Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Ultrawide Band Ranging Using Two Antennas

Ultrawideband technology can be used to measure the distance between two device equipped with ultrawideband transceivers. Multiple ultrawideband devices known as anchors can localize a device that supports ultrawideband communication after each anchor measures the distance between itself and the device. The anchor and the tag can measure the distance between themselves by measuring the time of flight of the ultrawideband messages exchanged between them. If the location of the anchors are known, the location of the device can be computed. However, errors in range measurements can cause errors in localization of the device. There are many sources that can cause errors in the measured range between the anchor and the tag. For example, when the tag transmits a signal, and the path between the anchor and the tag is blocked, the signal may be attenuated to such an extent that it may go undetected at the anchor, causing the anchor to measure the time of arrival of a delayed reflection. In this thesis, we aim to find a solution to improve the ranging accuracy when an anchor is equipped with two antennas. We target approaches that can be implemented on an embedded processor with limited resources. In order to improve the ranging accuracy, we first investigate various machine learning based classifiers that can identify non-line of sight and line of sight signals. Next, for each of the classified signal, we train and test various machine learning based regression models that will predict the true distance from the distance measured by the anchor. We conclude the thesis, by assessing the robustness of the derived classification and regression models by evaluating the range improvements in measurements taken in different scenarios.
Small Angle Neutron Scattering studies of Lysozyme solutions

Biomolecules such as proteins or nucleic acids are polymers that sustain life, with unique structures and broad scale dynamics. Inherent to their complexity, and ability to function in a variety of physicochemical environments, is a sensitivity that often renders biomolecules challenging to study. Typical samples are (i) isolated in very limited amounts, (ii) stable for relatively short periods of time and (iii) prone to aggregation or polydispersity. Small Angle Neutron Scattering studies of biomolecules (BioSANS) require careful planning. A well-known model protein will be used to provide an example of common pitfalls - even for proteins generally considered structurally robust such as lysozyme - while highlighting the tremendous potential of BioSANS. Students will simulate SANS curves to compare to experimental data collected for lysozyme solutions, where subtle variations in ionic strength were introduced. During the BioSANS course students will assess data collection strategies, learn how contrast variation can be used, reduce SANS data collected at the NCNR and fit it using form and structure factor models.
DETECTING AND CAPTIONING IMAGES USING DEEP NEURAL NETWORKS AND FLASK

: Captioning images automatically is one of the heart of the human visual system. There are various advantages if there is an application which automatically caption the scenes surrounded by them and revert back the caption as a plain message. In this paper, we present a model based on CNN-LSTM neural networks which automatically detects the objects in the images and generates descriptions for the images. It uses various pre-trained models to perform the task of detecting objects and uses CNN and LSTM to generate the captions. It uses Transfer Learning based pre-trained models for the task of object Detection. This model can perform two operations. The first one is to detect objects in the image using Convolutional Neural Networks and the other is to caption the images using RNN based LSTM(Long Short Term Memory). Interface of the model is developed using flask rest API, which is a web development framework of python. The main use case of this project is to help visually impaired to understand the surrounding environment and act according to that.
Constraint Propagation based Approach for Simultaneous Localization And Mapping of Mobile Robots ∗

Simultaneous Localization and Mapping (SLAM) problems have been classically solved by probabilistic methods (EKF-SLAM/Fast-SLAM) or Optimization method (Graph-SLAM). A common issue confronted by these methods is the consistency problem. This paper proposes a new method which theoretically relys on interval analysis to solve the SLAM problem. The aim is to achieve consistent solutions. Diﬀerent from other methods which require probability distribution assumptions on uncertainties, only a soft prerequriste is needed (the errors are bounded). With proposed bounded-error parametric models, we cast the SLAM problem into an Interval Constraint Satisfaction Problem (ICSP), constraint propagation techniques are then applied to search for all feasible solutions, providing a guaranteed rather than a probabilistically deﬁned result. Our method has been validated on both simulations and experimentations. Results demonstrate the capability of the our method in obtaining consistent solutions.
LncRNA SNHG14 promotes cell proliferation and invasion in colorectal cancer through modulating miR-519b-3p/DDX5 axis

Numbers of studies suggest that long non-coding RNAs (lncRNAs) exert an important role in cancer progression. It is reported that lncRNA SNHG14 (SNHG14) promotes cell proliferation and invasion in many cancers. However, the underlying molecular mechanism of SNHG14 in colorectal cancer (CRC) remains unclear. In our study, we found that SNHG14 is highly expressed in CRC tissues and cells, especially in SW480 and HT-29 cells. In addition, sh-SNHG14 inhibits cell proliferation, cell migration and invasion, promotes cell apoptosis in CRC cell lines. Furthermore, we found that SNHG14 functions as a sponge for miR-519b-3p, while the DEAD box protein 5 (DDX5) is a downstream target gene of miR-519b-3p, and the functions of miR-519b-3p inhibitors on the CRC progression could be rescued by downregulation of DDX5. Our findings suggest that SNHG14 promotes the CRC progression by miR-519b-3p/DDX5 axis, implying the promising therapeutic target of SNHG4 for CRC patients.
Artificial Intelligence in Cyber Security

- In the field of Cyber Security there has been a development from the stage of Cyber Criminality to the stage of Cyber War over the last few years. Even though security devices are becoming modern and powerful, cyber-attack or cyber threats are increasing day by day. The main cause for this cyber-attack is our traditional threat detection methods to identify the threat or malware are falling apart. Cybercriminals are every day coming up with competent ways to bypass the computer security devices or programs and infect the network and computer systems with various kinds of malicious software’s. Artificial Intelligence changed each area it has been introduced. Credit Card Fraud Detection and Spam Filtering becomes possible due to the use of ML and AI algorithms these algorithms can study from historical fraud data and identify outliers and recognize them in future transactions. AI Algorithms able to works more efficient than humans when it comes to the data processing speed. Also, AI Algorithms can identify most difficult hidden fraud traces that a human cannot detect easily. AI can help to reduce number of false positives that may arise with use of out-of-date fraud identification methods. Artificial Intelligence methods has discovered that this is similarly efficient for all stages of the Cyber Security.
THE IDENTIFICATION OF RESULTS OF INSPECTION IN LOGISTICS USING DECISION TREE CLASSIFIER AND DTREEVIZ

: Due to inadvertent mistakes in the production process, the manufacturer processes the step called inspection for checking and assuring the quality of the products. The inspection helps to rectify the mistakes or damage in the products and also increases customer satisfaction by delivering good quality products to the customers. The damage rate of the product and inspected quantity are used to classify in the decision tree for decision making. The main aim of the study is to identify the inspection results that “how many products were passed; how many went to rework and how many products were failed in the Inspection Process” using Decision tree classifier and dtreeviz in Jupyter Notebook. The inspected product with pass result products will move to the next process called loading.
Nano-immunotherapy for each stage of cancer cellular immunity: which, why, and what?

Immunotherapy provides a new avenue for combating cancer. Current research in anticancer immunotherapy is primary based on T cell-mediated cellular immunity, which can be divided into seven steps and is named the cancer-immunity cycle. Unfortunately, clinical applications of cancer immunotherapies are restricted by inefficient drug delivery, low response rates, and unmanageable adverse reactions. In response to these challenges, the combination of nanotechnology and immunotherapy (nano-immunotherapy) has been extensively studied in recent years. Rational design of advanced nano-immunotherapies requires in-depth consideration of “which” immune step is targeted, “why” it needs to be further enhanced, and “what” nanotechnology can do for immunotherapy. However, the applications and effects of nanotechnology in the cancer-immunity cycle have not been well reviewed. Herein, we summarize the current developments in nano-immunotherapy for each stage of cancer cellular immunity, with special attention on the which, why and what. Furthermore, we summarize the advantages of nanotechnology for combination immunotherapy in two categories: enhanced efficacy and reduced toxicity. Finally, we discuss the challenges of nano-immunotherapy in detail and provide a perspective.
Exploring the Relationship Between Intrinsic Motivation and Receptivity to mHealth Interventions

Recent research in mHealth has shown the promise of Just-in-Time Adaptive Interventions (JITAIs). JITAIs aim to deliver the right type and amount of support at the right time. Choosing the right delivery time involves determining a user’s state of receptivity, that is, the degree to which a user is willing to accept, process, and use the intervention provided. Although past work on generic phone notifications has found evidence that users are more likely to respond to notifications with content they view as useful, there is no existing research on whether users’ intrinsic motivation for the underlying topic of mHealth interventions affects their receptivity. In this work, we explore whether relationships exist between intrinsic motivation and receptivity across topics and within topics for mHealth interventions. To this end, we conducted a study with 20 participants over 3 weeks, where participants received interventions about mental health, COVID-19, physical activity, and diet & nutrition. The interventions were delivered by the chatbot-based iOS app called Elena+, and via the MobileCoach platform. Our exploratory analysis found that significant differences in mean intrinsic motivation scores across topics were not associated with differences in mean receptivity metrics across topics. We also found that positive relationships exist between intrinsic motivation measures and receptivity for interventions about a topic.
Design of Standoff Cooperative Target-Tracking Guidance Laws for Autonomous Unmanned Aerial Vehicles

This paper investigates two guidance laws of standoff cooperative tracking static and moving of multiple autonomous unmanned aerial vehicles for targets from the perspective of the control system design. In the scheme of the proposed guidance laws, one vehicle is chosen as leader and others as followers. The leader only needs the measurement of the target, and the followers only measure the leader and its neighbors in the communication topology network. By using the proposed guidance laws, it is guaranteed that all vehicles can track a static or moving target with an evenly spaced formation of circle. Considering the coupling of tracking and cooperation, the stability analysis is performed by constructing two relatively independent subsystems based on Lyapunov theory, and the corresponding rigorous proofs of stability are given. By comparing with the Lyapunov vector field guidance law, the simulation results verify the effectiveness and superiority of the proposed guidance laws.
BlockMaestro: Enabling Programmer-Transparent Task-based Execution in GPU Systems

As modern GPU workloads grow in size and complexity, there is an ever-increasing demand for GPU computational power. Emerging workloads contain hundreds or thousands of GPU kernel launches, which incur high overheads, and exhibit data-dependent behavior between kernels, which requires synchronization, leading to GPU under-utilization. Task-based execution models have been proposed to solve these issues, but they require significant programmer effort to port applications to proprietary task-based programming models in order to specify tasks and task dependencies. To address this need, we propose BlockMaestro, a software-hardware solution that combines command queue reordering, kernel-launch-time static analysis, and runtime hardware support to dynamically identify and resolve thread-block level data dependencies between kernels. Through static analysis of memory access patterns at kernel-launch-time, BlockMaestro can extract inter-kernel thread block-level data dependencies. BlockMaestro also introduces kernel pre-launching to reduce the kernel launch overheads experienced by multiple dependent kernels. Correctness is enforced by dynamically resolving thread block-level data dependency at runtime through hardware support. BlockMaestro achieves an average speedup of 51.76% (up to 2.92x) on data-dependent benchmarks, and requires minimal hardware overhead.
The Need to Reinsert Subjectivity in Medical Care

Without ignoring the warm and prudent style that many doctors can radiate individually in the treatment they provide to their patients, the truth is that this bioethical concern is not consistent with the prevalent positivist matrices that sustain medicine as a social institution. As we will see, the discourse of a discipline is the linguistic form in which knowledge is exposed. The words that a speech reiterates (its vocabulary) reflect both the way of "saying" and that of "thinking". In order to the absence of terms referring to subjectivity in medical discourse, we will take care to point out the healthcare consequences that depend on this conceptual exclusion. When the object of a science is also a subject, disregarding considerations related to this aspect carries significant clinical risks. Reinserting the subject in care, leaving the exclusive objectification, prevents the medicine from causing the same thing that it intends to treat.
Visible-Light Photocatalysis of Aerobic Oxidation Reactions Using Carbazolic Conjugated Microporous Polymers

Carbazolic conjugated microporous polymer (C-CMP) is obtained via straightforward carbazole-based oxidative coupling polymerization. C-CMP exhibits high porosity and a specific surface area of 1137 m2 g–1, and it is thermally stable to 600 °C in nitrogen. C-CMP is shown to be a highly effective heterogeneous photocatalyst for a wide range of reactions, including the oxidative coupling of primary amines, aerobic dehydrogenation of nonactive secondary amine substrates such as pharmaceutically relevant nitrogen heterocycles, and selective oxidation of sulfide using molecular oxygen and visible light. This work highlights the potential of developing photoactive N-containing CMPs as a highly stable, molecularly tunable, reusable, and metal-free visible light photocatalysts for a wide variety of organic transformations.
Review on security and privacy issues in health information systems adoption and usage

Health information systems (HIS) are major applications in the field of Information and Communication Technology (ICT). Lots of benefits and advantages can be obtained if we are to apply HIS systems in real world and this would serve healthcare providers, professionals and health population as a whole. However, these advantages of EMR, EHR and PHR cannot be accomplished if users do not adopt health information system in their healthcare dealing. Recent publication highlighted on the lack of adopting EMR, EHR and PHR in healthcare field. There, this paper aims to review the privacy and security factors effect on health information system acceptance and usage. The Results indicated that empirical studies that examined privacy and security factors in healthcare context are limited, particularly in health information system acceptance and usage; in addition to that very limited studies have applied UTAUT2 in healthcare domain including health information system acceptance.
RaPiD: AI Accelerator for Ultra-low Precision Training and Inference

The growing prevalence and computational demands of Artificial Intelligence (AI) workloads has led to widespread use of hardware accelerators in their execution. Scaling the performance of AI accelerators across generations is pivotal to their success in commercial deployments. The intrinsic error-resilient nature of AI workloads present a unique opportunity for performance/energy improvement through precision scaling. Motivated by the recent algorithmic advances in precision scaling for inference and training, we designed RaPiD1, a 4-core AI accelerator chip supporting a spectrum of precisions, namely, 16 and 8-bit floating-point and 4 and 2-bit fixed-point. The 36mm2 RaPiD chip fabricated in 7nm EUV technology delivers a peak 3.5 TFLOPS/W in HFP8 mode and 16.5 TOPS/W in INT4 mode at nominal voltage. Using a performance model calibrated to within 1% of the measurement results, we evaluated DNN inference using 4-bit fixed-point representation for a 4-core 1 RaPiD chip system and DNN training using 8-bit floating point representation for a 768 TFLOPs AI system comprising 4 32-core RaPiD chips. Our results show INT4 inference for batch size of 1 achieves 3 - 13.5 (average 7) TOPS/W and FP8 training for a mini-batch of 512 achieves a sustained 102 - 588 (average 203) TFLOPS across a wide range of applications.
Exosomal IDH1 increases the resistance of colorectal cancer cells to 5-Fluorouracil

Chemoresistance challenges the clinical treatment of colorectal cancer and requires an urgent solution. Isocitrate dehydrogenase 1 (IDH1) is a key enzyme involved in glucose metabolism that mediates the malignant transformation of tumors. However, the mechanisms by which IDH1 is involved in colorectal cancer cell proliferation and drug resistance induction remain unclear. In this study, we found that IDH1 was highly expressed in human colorectal cancer tissues and could be used to indicate a high-grade tumor. In vitro gene overexpression and knockdown were used to determine whether IDH1 promoted the proliferation of the colorectal cancer cell line HCT8 and resistance to 5-Fluorouracil (5FU). Further studies have shown that the 5FU-resistant cell line, HCT8FU, secreted exosomes that contained a high level of IDH1 protein. The exosomal IDH1 derived from 5FU-resistant cells enhanced the resistance of 5FU-sensitive cells. Metabolic assays revealed that exosomes derived from 5FU-resistant cells promoted a decrease in the level of IDH1-mediated NADPH, which is associated with the development of 5FU resistance in colorectal cancer cells. Therefore, exosomal IDH1 may be the transmitter and driver of chemoresistance in colorectal cancer and a potential chemotherapy target.
A Literature Study on the Customer Perceived Value

In view of the current dynamic usage environment technological advancement and also interconnected macroeconomic developments, micro-with societal-level researchers and practitioners have shown increasing interest within the concept with perceived value of customers. However, particularly given its large evidential implementation, remarkably tiny effort was made to synthesize the different perspectives mostly on dimensional space, abstract thought and method anatomy of the apparent value of customers. The develop a coherent by the production company of the manner in which the purchaser chooses between different products which might fulfil the same need seems to be a very demanding and complicated issue. Consequently, mostly on grounds of a systematic literature analysis, this paper describes the prevailing conceptual model of customer perceived value of the consumer, while also having a sustainable basis for possible empiric evaluations of this definition. Discuss avenues for further research study. In way that contributes to analysis, this thesis also relates to the tradition of placing the perceived value of the consumer as a core source of comparative advantage throughout regard to marketing, economics and leadership models.
Efficient Multi-GPU Shared Memory via Automatic Optimization of Fine-Grained Transfers

Despite continuing research into inter-GPU communication mechanisms, extracting performance from multi-GPU systems remains a significant challenge. Inter-GPU communication via bulk DMA-based transfers exposes data transfer latency on the GPU’s critical execution path because these large transfers are logically interleaved between compute kernels. Conversely, fine-grained peer-to-peer memory accesses during kernel execution lead to memory stalls that can exceed the GPUs’ ability to cover these operations via multi-threading. Worse yet, these sub-cacheline transfers are highly inefficient on current inter-GPU interconnects. To remedy these issues, we propose PROACT, a system enabling remote memory transfers with the programmability and pipeline advantages of peer-to-peer stores, while achieving interconnect efficiency that rivals bulk DMA transfers. Combining compile-time instrumentation with fine-grain tracking of data block readiness within each GPU, PROACT enables interconnect-friendly data transfers while hiding the transfer latency via pipelining during kernel execution. This work describes both hardware and software implementations of PROACT and demonstrates the effectiveness of a PROACT software prototype on three generations of GPU hardware and interconnects. Achieving near-ideal interconnect efficiency, PROACT realizes a mean speedup of 3.0× over single-GPU performance for 4-GPU systems, capturing 83% of available performance opportunity. On a 16-GPU NVIDIA DGX-2 system, we demonstrate an 11.0× average strong-scaling speedup over single-GPU performance, 5.3× better than a bulk DMA-based approach.
PSMD7 downregulation suppresses lung cancer progression by regulating the p53 pathway

Lung cancer is the second most common cancer in both men and women. The deubiquitinase PSMD7, as a core component of the 26S proteasome, is critical for the degradation of ubiquitinated proteins in the proteasome. Currently, PSMD7 expression and its roles in the progression of lung cancer remain largely unknown. In this study, we assessed PSMD7 expression and investigated the underlying molecular events by which PSMD7 regulates tumor progression in non-small cell lung cancer (NSCLC). The results showed that PSMD7 is more highly expressed in NSCLC tissues than in adjacent noncancerous tissues. PSMD7 expression was also closely associated with lymph node invasion and the laterality of the tumor in lung adenocarcinoma (LUAD). A high PSMD7 level predicted poor overall survival (OS) and disease-free survival (DFS) in LUAD patients, and PSMD7 knockdown significantly reduced cell proliferation and induced G0/G1-phase cell cycle arrest, cell senescence and apoptosis. PSMD7 knockdown inhibited expression of a set of proteins regulating cell cycle progression. Depletion of PSMD7 increased p53 levels and induced p21 and puma expression in a p53-dependent manner. Importantly, knockdown of PSMD7 markedly inhibited LUAD tumor growth in a xenograft mouse model. Taken together, these findings indicate that PSMD7 may serve as a valuable prognostic indicator and potential therapeutic target in LUAD.