Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Coded Caching Scheme via Cartesian Product Method

Coded caching stores popular files in local caches, which is an effective method for decreasing network traffic during peak hours. Yan et al. put forward a combinatorial structure named placement delivery array (PDA), which is used to design coded caching schemes with uncoded placement and one-shot delivery. Recently, Wang et al. constructed an $mK_{1}$- user PDA by using the $m$ -fold Cartesian product of a special $K_{1}$ -user PDA called base PDA. In this paper, we first construct a new base PDA under the cyclic placement, then by using the Cartesian product method, a new coded caching scheme is attained, which is applicable to arbitrary quantity of users and memory ratio. Performance analyses show that the new scheme performs significantly better than previous schemes in terms of subpacketization or transmission load.
Visual–Inertial Fusion-Based Human Pose Estimation: A Review

Human pose estimation provides valuable information for biomedical research on human movement and applications, such as entertainment and physical exercise. The fusion of visual and inertial data has been increasingly studied in the past two decades to take advantage of these two naturally complementary sensing modalities. In this article, we systematically reviewed the advances in visual–inertial fusion-based human pose estimation with a thorough search for related studies in five mainstream literature databases. A total of 54 studies were identified and included by screening 4586 records retrieved in the review process. The estimation targets, hardware design, fusion methods, evaluation metrics, and system accuracy of these included studies were summarized and categorized for analysis. From these state-of-the-art studies, challenges in terms of mobility, calibration, real-time estimation, and evaluation methods are further discussed in depth and possible directions to overcome these issues are recommended. We expect that this systematic review can provide researchers and engineers with a thorough idea of the progress and performance in visual–inertial fusion-based human pose estimation. We also hope that the discussions on challenges and possible future directions can facilitate future work to improve such systems and promote their applications in real life.
Transmutation of 239Pu irradiated by 660 MeV protons

Today, one of the problems related to nuclear energy is the question of the utilization of plutonium from spent nuclear fuel, which has a relation to the problem of nuclear weapons and environmental protection. Nuclear transmutation of long-lived fission products is one of the great solutions to these problems. Transmutation is also the basic process of accelerator driven systems. It is claimed that the ADS offer new prospects and advantages for the reprocessing of high-level radioactive waste. Therefore, the study of plutonium spallation reactions by protons and/or neutrons is of great significance. In can bring a better understanding of the processes related to the issue. Paper describes cross-sections of fission and spallation reactions of a plutonium target irradiated with a proton beam with an energy of 660 MeV. Cross-section and reactions’ yields determination consists of three parts: experiment, data processing, and mathematical simulating the problem.
Photonic Characterization and Modeling of Highly Efficient Color Conversion Layers With External Reflectors

We propose a new method for fabricating arrays of colloidal quantum dots for color conversion layers (CCLs) with atomic layer deposited (ALD) encapsulation. Semiconductor processes were used to transfer the CCL onto a transparent glass substrate. This method enables pixels as small as 5 μm, and the pixel's size and pitch can be controlled through the semiconductor process and mask layout. A dual-color array with a red-and-green CCL was also demonstrated to be suitable for microdisplay applications in experiments. The best color conversion efficiency of a single color CCL reached as high as 20.7%. A set of distributed Bragg reflector mirrors was used to increase photon recycling, and these mirrors yielded an increase in peak intensity of 35.7%. We formulated a model of incoherent reflection and transmission to calculate the changes in CCL properties when mirrors are added; this model had close fit with results from experiments in which mirrors of different reflection coefficients were used. Through the use of ALD, the CCL can be stored at a normal room-temperature environment for more than 9000 hours, and the projected lifetime is linearly extrapolated to be 44,041 hours.
Bidirectional Piggybacking Design for All Nodes with Sub-Packetization l = r

Piggybacking design has been applied extensively in distributed storage systems in recent years, since it can reduce repair bandwidth significantly with small sub-packetization. In this work, we propose a bidirectional piggybacking design (BPD) with sub-packetization l = r, where r = n − k equals the redundancy of an [n,k] linear code. Unlike most existing piggybacking designs, there is no distinction between systematic nodes and parity nodes in BPD and the piggybacks are added bidirectionally. Consequently, BPD leads to lower average repair bandwidth than previous piggybacking designs at equal subpacketization level when r ≥ 3. However, BPD needs larger fields to maintain the MDS property. We prove two upper bounds on the field size for explicit BPD and existential constructions respectively. By computer search, our BPD can be given over a field much smaller than the proved upper bounds. As an example, we provide the BPD for the [14], [10] Reed-Solomon (RS) code over F28 and obtain approximately 50% savings in the average repair bandwidth compared with the trivial repair approach. This is the lowest repair bandwidth achieved so far for [14], [10]256 RS codes with sub-packetization l ≤ 4.
A new method for producing temperature profiles based on ERA5 and RAOB

Temperature profiles are important meteorological parameters of the atmosphere that can determine atmospheric thermal processes. Detecting global spatial and temporal continuous atmospheric temperature profiles is crucial for weather protection work. Atmospheric datasets such as ERA5 (fifth generation ECMWF reanalysis) provide global and continuous temperature profile datasets with good resolution. RAOB (radiosonde) sounding data have high confidence and representativeness and are commonly used for data accuracy validation. In this paper, we use the RAOB sounding data of 2017 as the true value and revise the ERA5 reanalysis data based on machine learning methods to optimize the data. The algorithm not only improves the problem of RAOB distribution discontinuity but also improves the accuracy of ERA5 itself. In order to verify the results of the algorithm, the RAOB sounding data are compared with it, and it is found that the accuracy of the revised data is reduced by about 3K compared to the preprocessing RMSE, which is closer to the RAOB data. The algorithm proposed in this paper can provide important data support for subsequent meteorological studies.
Application of 3D multi-user software tools in digital medicine – a scoping review

Nowadays, the amount of information to be processed in specialized fields of medicine is increasing day by day, and the time allocated for processing is constantly decreasing. Despite the fact that real-time multi-user software is now very popular in some domains of medicine, like radiology, the real potential provided by this technology has not yet been exploited. A scoping review was performed to identify key clinical areas where large-scale information processing presents a usability challenge. In digital pathology, similarly to robot-assisted surgery, the use of multi-user software is a key issue, as it can revolutionize the flow of decision making, the education of medical students, and the consultation between doctor and patient and doctor and doctor as well. The aim of our paper is to give the reader a useful overview of the current level of the adaption of real-time multi-user software tools in two prominent subfields of medicine, digital pathology and robot-assisted surgery.
A Convolutional Recurrent Model for the Identification of Patients with Atrial Fibrillation Based on Heart Rate Variability Data During Sinus Rhythm

Atrial fibrillation (AF) is one of the most common heart diseases in population. Timely diagnosis of AF is challenging due to the asymptomatic and episodic nature of the disease. It is therefore necessary to develop methods that can identify patients with AF using electrocardiographic data of normal sinus rhythm when there's no abnormal rhythm present in recordings. Models based on convolutional neural networks have been successful using 12-lead ECGs with high sampling rates. We believe it is possible to solve the problem with more generalised biomarker data of heart rate variability. We consider recurrent neural networks for this task. In this paper, we consider a convolutional recurrent neural network (CRNN) model with LSTM layers and compare its classification performance to a convolutional neural network (CNN) with a global average pooling operator. In addition, we generate attention maps of the CRNN model and demonstrate the peculiarities of its decision mechanism. Open PhysioNet data is used for model training; data from a local clinical hospital is used for model validation. In general, the CRNN model shows better patient classification results and provides interpretable attention maps with GradCAM++ method.
Eavesdropping Attack Detection in UAVs using Ensemble Learning

The use of Unmanned Aerial Vehicles (UAVs) is proliferated and is prone to cyber attacks. Eavesdropping attack is an active threat to the security of an UAV as attackers intercept the communication medium over the wireless communication networks and get access to sensitive information. An active eavesdropper infiltrates the system and attacks the UAV during authentication. It involves the unauthorized interception of communication signals between the UAV and its control system. This type of intrusion can have severe consequences, including loss of control over the UAV, theft, espionage, and sabotage. To maintain the privacy and security of UAV communications and to protect sensitive information from unauthorized access, the detection of eavesdropping is of utmost importance. For the detection of eavesdropping attacks, we build an ensemble learning model with supervised machine learning algorithms (Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbours and Support Vector Machine) and unsupervised learning methods (One Class Support Vector Machine and K-Means Clustering). By combining the predictions of multiple algorithms, ensemble learning enhances the security and privacy of UAV communication. Additionally, by pooling together the strengths of different algorithms, ensemble learning improves the overall robustness and resilience of the UAV communication system and is a beneficial approach for the detection of eavesdropping attack packets. To train our proposed model we use the Kitsune Network Attack dataset. From the results, it is observed that our ensemble learning approach is a valid stratagem and can be used to detect eavesdropping attacks on UAV.
Evaluation of the Rawls et al. (1982) Pedotransfer Functions for their Applicability at the U.S. National Scale Large scale environmental impact studies typically involve the use of simulation models and

require a variety of inputs, some of which may need to be estimated when adequate measured data are absent. As an example, soil water retention needs to be estimated for a large number of soils that are to be used in the context of the U.S. national scale Conservation Eff ects Assessment Project (CEAP). Use of a set of well known linear regression based pedotransfer functions (PTFs) developed in 1982 was proposed to address such data need. Examination of the underlying data as well as comparative estimations to an independent US-wide data set revealed that the proposed equations were most likely meant to use organic carbon (OC) data in place of the reported organic matter (OM) data. Other discrepancies—possibly due to misreporting—were also found in a large portion of the OM data. Th ese PTFs were also developed from data originating from only 18 U.S. states—and 48% of them dominated by 3 U.S. states—while major cropland states/regions were barely or not represented at all. Resulting estimations showed non-random distribution of estimation residuals (i.e., bias) that could however be corrected with data transformations and by using a k-Nearest Neighbor algorithm as an alternative PTF technique. We recommend that the PTF equations proposed in 1982 not be used in the context of the U.S. national scale CEAP project. Alternative solutions should ensure the proper representation of U.S. soils and their properties.
Scalable Deep Reinforcement Learning-Based Online Routing for Multi-Type Service Requirements

Emerging applications raise critical QoS requirements for the Internet. The improvements in flow classification technologies, software-defined networks (SDN), and programmable network devices make it possible to fast identify users’ requirements and control the routing for fine-grained traffic flows. Meanwhile, the problem of optimizing the forwarding paths for traffic flows with multiple QoS requirements in an online fashion is not addressed sufficiently. To address the problem, we propose DRL-OR-S, a highly scalable online routing algorithm using multi-agent deep reinforcement learning. DRL-OR-S adopts a comprehensive reward function, an efficient learning algorithm, and a novel deep neural network structure to learn appropriate routing strategies for different types of flow requirements. In order to enhance the generalization and scalability, we propose a novel graph-based actor-critic network architecture and a carefully designed input state for DRL-OR-S. To accelerate the training process and guarantee reliability, we further introduce an NN-simulator for efficient offline training and a safe learning mechanism to avoid unsafe routes during the online routing process. We implement DRL-OR-S under SDN architecture and conduct Mininet-based experiments using real network topologies and traffic traces. The results validate that DRL-OR-S can well satisfy the requirements of latency-sensitive, throughput-sensitive, latency-throughput-sensitive, and latency-loss-sensitive flows at the same time, while exhibiting great adaptiveness and reliability under the scenarios of link failure, traffic change, unseen large topology and partial deployment.
minBERT and Multi-Task Learning for Downstream Tasks

The Bidirectional Encoder Representations from Transformers model (BERT) proposed by Devlin et al [1] significantly improved fine-tuning based, pre-trained approaches to downstream NLP tasks. In this project, we implement key features of BERT and go further to implement extensions to pretrained BERT to further improve BERT’s performance on multiple downstream tasks. Implemented extensions augment BERT’s finetuning mechanisms to train the model for three tasks – sentiment classification, semantic textual similarity, and paraphrase detection – by altering how and when the model is trained for certain tasks and or by using a different objective function. Further experimentation was also conducted with the model’s hyperparameters. We find that a combination of Gradient Surgery, as described by [2] and Pearson Correlation Loss resulted in the best performance both task-wise and on average with a improvement in average accuracy by 0.24 over all three tasks. However, other permutations of extensions were also shown to on average improve upon base BERT’s performance with an average accuracy of 0.453 versus base BERT’s 0.299. It was also found that the model generally performs better with a batch size of 8 and learning rate of 1e − 5 . Our findings suggest that Gradient Surgery is a viable method for multitask finetuning and further benefits from the use of Pearson Correlation Loss as its objective function, though other permutations are also potentially promising.
Impact of duration of untreated psychosis on remission in first-episode schizophrenia in Thailand: a cohort study

Background: To scale up the services for first-episode schizophrenia in Thailand, it is essential to understand to what extent health careseeking is delayed, and how much the delay affects the treatment outcome. Objectives: To investigate the duration of untreated psychosis (DUP) and its impact on remission in first-episode schizophrenia across the country. Methods: 276 outpatients with a first-episode schizophrenia were followed for 6 months and assessed whether they fulfilled the criteria for remission at the follow-up. The proportion of those achieving remission was compared by the DUP. The impact of DUP on remission was estimated in multivariate analyses. Results: At the follow-up, 83% (71/86) of patients who had met the criteria for symptomatic remission at the baseline achieved enduring remission, whereas 63% (119/190) of patients who had not met the criteria for symptomatic remission at baseline met it at the follow-up. The shorter the DUP, the higher the proportion of those who achieved symptomatic or enduring remission at the follow-up. The impact of DUP on symptomatic remission appeared to be significant after controlling for other factors influencing remission. Conclusion: Since the DUP would influence remission of patients with schizophrenia, early detection and intervention services should be provided in Thailand. Takizawa P / Arch Clin Psychiatry. 2021;48(3):141-146
The Eat Sleep Console Approach: An Evaluation of Impact, Outcomes and Continuation of Care

Neonatal Opioid Withdrawal Syndrome (NOWS), a subgroup of Neonatal Abstinence Syndrome (NAS), is a constellation of postnatal symptoms that occurs due to the abrupt cessation of intrauterine opioid exposure. Historically, these infants have been evaluated and treated by the Finnegan Neonatal Abstinence Scoring Tool (FNAST) (Appendix A). Recent evidence supports the use of the Eat Sleep Console Approach (ESC) (Appendix B) as an alternative to the FNAST. The ESC model focuses on the comfort and care of these infants by maximizing nonpharmacologic methods and increasing family involvement in the treatment of their infant. The purpose of this project was to evaluate the implementation of ESC in a community hospital and to develop recommendations for subsequent steps to continue the improvement of care for this mother-infant dyad. Clinical data was collected and evaluated on opioid exposed newborns (OENs) from December 2019 through December 2020. ESC was implemented in June of 2020; therefore, we were able to compare six months of infants who were evaluated and treated using FNAST to six months of infants who were evaluated and treated using ESC. Additionally, clinical data was collected and examined to evaluate maternal outcomes that may have been impacted by this change in assessment tools. Lastly, survey data was collected from outpatient pediatric providers to assess their level of comfort in caring for these newborns, assess typical practice in caring for this patient population and evaluate handoff from the inpatient facility. The goals to decrease the average length of stay (ALOS) by one day and total morphine exposure by 20% were met. Other findings include that ESC enhances mother-infant bonding and decreases cost. Additionally, there were inconsistencies identified in outpatient follow up practices and outpatient providers desire consistent handoff from inpatient facilities. ESC: IMPACT, OUTCOMES AND CONTINUATION OF CARE 3
Cyber contagion: impact of the network structure on the losses of an insurance portfolio

In this paper, we provide a model that aims to describe the impact of a massive cyber attack on an insurance portfolio, taking into account the structure of the network. Due to the contagion, such an event can rapidly generate consequent damages, and mutualization of the losses may not hold anymore. The composition of the portfolio should therefore be diversified enough to prevent or reduce the impact of such events, with the difficulty that the relationships between actor is difficult to assess. Our approach consists in introducing a multi-group epidemiological model which, apart from its ability to describe the intensity of connections between actors, can be calibrated from a relatively small amount of data, and through fast numerical procedures. We show how this model can be used to generate reasonable scenarios of cyber events, and investigate the response to different types of attacks or behavior of the actors, allowing to quantify the benefit of an efficient prevention policy.
ENUMERATION OF HOLOMORPHIC CYLINDERS

We define the counting of holomorphic cylinders in log CalabiYau surfaces. Although we start with a complex log Calabi-Yau surface, the counting is achieved by applying methods from non-archimedean geometry. This gives rise to new geometric invariants. Moreover, we prove that the counting satisfies a property of symmetry. Explicit calculations are given for a del Pezzo surface in detail, which verify the conjectured wall-crossing formula for the focusfocus singularity. Our holomorphic cylinders are expected to give a geometric understanding of the combinatorial notion of broken line by Gross, Hacking, Keel and Siebert. Our tools include Berkovich spaces, tropical geometry, GromovWitten theory and the GAGA theorem for non-archimedean analytic stacks.
Freeze Your Layers

To achieve high performance on natural language processing tasks requiring supervised learning, empirical work has demonstrated the success of pretraining a language model on a large amount of text data from a general domain and then finetuning the model on a smaller amount of data from the problem domain. Within a multi-task learning framework, the model is simultaneously finetuned on multiple different datasets for different tasks. With a variation of the widely used BERT model serving as our representation learner, we investigate the best way to use multi-task learning to achieve high performance on three distinct tasks. We find that a simple implementation of such a model performs moderately well on all tasks, and we are only able to improve performance in relation to such an implementation by freezing layers of our version of BERT during training
MT-BERT: Fine-tuning BERT for Downstream Tasks Using Multi-Task Learning

The goal of this project is to fine-tune the contextualized embeddings from BERT to perform well simultaneously on three different downstream tasks: Sentiment Analysis (SST), Paraphrase Detection (PD), and Semantic Textual Similarity (STS). Our work uses four main techniques to improve the baseline BERT implementation: additional pretraining on the target domain data using Masked Language Modelling, multi-task fine-tuning using gradient surgery, single-task fine-tuning, and feature augmentation. We are able to achieve accuracies of 0.539 and 0.877 for SST and PD respectively, and a Pearson correlation score of 0.863 on the STS test set using a single model without ensembling.
BERT-MTS: Fine Tuning BERT for Multi-Task Serving

The project has a two fold goal: implement core components of BERT to better understand its architecture, and experiment with possible extensions to produce generalized sentence embeddings that can perform well on multiple tasks. For the ﬁrst goal, important components of BERT were implemented to create a minBERT model. As for the second goal, three task speciﬁc minBERT models were fully ﬁne-tuned for comparison with a single multi-task model built using the framework proposed in Wei et al. (2022). The fully ﬁne-tuned task speciﬁc models outperformed the pre-trained minBERT model, and the single multi-task model achieved 99% accuracy of the fully ﬁne-tuned models