or due to misconﬁguration) can quickly spread through the network. This causes other nodes to (incorrectly) route via the misconﬁgured node, resulting in suboptimal routing and network congestion. We propose three algorithms for eﬃcient recovery in such scenarios and prove the correctness of each of these algorithms. Through simulation, we evaluate our algorithms – in terms of message and time overhead – when applied to removing false state in distance vector routing. Our analysis shows that over topologies where link costs remain ﬁxed and for the same topologies where link costs change, a recovery algorithm based on system-wide checkpoints and a rollback mechanism yields superior performance when using the poison reverse optimization.
Optimal Control for Minimum Fuel Pinpoint Landing

Autonomous landing is a modern problem in the Aerospace industry, inspired by the desire for reusable launch vehicles to reduce cost to orbit. Major innovations in this field were made in the early 2010s, where fuel and landing optimal trajectories were designed using Convex Optimization in reference to the Mars landing problem. This project starts by revisiting these innovations in the guidance problem and then expands the work to include more sophisticated dynamics, including drag and attitude dynamics. We also address the tracking problem to ensure that the vehicle follows the prescribed trajectory in the presence of disturbances. Code for this project can be found in our GitHub repository. Additionally, a video presentation can be found on Google Drive.
SegmentationAutomated Pre-and Postoperative Glioma Based Pipeline for - of a Deep Learning Development and Practical Implementation

BACKGROUND AND PURPOSE: Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treat-ment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentation with a pipeline for clinical implementation. Developed and engineered in concert, the work seeks to accelerate clinical realization of such tools. MATERIALS AND METHODS: A deep learning model, autoencoder regularization – cascaded anisotropic, was developed, trained, and tested fusing key elements of autoencoder regularization with a cascaded anisotropic convolutional neural network. We constructed a dataset consisting of 437 cases with 40 cases reserved as a held-out test and the remainder split 80:20 for training and validation. We performed data augmentation and hyperparameter optimization and used a mean Dice score to evaluate against baseline models. To facilitate clinical adoption, we developed the model with an end-to-end pipeline including routing, preprocessing, and end-user interaction. RESULTS: The autoencoder regularization – cascaded anisotropic model achieved median and mean Dice scores of 0.88/0.83 (SD, 0.09), 0.89/0.84 (SD, 0.08), and 0.81/0.72 (SD, 0.1) for whole-tumor, tumor core/resection cavity, and enhancing tumor subregions, respectively, including both preoperative and postoperative follow-up cases. The overall total processing time per case was (cid:1) 10minutes, including data routing ( (cid:1) 1 minute), preprocessing ( (cid:1) 6 minute), segmentation ( (cid:1) 1 – 2 minute), and postprocessing ( (cid:1) 1 minute). Implementation challenges were discussed. CONCLUSIONS: We show the feasibility and advantages of building a coordinated model with a clinical pipeline for the rapid and accurate deep learning segmentation of both preoperative and postoperative gliomas. The ability of the model to accommodate cases of postoperative glioma is clinically important for follow-up. An end-to-end approach, such as used here, may lead us toward successful clinical translation of tools for quantitative volume measures for glioma
Improving Network Intrusion Detection Performance : An Empirical Evaluation Using Extreme Gradient Boosting (XGBoost) with Recursive Feature Elimination

In cybersecurity, Network Intrusion Detection Systems (NIDS) are essential for identifying and preventing malicious activity within computer networks. Machine learning algorithms have been widely applied to NIDS due to their ability to identify complex patterns and anomalies in network traffic. Improvements in the performance of an IDS can be measured by increasing the Matthew Correlation Coefficient (MCC), the reduction of False Alarm Rates (FARs), and the maintenance of up-to-date signatures of the latest attacks to maintain confidentiality, integrity, and availability of services. Integrating machine learning with feature selection for IDSs can help eliminate less important features until the optimal subset of features is achieved, thus improving the NIDS.In this research, we propose an approach for NIDS using XGBoost, a popular gradient boosting algorithm, with Recursive Feature Elimination (RFE) feature selection. We used the NSL-KDD dataset, a benchmark dataset for evaluating NIDS, for training and testing. Our empirical results show that XGBoost with RFE outperforms other popular machine learning algorithms for NIDS on this dataset, achieving the highest MCC for detecting NSL-KDD dataset attacks of type DoS, Probe, U2R, and R2L and very high classification time.
Sequential Quadratic Programming for Nonlinear Problems with Cardinality Constraints

Nonlinear optimization problems with cardinality constraints and risk measurements, particularly in the fields such as finance and risk management, have posed significant challenges. Portfolio optimization under these constraints requires effective algorithms and methodologies to improve performance. Thus, this study aimed to investigate in nonlinear optimization problems with cardinality constraints and risk measurements in fields like finance and risk management. Indeed, by using various algorithms and methodologies, this study aimed to improve the performance of portfolio optimization under these constraints. To measurement risk in portfolio programing, deemed as an application in nonlinear problems with cardinality constraints, VaR (value at risk), CVaR (conditional value at risk), RVaR (robust value at risk), and RCVaR (robust conditional value at risk) were used. Regularization techniques played a crucial role in optimizing non-smooth problems, addressing challenges arising from the non-smoothness of the constraints, thereby enhancing algorithm performance. By using some regularization algorithms, such as Scholtes, Kanzow-Schwartz, and Bordakov methods, an improvement in performance was obtained. Notably, sequential quadratic programming (SQP), which is an iterative optimization technique, was used to solve nonlinear programming problems by approximating the objective and constraints using quadratic functions. After a comparison of algorithms’ performance was explored, the experiments indicated a significant enhancement in the performance of the SQP algorithm by using the Scholtes regularization technique. Thus, these findings show that SQP can be a promise method to solving this classification of cardinality-constraints nonlinear problems. The study introduces innovative optimization solutions to improve algorithmic performance in complex problems, contributing to advancements in finance and risk management approaches.
Low-Resource Domain Adaptation for Jihadi Discourse Tackling Low-Resource Domain Adaptation for Neural Machine Translation Using Real and Synthetic Data

In this thesis, I explore the problem of low-resource domain adaptation for jihadi discourse. Due to the limited availability of annotated parallel data, developing accurate and eﬀective models in this domain poses a challenging task. To address this issue, I propose a method that leverages a small in-domain manually created corpus and a synthetic corpus created from monolingual data using back-translation. I evaluate the approach by ﬁne-tuning a pre-trained language model on diﬀerent proportions of real and synthetic data and measuring its performance on a held-out test set. My experiments show that ﬁne-tuning a model on one-ﬁfth real parallel data and synthetic parallel data eﬀectively reduces occurrences of over-translation and bolsters the model’s ability to translate in-domain terminology. My ﬁndings suggest that synthetic data can be a valuable resource for low-resource domain adaptation, especially when real parallel data is diﬃcult to obtain. The proposed method can be extended to other low-resource domains where annotated data is scarce, potentially leading to more accurate models and better translation of these domains.
Rapid degassing in basaltic sills as a source of Deep Long Period volcanic earthquakes

In this paper, we present numerical modeling aimed to explain Deep Long Period (DLP) events occurring in middle-to-lower crust beneath volcanoes and often observed in association with volcanic eruptions or their precursors. We consider a DLP generating mechanism caused by the rapid growth of gas bubbles in response to the slow decompression of H \ textsubscript { 2 } O– CO \ textsubscript { 2 } over-saturated magma. The nucleation and rapid growth of gas bubbles lead to rapid pressure change in the magma and elastic rebound of the host rocks, radiating seismic waves recorded as DLP events. The magma and host rocks are modeled as Maxwell bodies with diﬀerent relaxation times and elastic moduli. Simulations of a single sill-shaped intrusion with diﬀerent parameters demonstrate that realistic amplitudes and frequencies of P and S seismic waves can be obtained when considering intrusions with linear sizes of the order of 100 m. We then consider a case of two closely located sills and model their interaction. We speculate on conditions that can result in consecutive triggering of the bubble growth in multiple closely located batches of magma, leading to the generation of earthquake swarms or seismic tremors
Orthopnea and inspiratory e¡ort in chronic heart failure patients

Study objective: Orthopnea is a typical feature of patients with chronic heartfailure (CHF), the factors contributing to it are not completely understood.We investigated changes in dyspnea and other respiratory variables, induced by altering posture (from sitting to supine) in 11 CHF patients (NYHA classes II^IV) and 10 control subjects. Methods and Results: We measured dyspnea (Borg scale), the diaphragm pressure time product per minute (PTPdi/m, indexof metabolic consumption), and mechanicalproperties ofthe lung (lung compliance ( C , L ) and resistances ( R , L ). CHF patients also underwent atrialofnon-invasivemechanicalventilation (NIMV) inthe supine positionin order to ascertain whether unloading theinspiratorymusclescould somehowrelieve dyspnea.While sitting the PTPdi/minwas significantly higher in CHF patients than in controls (181 7 54 cm H 2 O (cid:2) s/min vs . 96 7 32; P o 0.05). Assuming a supine position caused no major changesin controls, whereas CHF patients showed a significant worseningin dyspnea, a rise in PTPdi/ min (243 7 97 p o 0.01) and R , L (4.7 7 1.2 cm H 2 O/L (cid:2) s sitting vs . 7.9 7 2.5 supine; P o 0.01) and a decrease in C , L (0.08 7 0.02 L/cm H 2 O sitting vs .0.07+0.01supine; P o 0.05). Applying NIMV to supine CHF patients significantlyreduced the PTPdi/min to 81 7 42 ( P o 0.001). Changesin dyspnea, produced by varying position or applying NIMV, were signifi-cantlycorrelatedwith PTPdi/min ( r =0.80, P o 0.005 and r =0.58, P o 0.01, respectively). Conclusions: CHF patients had a higher PTPdi/min than controls when sitting, and assuming a supine position induced severe dyspnea, a large rise in R , L , and a reduction in C , L so that PTPdi/min increased further.Orthopnea was strongly correlatedwiththeincreased diaphragmaticeffort.
LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer

3D motion transfer aims at transferring the motion from a dynamic input sequence to a static 3D object and outputs an identical motion of the target with high-fidelity and realistic visual effects. In this work, we propose a novel 3D Transformer framework called LART for 3D motion transfer. With carefully-designed architectures, LART is able to implicitly learn the correspondence via a flexible geometry perception. Thus, unlike other existing methods, LART does not require any key point annotations or pre-defined correspondence between the motion source and target meshes and can also handle large-size full-detailed unseen 3D targets. Besides, we introduce a novel latent metric regularization on the Transformer for better motion generation. Our rationale lies in the observation that the decoded motions can be approximately expressed as linearly geometric distortion at the frame level. The metric preservation of motions could be translated to the formation of linear paths in the underlying latent space as a rigorous constraint to control the synthetic motions occurring in the construction of the latent space. The proposed LART shows a high learning efficiency with the need for a few samples from the AMASS dataset to generate motions with plausible visual effects. The experimental results verify the potential of our generative model in applications of motion transfer, content generation, temporal interpolation, and motion denoising. The code is made available: https://github.com/mikecheninoulu/LART .
Types and Methods of Managing SPAM Messages: A Review

This paper explores the problems caused by spam messages on the internet. It talks about diﬀerent types of spam, like email spam and fake advertisements on social media. The paper also looks at how spam can make things diﬃcult for people, like draining resources and tricking them into clicking on harmful links. The paper suggests ﬁghting against spam by enabling ﬁlters in emails, using third-party spam ﬁltering, avoiding clicking suspicious links, reporting, and blocking and avoiding using personal or professional emails. Lastly, the paper discusses what might happen with spam in the future and the challenges we may face. Overall, the paper aims to help and understand then ﬁnd ways to deal with it for a safer and smoother experience.
Alien Algae Species Invasions in Humic Rivers within Weakly Human Impact Basin

Increasing anthropogenic influence and climate change are leading to significant transformations in living conditions for indigenous representatives of aquatic communities. The problem of alien species invasions is actively discussed in the example of large rivers and water reservoirs, but medium and small rivers with weak anthropogenic influence have been insufficiently studied in this aspect. With the help of analysis of literary data and the results of our own long-term observations of phytoplankton using morphological and molecular genetic methods in some left-bank Volga tributaries, we identified six invasive species of different taxonomic groups of algae, with a predominance of diatoms. The relevance of using both traditional and modern approaches to identifying invasive algae species is revealed. Such invasive species as Thalassiosira incerta, T. faurii, Skeletonema subsalsum, Unruhdinium kevei, and Gonyostomum semen were part of planktonic communities; the benthic species Plagiotropis lepidoptera var. proboscidea sometimes reached a significant level of development (up 6 to 44% from total biomass) in plankton. It was demonstrated that some algae species have firmly taken the position of dominants and subdominants in planktonic algae communities. The expansion of alien representatives of microphytobenthos was noted in the Volga River basin for the first time. For Gonyostomum semen, its European origin was revealed, for plankton and benthic diatom—Ponto-Caspian. Our study showed that the processes of invasion and subsequent development of alien species take place in habitats with weak anthropogenic influence, which is likely determined by the hydrological, hydrochemical, and climatic changes in river basins and the high adaptive capabilities of invasive species.