Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study of Truck Payload Allocation(ศึกษาข้อมูลเชิงประจักษ์ การปันส่วนน้ำหนักบรรทุก ตามชนิดของรถบรรทุก)

This study estimates the number of trucks by type and the level of service on the Interstate highway 15 (I - 15) located in Utah. Since an increasingnumber of trucks affect the efficiency of the highway, providing the trips by truck types, body types and commodity types can be more clearly analyzed the relationship between density and delay on the highway. The predicted commodity flow data from the Freight Analysis Framework version 3 of the United Stated Department of Transportation in the year 2015 is derived. The truck allocation factors are modified to convert the commodity flow data to the annual average daily truck traffic (AADTT). The results have shown that the degree of saturation on the I - 15 highway for the year 2015 approaches a full capacity, requiring a short - term transport solution. การศกษาเปนการนำเสนอวธการพยากรณความหนาแนนของรถบรรทกทวงผานเสนทางระหวางรฐในประเทศสหรฐอเมรกา กรณศกษาทางหลวงหมายเลข 15ทตดผาน และอยในเขตรฐยทา (I - 15) เนองจากระบบการขนสงหลกภายในประเทศสหรฐอเมรกา คอ การขนสงโดยรถบรรทก ทางหลวงระหวางรฐจงมบทบาท และความสำคญตอระบบการขนสงภายในประเทศเปนอยางมาก ในขณะทแนวโนมของรถบรรทกมจำนวนเพมขนทกป ซงจะมผลกระทบโดยตรงตอความหนาแนนของรถบนโครงขายทางหลวง การศกษาเปนการนำเสนอวธการพยากรณจำนวนรถบรรทก โดยใชขอมลทตยภมทางดานสถตของการเคลอนทของสนคาจากจดกำเนดถงจดหมายปลายทางแยกตามประเภทของสนคา การพยากรณโดยใชขอมลดงกลาวถกแปลงคาเปนปรมาณรถบรรทกจราจรเฉลยตอวนทงป (Annual Average Daily Truck Traffic: AADTT) ผลจากการศกษาจะสามารถนำไปวเคราะหความสมพนธระหวางความหนาแนน และความลาชาของรถบรรทกบนทางหลวงไดชดเจนมากยงขน การศกษา พบวา ความหนาแนนของรถจะเกดขนในระดบสงสดบนถนนเสน I - 15 ในเขตยทาในป 2015 ซงผลการศกษาดงกลาวสามารถนำไปกำหนดพนท เพอขยายและปรบปรงเสนทางกอนจะเกดวกฤตบนทองถนนดงกลาวได
S-Acylation Regulates the Membrane Association and Activity of Calpain-5

Calpain-5 (CAPN5) is a member of the calpain family of calcium-activated neutral thiol proteases. CAPN5 is partly membrane associated, despite its lack of a transmembrane domain. Unlike classical calpains, CAPN5 contains a C-terminal C2 domain. C2 domains often have affinity to lipids, mediating membrane association. We recently reported that the C2 domain of CAPN5 was essential for its membrane association and the activation of its autolytic activity. However, despite the removal of the C2 domain by autolysis, the N-terminal fragment of CAPN5 remained membrane associated. S-acylation, also referred to as S-palmitoylation, is a reversible post-translational lipid modification of cysteine residues that promotes membrane association of soluble proteins. In the present study several S-acylated cysteine residues were identified in CAPN5 with the acyl-PEG exchange method. Data reported here demonstrate that CAPN5 is S-acylated on up to three cysteine residues including Cys-4 and Cys-512, and likely Cys-507. The D589N mutation in a potential calcium binding loop within the C2 domain interfered with the S-acylation of CAPN5, likely preventing initial membrane association. Mutating specific cysteine residues of CAPN5 interfered with both its membrane association and the activation of CAPN5 autolysis. Taken together, our results suggest that the S-acylation of CAPN5 is critical for its membrane localization which appears to favor its enzymatic activity.
An LWE-Based Multi-Key Privacy-Preserving Distributed Deep Learning*

Privacy preservation in distributed deep learning (DDL) has received a lot of attention recently. One key approach to preserving privacy for DDL is the use of Homomorphic Encryption (HE), which allows computation on ciphertexts without decryption. Although current applications of HE provide some level of privacy to DDL, they are not post-quantum resistant and multi-key compliant. The use of same key by all participants could lead to collusion attacks and possible leakage of sensitive information from a participant's local dataset in the distributed deep learning setting. Furthermore, most of the privacy solutions provided for the DDL assume that all learning participants must be present before the start of the protocol. Hence, such solutions do not allow a new participant to benefit from the utility of the joint learning of other participants, for which it was not present. To address the above problems, we propose a new privacy preserving solution for DDL that uses an LWE-based Multi-key approach. Our proposed privacy preserving DDL solution is multi-key compliant, post-quantum resistant and has an additional property of allowing a new participant to benefit from the utility of an already trained collaborative deep learning system. We prove that our privacy-preserving distributed deep learning system is secured. We note that our proposed solution generally provides a robust privacy-preserving solution for the DDL and at the same time has great potential to reduce communication and computation costs associated with similar systems.
Deep Framework for Predicting COVID-19 and Related Lung diseases using CXR Images

Along with a health crisis, COVID-19 has also led the world towards an economical barrier. So far the virus has effected approximately 400 Millions causing 5 Million deaths and is expanding everyday. There is an urge to stop the exponential growth of the contagious disease, only possible through an early diagnosis of the disease. Currently, several testing techniques are being used to diagnose COVID-19, among them Polymerase Chain Reaction (PCR) is a gold standard globally. However, due to it’s processing time, cost and less sensitivity towards COVID-19, physicians suggest to correlate the results with radiological tests preferably Chest X-Ray (CXR) imaging since it consumes less time and is more sensitive towards COVID-19. To overcome the pandemic many research groups have been working on the solution. Several Computer Aided Diagnostic (CAD) systems have been proposed by the researchers however, they lack robustness and stability towards blind datasets. Moreover, majority of the CAD systems provide binary classification between healthy and COVID-19, various lung abnormalities resembles COVID-19 in terms of their structural appearance and can be falsely classified as COVID-19. In this paper, we have proposed a deep model using EfficinetNet-B0 as a baseline model. Our proposed model has been trained on the largest available CXR dataset of COVID-19 comprising CXR images of normal, Viral Pneumonia, Lung Opacity and COVID-19 effected lungs and yielded an accuracy of 99.46%. Proposed model has been blind tested on four publicly available datasets achieving highest accuracy of 99.96%. Furthermore, the model is transfer learned and fine tuned on another publicly available CXR dataset and evaluated to be 85.26% accurate for 20 epochs.
Improving the supply chain using artificial intelligence

Though artificial intelligence, AI, has been around for the last half-century and has greatly improved performances in many industries, the technology has not been fully exploited in supply chain management, SCM. The flows in today’s supply chains are usually fast-moving and dynamic, but are often handled manually. This can inhibit the organizations’ capability to adapt to the quick changes in demand. To be able to quickly respond to these changes, cooperation between the purchasing company and the supplier is needed. Despite this, many companies are afraid to share information with each other, obstructing cooperation. The use of AI could provide a dynamic solution to these problems. The overall objective of this project was to examine how AI could be applied to SCM and what benefits this could enclose. By interviewing people working with SCM, problems within the area and desired solutions could be mapped. As there existed a desire for a system that predicts whether an order will arrive on time, a prototype for such a system was implemented. By analyzing data through experimental testing, using the data mining tool Weka, a suitable AI model could be obtained. It has been concluded that the performance of the model depends on some key conditions. The most crucial factor is the presence of patterns in the supplier’s behavior. If these conditions are taken into consideration, AI can be used within SCM to provide valuable information. The positive effects of displaying this information can benefit both the purchasing company and the supplier.
Scene Recognition with Objectness, Attribute and Category Learning

—Scene classiﬁcation has established itself as a chal- lenging research problem. Compared to images of individual objects, scene images could be much more semantically complex and abstract. Their difference mainly lies in the level of granu-larity of recognition. Yet, image recognition serves as a key pillar for the good performance of scene recognition as the knowledge attained from object images can be used for accurate recognition of scenes. The existing scene recognition methods only take the category label of the scene into consideration. However, we ﬁnd that the contextual information that contains detailed local descriptions are also beneﬁcial in allowing the scene recognition model to be more discriminative. In this paper, we aim to improve scene recognition using attribute and category label information encoded in objects. Based on the complementarity of attribute and category labels, we propose a Multi-task Attribute- Scene Recognition (MASR) network which learns a category embedding and at the same time predicts scene attributes. Attribute acquisition and object annotation are tedious and time consuming tasks. We tackle the problem by proposing a partially supervised annotation strategy in which human intervention is signiﬁcantly reduced. The strategy provides a much more cost-effective solution to real world scenarios, and requires considerably less annotation efforts. Moreover, we re-weight the attribute predictions considering the level of importance indicated by the object detected scores. Using the proposed method, we efﬁciently annotate attribute labels for four large-scale datasets, and systematically investigate how scene and attribute recognition beneﬁt from each other. The experimental results demonstrate that MASR learns a more discriminative representation and achieves competitive recognition performance compared to the state-of-the-art methods.
A KINEMATICALLY REPETITIVE MULTIPLE ARMS FOR INDUSTRIAL TASKS

Our approach is sensor-based, allowing versatility in task specs and execution. We consider robots with multiple kinematically redundant arms. Such robots can tackle a much wider selection of tasks than the usual single arm, but simultaneously incur elevated complexity when it comes to potential collision in addition to pressure of interaction in collaborative tasks. Industrial robots allow limited feedback from sensors, for example vision or pressure/torque sensors, through command trajectory modification. When multiple arms jointly hold a lot, additionally towards the motion from the load, the interior pressure inside the load must be controlled for stable grasping while staying away from damaging the part. The primary aspects of the machine include vision-led motion control, redundancy resolution, collision avoidance, squeeze pressure control, load compensation, and human gestural interface. We used a commercial robot controller, and despite its significant time delay, we could achieve robust performance for complex motion and pressure objectives. We exploit kinematic redundancy within our system to prevent collision and enhance manipulability and grasp stability by using potential fields. Motivated by fabric layups in composites manufacturing, we're also investigating an alternative around the earlier discussed complementarily pressure control condition in which, rather of applying a squeeze pressure on the rigid body, the robot must conserve a prescribed tension inside a flexible object during motion. Using the Lyapunov function , you can easily reveal that the positioning and orientation error will converge when we ignore feedback delay. We decide RR over other distributed automatic middleware systems for example ROS Industrial because of its multiplatform compatibility. We make use of the well-known integral pressure control, having a pressure-dependent gain to boost sturdiness and show closed loop stability for any compliant object.
Endocrinology and Metabolism: Long-Term Stability of Amino Acids and Acylcarnitines in Dried Blood Spots

Background: Dried blood filter cards, collected for newborn screening, are often stored for long periods of time. They may be suitable for the retrospective diagnosis of inborn errors of metabolism, but no data are currently available on the long-term stability of amino acids and acylcarnitine species. Methods: We analyzed amino acids and acylcarnitines by tandem mass spectrometry in 660 anonymous, ran-domly selected filter cards from 1989 through 2004. We assessed long-term stability of metabolites by linear regression and estimated annual decrease of concentration for each metabolite. Results: Concentrations of free carnitine increased by 7.6% per year during the first 5 years of storage and decreased by 1.4% per year thereafter. Alanine, arginine, leucine, methionine, and phenylalanine decreased by 6.5%, 3.3%, 3.1%, 7.3%, and 5.7% per year, respectively. Acetylcarnitine, propionylcarnitine, citrulline, glycine, and ornithine decreased by 18.5%, 27.4%, 8.1%, 14.7%, and 16.3% per year during the first 5 years, respectively; thereafter the decline was more gradual. Tyrosine decreased by 1.7% per year during the first 5 years and 7.9% per year thereafter. We could not analyze medium-and long-chain acylcarnitine species because of low physiological concentrations. Conclusions: Estimation of the annual decrease of metabolites may allow for the retrospective diagnosis of inborn errors of metabolism in filter cards that have been stored for long
Enhancement Encoding: A New Imbalanced Classification Approach via Encoding the Labels

Class imbalance, which is also called long-tailed distribution, is a common problem in classiﬁcation tasks based on machine learning. If it happens, the minority data will be overwhelmed by the majority, which presents quite a challenge for data science. To address the class imbalance problem, researchers have proposed lots of methods: some people make the data set balanced (SMOTE [6]), some others reﬁne the loss function (Focal Loss [33]), and even someone has noticed the value of labels inﬂuences class-imbalanced learning (Yang and Xu. Rethinking the value of labels for improving class-imbalanced learning. In NeurIPS 2020 [48]), but no one changes the way to encode the labels of data yet. Nowadays, the most prevailing technique to encode labels is the one-hot encoding due to its nice performance in the general situation. However, it is not a good choice for imbalanced data, because the classiﬁer will treat majority and minority samples equally. In this paper, we innovatively propose the enhancement encoding technique, which is specially designed for the imbalanced classiﬁcation. The enhancement encoding combines re-weighting and cost-sensitiveness, which can reﬂect the difference between hard and easy (or minority and majority) classes. In order to reduce the number of validation samples and the computation cost, we also replace the confusion matrix with the novel soft-confusion matrix which works better with a small validation set. In the experiments, we evaluate the enhancement encoding with three different types of loss. And the results show that enhancement encoding is very effective to improve the performance of the network trained with imbalanced data. Particularly, the performance on minority classes is much better.
Disconnection confounded at various stages of knowledge transfer from the academe to the hospital

There is a recognized gap between the knowledge produced in the academe and practice in the health care institutions. An understanding on the elements that contributed to the disconnection confounded at various stages of knowledge transfer helps address the phenomenon. Hence, this qualitative research sought to confirm the disconnection confounded at various stages of knowledge transfer from the academe to the end- users. It revealed that the success in the transfer of knowledge is linked to the policy and organizational environment support, dissemination and utilization mechanisms and dynamic interaction between the Higher Education Institutions (HEIs) and the hospitals. Thus, this research primarily confirmed the disconnection confounded at various stages of knowledge transfer from the academe to the end-users are due to: (a) dysfunctional policy and organizational environment at both ends; (b) ineffective dissemination and utilization mechanisms; and (c) absence of the dynamic interaction between the would- be users of research and the academic health researchers”.
Leakage-Resilient Hardness v.s. Randomness

A central open problem in complexity theory concerns the question of whether all efficient randomized algorithms can be simulated by efficient deterministic algorithms. The celebrated “hardness v.s. randomness” paradigm pioneered by Blum-Micali (SIAM JoC’84), Yao (FOCS’84) and Nisan-Wigderson (JCSS’94) presents hardness assumptions under which e.g., prBPP = prP (so-called “high-end derandomization), or prBPP ⊆ prSUBEXP (so-called “low-end derandomization), and more generally, under which prBPP ⊆ prDTIME ( C ) where C is a “nice” class (closed under composition with a polynomial), but these hardness assumptions are not known to also be necessary for such derandomization. In this work, following the recent work by Chen and Tell (FOCS’21) that considers “almost-all-input” hardness of a function f (i.e., hardness of computing f on more than a finite number of inputs), we consider “almost-all-input” leakage-resilient hardness of a function f – that is, hardness of computing f ( x ) even given, say, p | x | bits of leakage of f ( x ). We show that leakage-resilient hardness characterizes derandomization of prBPP (i.e., gives a both necessary and sufficient condition for derandomization), both in the high-end and in the low-end setting. In more detail, we show that there exists a constant c such that for every function T , the following are equivalent:
Dalton: Learned Partitioning for Distributed Data Streams

To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability Deciding on a static partitioning for a given set of "hot" keys is not sufficient as these keys are not known in advance, and even worse, the data distribution can change unpredictably. Existing algorithms either optimize for a specific distribution or, in order to adapt, assume a centralized partitioner that processes every incoming tuple and observes the whole workload. However, this is not realistic in a distributed environment, where multiple parallel upstream operators exist, as the centralized partitioner itself becomes the bottleneck and limits scalability In this work, we propose Dalton: a lightweight, adaptive, yet scalable partitioning operator that relies on reinforcement learning. By memoizing state and dynamically keeping track of recent experience, Dalton: i) adjusts its policy at runtime and quickly adapts to the workload, ii) avoids redundant computations and minimizes the per-tuple partitioning overhead, and iii) efficiently scales out to multiple instances that learn cooperatively and converge to a joint policy Our experiments indicate that Dalton scales regardless of the input data distribution and sustains 1.3X - 6.7X higher throughput than existing approaches.
Acceleration of Multi-b-value Multi-shot Diffusion-weighted Imaging using Interleaved Keyhole-EPI and Locally Low Rank Reconstruction

Muti-b-value Diffusion Weighted Imaging (DWI) is commonly used in clinical and neuroscientific applications. The traditional single-shot Echo-Planer Imaging (EPI) sequence suffers from low image resolution. Although the multi-shot EPI sequence can increase spatial resolution, the multi-shot k-space sampling causes linearly increased scan time. An interleaved EPI acquisition can significantly reduce the scan time; however, the dynamic change of image phase and image contrast causes aliasing artifacts. To improve the scan efficiency and preserve the image quality, an interleaved keyhole-EPI multi-b-value multi-shot sequence is proposed, with the image reconstruction formulated as a Locally Low Rank (LLR) constrained problem. The resultant cost function is minimized by a computationally efficient ADMM algorithm. The proposed method was compared with interleaved EPI acquisition using the state-of-the-art SPatial-Angular Locally Low Rank (SPA-LLR) algorithm in two healthy subjects. The results showed that the proposed method achieved superior image quality and fewer aliasing artifacts compared with the state-of-the-art method in both the raw DWI images and Apparent Diffusion Coefficient (ADC) maps.
Analysis of Amniotic fluid in fetal assessment using Biophysical Profile Images

The assessment of prenatal Amniotic Fluid Volume (AFV) is critical because it provides essential information on fetal development and well-being, including perinatal prognosis. AFV measurement, on the other hand, is time-consuming and patient-specific. Furthermore, it depends on the sonographer, with measurement accuracy ranging substantially depending on the sonographer's experience. As a result, developing accurate, resilient, and adaptable methodologies to assess AFV is essential. Automation is expected to diminish user-based variability and the sonographer burden. Nevertheless, automation of AFV measurement is problematic because of several confounding variables, making precise recognition of AF pockets challenging. Furthermore, the forms and sizes of AF pockets are unknown, and ultrasound images frequently reveal them caused by missing structural boundaries. The suggested approach is to construct an Accurate and Efficient Deep Convolutional Neural Network (AEDCN-Net) predicated on an intensive preprocessing phase and a resourcefulness model structure to address the identified challenges. The AEDCN-Net uses asymmetric convolution-based residual skip interconnections in the processing path to learn feature representations with a broader receptive field while reducing the number of training data and floating-point operations (FLOPs). automatically generated when manuscripts are processed after acceptance.