Miracle-dz/UniFAR-FTUs huggingface dataset:
Vector error correction heterogeneous autoregressive forecast model of realized volatility and implied volatility

ABSTRACT A vector error correction model is proposed for forecasting realized volatility which takes advantage of the cointegration relation between realized volatility and implied volatility. The model is constructed by adding a cointegration error term to a vector-and-unit-root version of the heterogeneous autoregressive (HAR) model of Corsi (2009). The proposed model is easier to implement, extend, and interpret than fractional cointegration models. A Monte Carlo simulation and real data analysis reveal advantages of the proposed model over other existing models of Corsi (2009), Busch Christensen and Nielsen (2011), Cho and Shin (2016), and Bollerslev Patton, and Quaedvlieg (2016).
Algèbres commutatives de champs de vecteurs isochores

RÉSUMÉ. — Nous montrons qu'une sous-algèbre de Cartan de champs de vecteurs isochores peut être analytiquement réduite à sa forme normale. ABSTRACT. — We prove that a Cartan subalgebra of isochore vector-fieids can be analytically reduced to its normal form.
Sequential Decision Making under Uncertainty with Dynamic Resource Constraints

This paper studies a class of constrained restless multi-armed bandits. The constraints are in the form of time varying availability of arms. This variation can be either stochastic or semi-deterministic. A fixed number of arms can be chosen to be played in each decision interval. The play of each arm yields a state dependent reward. The current states of arms are partially observable through binary feedback signals from arms that are played. The current availability of arms is fully observable. The objective is to maximize long term cumulative reward. The uncertainty about future availability of arms along with partial state information makes this objective challenging. This optimization problem is analyzed using Whittle's index policy. To this end, a constrained restless single-armed bandit is studied. It is shown to admit a threshold-type optimal policy, and is also indexable. An algorithm to compute Whittle's index is presented. Further, upper bounds on the value function are derived in order to estimate the degree of sub-optimality of various solutions. The simulation study compares the performance of Whittle's index, modified Whittle's index and myopic policies.
AdaptParse: Adaptive Contextual Aware Attention Network for Log Parsing via Word Classification

Logs are widely used during the development and maintenance of software systems. Logs assist developers and operation & maintenance personnel to understand the state and behavior of systems at runtime. Also, logs can diagnose system failures and conduct abnormal analyses to provide further protection to the security of systems. However, large software systems generate large amounts of semi-structured logging routinely. The first step to support further analysis is how to parse semi-structured records with free-form text log messages into structured templates. Therefore, log parsing is rather challenging. Because logs are generated by static templates (i.e., log statements) in the source code, templates are often not accessible when parsing logs. It is worth noting that most proposed approaches still rely on log-specific heuristics or manual rule extraction. Those existed methods are often specialized for parsing certain log types and often neglect the semantic meaning of log messages, thus limiting performance scores and generalization, hence, in this paper, we propose a new parsing technique - Adaptive Contextual Aware Attention Network for Log Parsing via Word Classification, named AdaptParse. Adapt-Parse transforms the template generation problem into a word classification task, then learns the features of template words and variable words. We evaluate our AdaptParse on 5 realworld log datasets and compare the performance with 7 parsing techniques. Our experimental results show that the proposed approach can effectively understand the semantic meaning of log messages and achieve accurate log parsing results. Overall, AdaptParse achieves state-of-the-art performance on five realworld log datasets, outperforming all the baseline models.
A typological analysis: understanding pre-service teacher beliefs and how they are transformed

ABSTRACT This article describes the beliefs and their transformations of members of a cohort of early-childhood, elementary and middle-level pre-service teachers (PSTs) as they professionally develop. A typological analysis of both quantitative and qualitative data collected between August 2011 and May 2013 was utilized to categorize how 40 PSTs’ beliefs transformed throughout their formal teacher preparation. Five typologies were identified, showing variation in how PST beliefs transform or remain static. Among the findings, strong support related to the development of innovative beliefs during coursework coupled with at least one transformative experience where innovation was observed ‘working’ in the field were sufficient for the transformation to innovative beliefs, despite potential constraints by supervisors, cooperating teachers and/or mandated curricula (Typology 3). Another finding revealed disguised growth toward innovation among those in Typology 5, who reported being innovative and having productive beliefs but described extremely traditional practices. Implications call for improved connections between mathematics methods professors and field supervisors, particularly during clinical internships when PSTs are no longer enrolled in methods courses, to enhance PSTs’ productive struggle in their development of innovative beliefs (T3) and to increase opportunities for disconnects between innovative beliefs and traditional practices to be made explicit and negotiated (T5).
A 3.1 W High Gain 51% Fractional Bandwidth GaN HEMT Power Amplifier Design for Far-Field Wireless Power Transfer (WPT) Applications

This paper presents the design of a high gain and efficient power amplifier (PA) that is capable of operating within the ultra-wideband (UWB) frequency range (3.1 - 10.6 GHz) for wireless power transfer (WPT) applications. Qorvo TGF2977 GaN HEMT transistor has been used for the proposed design since it is capable of operating in the desired UWB frequency range from DC to 12 GHz. An impedance matching technique has been proposed in this paper so that when the matching network elements are translated through the T-line elements, the impedance of the matching network incorporating the T-line elements is matched to the optimal source and load impedances of the transistor. An analytical model of the microstrip T-line has been developed in this paper so that proper impedance matching can be achieved by tuning and controlling the width of the T-line elements. The desired S-parameter response can be obtained by using this technique so that the target performances of the PA can be achieved. The proposed PA is chosen to operate in the class-C mode since this mode of operation can achieve a very high efficiency. The designed PA is capable of achieving a gain of 14.34 dB and has a $-3dB$ gain bandwidth of 3.03 GHz (51% fractional bandwidth) from 4.22 – 7.25 GHz. The PA is able to generate the highest output power of 34.9 dBm $(3.1W)$ at the $P_{1dB}$ of 22 dBm input power at 6 GHz frequency. At the same frequency and the $P_{1dB}$ input power, the proposed PA is able to achieve a power added efficiency (PAE) of 37.1% while the highest PAE of 45.6% is achieved at the 28 dBm input power.
A Novel Spatial-Temporal Deep Neural Network for Electricity Price Forecasting

Electricity price forecasting is a difficult task because it depends on various factors such as weather, fuel, load, and bidding strategies. These characteristics bring a lot of volatility to electricity prices. In addition, there exist coupling relationships between different price zones in Europe. CNN-based or LSTM-based methods cannot capture the relationship by their structures or there is a need to extract these couplings explicitly manually. In this work, an end-to-end graph neural network is proposed for the first time to learn the coupling between different price zones automatically. The proposed model mainly consists of two parts: a graph learning module and a temporal learning module, which both are designed to learn spatial information of different price zones and temporal information of historical data, respectively. The performance of the proposed model is evaluated on one-year public data collected from the Nord Pool. The results indicate that our model provides a better solution for electricity price forecasting.
Insty-GAN for Image-to-Image Location Transfer

Image-to-Image Transformation (I2I) aims to transfer an image from a source domain to a target domain while changing some specified feature representations and preserving some other feature representations. For example, most I2I tasks keep the layout features of an image while changing its color, style, or resolution features. However, image-to-image location transfer only wants to change the layout of objects in an image's foreground, such as LT-GAN and Orthod-GAN. In this paper, we researched the above image-to-image location translation tasks. First, we studied the similarities and differences between malpositioned-teeth images and their corresponding neat-teeth images. Then we analyzed the problems of directly applying the LT-GAN method to the orthodontics transfer task. Finally, we proposed a new approach called Insty-GAN that uses an instance transfer bridge and a style loss to address image-to-image location transfer. Our Insty-GAN is applied to orthodontic and chaos-to-order transfer tasks and improved the FID score by 9.1% and 8.1% over their SOTA methods. LPIPS scores also show that Insty-GAN performs a more excellent production than other baselines in image-to-image location transfer. Additionally, we have annotated 10K instance segmentation labels for Orthod and M2C datasets. These labels could also be used in future works. The added data is available at https://drive.google. com/drive/folders/l5ilnLE_pwIB6fQq1nbWTpMxav6COdU7p? usp=sharing.
NodeMedic: End-to-End Analysis of Node.js Vulnerabilities with Provenance Graphs

Packages in the Node.js ecosystem often suffer from serious vulnerabilities such as arbitrary command injection and code execution. Existing taint analysis tools fall short in providing an end-to-end infrastructure for automatically detecting and triaging these vulnerabilities.We develop NodeMedic, an end-to-end analysis infrastructure that automates test driver creation, performs precise yet scalable dynamic taint propagation via algorithmically tuned propagation policies, and exposes taint provenance information as a provenance graph. Using provenance graphs we develop two post-detection analyses: automated constraint-based exploit synthesis to confirm vulnerabilities; Attack-defense-tree–based rating of flow exploitability.We demonstrate the effectiveness of NodeMedic through a large-scale evaluation of 10,000 Node.js packages. Our evaluation uncovers 155 vulnerabilities, of which 152 are previously undisclosed, and 108 were confirmed with automatically synthesized exploits. We have open-sourced NodeMedic and a suite of 589 taint precision unit tests.
Cryptosentiment: A Dataset and Baseline for Sentiment-Aware Deep Reinforcement Learning for Financial Trading

Deep Learning (DL) models have been applied in several studies to solve financial trading problems. Most approaches handle these problems as classification or reinforcement learning problems with the objective of developing profitable strategies. Recent works have demonstrated that supplying financial trading agents with sentiment information can lead to improved performance. However, most of these works focus on collecting sentiment in a coarse-grain manner, which is not always appropriate for making fine-grained trading decisions, e.g., on a minute basis. In this paper, we introduce a fine-grained cryptocurrency sentiment dataset, called CryptoSentiment, which contains 235,907 sentiment scores for 14 cryptocurrency assets, gathered by various online sources. Moreover, we provide Deep Reinforcement Learning (DRL) baselines using the collected dataset, investigating the impact of multi-modal features on cryptocurrency trading.
Path-following control for a slung load system

A slung load system (SLS) is a mechanical dynamical system composed of an unmanned aerial vehicle (UAV) carrying a slung load. The nonlinear underactuated SLS dynamics make its motion control a challenging and current problem. This paper proposes a motion control that uses path-following instead of traditional trajectory-tracking. The method defines a geometric path in 3D space for the SLS to follow and renders it controlled-invariant. Output tracking error dynamics are exponentially stabilized using a dynamic state feedback linearization. Simulations demonstrate the robustness of the control to payload mass uncertainty and its benefits over trajectory-tracking.
SLX: Similarity Learning for X-Ray Screening and Robust Automated Disassembled Object Detection

Baggage screening is important in security-critical applications in airports for detecting threats, including firearms and parts of them. Existing approaches underperform to recognise prohibited objects that are disassembled, especially when learning from limited data and from images produced by different scanners with multi-view orientations. To address such limitations, in this paper, we develop the Similarity Learning X-ray screening (SLX) model for accurate and robust firearm component detection in cluttered scenes. We evaluate SLX on the X-ray Image Library (XIL) dataset that the UK Government has provided us with, for this research. SLX is based on a contrastive similarity learning approach combined with Out-of-Distribution (OoD) detection/ anomaly detection using a deep discriminative model, ResNet-152, for detecting and classifying forbidden items. The evaluation of SLX on the XIL dataset shows that it is effective, beneficial for detecting firearms and their parts, and outperforms other baseline models, on average, by approximately 12 points in accuracy.
Holistic Deep-Reinforcement-Learning-based Training for Autonomous Navigation in Crowded Environments

In recent years, Deep Reinforcement Learning emerged as a promising approach for autonomous navigation of robots and has been utilized in various areas of navigation such as obstacle avoidance, motion planning, or decision making in crowded environments. However, most research works either focus on providing an end-to-end solution training the whole system using Deep Reinforcement Learning or focus on one specific aspect such as local motion planning. This however, comes along with a number of problems such as catastrophic forgetfulness, inefficient navigation behavior, and non-optimal synchronization between different entities of the navigation stack. In this paper, we propose a holistic Deep Reinforcement Learning training approach in which the training procedure is involving all entities of the navigation stack. This should enhance the synchronization between- and understanding of all entities of the navigation stack and as a result, improve navigational performance in crowded environments. We trained several agents with a number of different observation spaces to study the impact of different input on the navigation behavior of the agent. In profound evaluations against multiple learning-based and classic model-based navigation approaches, our proposed agent could outperform the baselines in terms of efficiency and safety attaining shorter path lengths, less roundabout paths, and less collisions especially in situations with a high number of pedestrians.
AirKeyLogger: Hardwareless Air-Gap Keylogging Attack

This paper presents AirKeyLogger - a novel radio frequency (RF) keylogging attack for air-gapped computers.Our keylogger exploits radio emissions from a computer’s power supply to exfiltrate real-time keystroke data to a remote attacker. Unlike hardware keylogging devices, our attack does not require physical hardware. Instead, it can be conducted via a software supply-chain attack and is solely based on software manipulations. Malware on a sensitive, air-gap computer can intercept keystroke logging by using global hooking techniques or injecting malicious code into a running process. To leak confidential data, the processor’s working frequencies are manipulated to generate a pattern of electromagnetic emissions from the power unit modulated by keystrokes. The keystroke information can be received at distances of several meters away via an RF receiver or a smartphone with a simple antenna. We provide related work, discuss keylogging methods and present multi-key modulation techniques. We evaluate our method at various typing speeds and on-screen keyboards as well. We show the design and implementation of transmitter and receiver components and present evaluation findings. Our tests show that malware can eavesdrop on keylogging data in real-time over radio signals several meters away and behind concrete walls from highly secure and air-gapped systems.
ECANodule: Accurate Pulmonary Nodule Detection and Segmentation with Efficient Channel Attention

Accurate detection and segmentation of pulmonary nodules in low-dose CT images is essential for early screening and treatment of lung cancer. Previous methods have often overlooked the critical role of segmentation in nodule feature learning, relying on relatively simple region proposal networks and false positive reduction modules. To address this limitation’ we introduce an segmentation branch to fully utilize the additional information such as nodule shape and boundary. Our proposed 3D U-Net detection model based on multi-task learning is optimized through bottom-layer parameter sharing to enhance prediction performance by fully utilizing complementary information between tasks. As for challenging problem of large nodule scale variety and complex background, we add more skip connections between the encoder and decoder structures, enhancing the fusion of features from different levels and facili-tating gradient flow, thus reducing model training difficulty. We also incorporate an efficient channel attention module in residual block to improve model learning and representation capability. Our method, named ECANodule, achieves an average detection sensitivity of 91.1% and a segmentation Dice score of 83.4% on the LIDC-IDRI dataset, surpassing many previous detection methods. In addition, we provide in-depth discussions on the multi-task strategy, network structure, and channel attention mechanism, offering valuable insights for future research.
Effective Representations of Clinical Notes

Clinical notes are a rich source of information about patient state. However, using them to predict clinical events with machine learning models is challenging. They are very high dimensional, sparse and have complex structure. Furthermore, training data is often scarce because it is expensive to obtain reliable labels for many clinical events. These difficulties have traditionally been addressed by manual feature engineering encoding task specific domain knowledge. We explored the use of neural networks and transfer learning to learn representations of clinical notes that are useful for predicting future clinical events of interest, such as all causes mortality, inpatient admissions, and emergency room visits. Our data comprised 2.7 million notes and 115 thousand patients at Stanford Hospital. We used the learned representations, along with commonly used bag of words and topic model representations, as features for predictive models of clinical events. We evaluated the effectiveness of these representations with respect to the performance of the models trained on small datasets. Models using the neural network derived representations performed significantly better than models using the baseline representations with small ($N < 1000$) training datasets. The learned representations offer significant performance gains over commonly used baseline representations for a range of predictive modeling tasks and cohort sizes, offering an effective alternative to task specific feature engineering when plentiful labeled training data is not available.
Multi-Branch Spatial-Temporal Decoupling Neural Network for Traffic Forecasting

Traffic forecasting has numerous application scenarios, but it is also a challenging task considering complex spatial and temporal correlations. Deep learning has shown great potential in recent years. Graph convolutional networks, recurrent neural networks and other deep learning architectures have been used in traffic forecasting. However, most existing methods capture spatial and temporal correlations sequentially or synchronously and couple spatial and temporal representations, which may introduce spurious dependencies. The heterogeneity of spatio-temporal data also increases the difficulty of modeling and makes it harder to obtain accurate predictions. In this paper, we propose a novel multi-branch spatial-temporal decoupling neural network (MBSTDN) for traffic forecasting. Specifically, we independently map hidden features into spatial and temporal representations and process them through corresponding branches to produce partial predictions. The partial predictions are then aggregated into final predictions by residual links. At the same time, we reconstruct input signals through a separate branch, introducing layer-by-layer decomposition based on residual learning to deal with the intricate dependencies. Furthermore, we design an embedding layer utilizing multi-dimensional information to reduce the influence of spatio-temporal heterogeneity on predictions. Experiments on four public traffic datasets demonstrate the effectiveness of MBSTDN which performs better than the state-of-the-art baselines.
Easily Adjusting Capillary Flow Rates on Nitrocellulose Membranes by a Household Laminator

Capillary flow rate control is interesting for biosensors based on nitrocellulose membranes. This paper introduces a method to control capillary flow rates on nitrocellulose membranes by simply pressing the nitrocellulose membranes using a household laminator. By applying pressure on nitrocellulose membranes, we can easily change the thickness of the nitrocellulose membrane and therefore change the pore size of the porous structure. We also notice that the pressure can break some nitrocellulose fibers. We investigate the influence by measuring the capillary flow rates on nitrocellulose membranes treated by different pressure, and also check their capacity of water absorption. We find that capillary flow rates decrease on nitrocellulose membranes with pressure treatment and the method can reduce the capillary flow rate by up to 42.3%. The water absorption capacity also decreases for nitrocellulose membranes with pressure treatment, and the maximum decrease is 44.6%. This method is promising in reducing the sample flow rates in biosensing devices made of nitrocellulose membranes.
0.5V Low Power Level Shifter Flipped Source Follower LPF Circuit for Healthcare System

A high-cut filter design, which is considered an important circuit can be used to provide significant advantages in the implementations of biological systems. This paper, presents a LSFSF LPF planned for biological healthcare systems. The LSFSF LPF design has been pretend in CMOS $0.18 \mu \mathrm{m}$ technology node. The proposed LPF gives -3 dB of gain, 100 Hz of bandwidth, 1 nW of power, $83 \mu \mathrm{V} _{rms}$ of IRN and $0.5 \times 10^{-13}$ J of figure of merit. The demonstrated LPF design can be used to achieve low-power dissipation and low-energy operation in biological health care systems.