Miracle-dz/UniFAR-FTUs huggingface dataset:
Test Modules Design for a SerDes Chip in 130 nm CMOS technology

In this thesis, a DFT architecture is proposed for testing a high-speed SerDes circuit. This architecture suggests the implementation of three testing modules: a comparator, a linear-feedback shift register (LFSR) and a signal driver. By embedding these test modules to the SerDes, the circuit will be able to perform eight different operating modes for testing in addition to the functional mode. With these operating modes, the functionality of all the modules individually and collectively can be tested by the use of multiplexers and BIST.
Economic scheduling model of microgrid considering the lifetime of batteries

Because of the uncertainty of renewable energy generation and load, batteries as energy storage devices play an important role in ensuring the safety and reliability of microgrid. To take full advantage of the batteries, the battery lifetime characteristics are analysed, and a weighted Wh throughput method is proposed to estimate the battery lifetime. To improve the economy of microgrid, an economic scheduling model of microgrid in grid-connected mode is established with the consideration of battery lifetime. For fast and efficiently solving the model, a technique is developed to convert the optimisation problem into a mixed integer linear programming problem and a mixed integer linear programming algorithm is applied to solve it. The proposed method has been validated on a microgrid, which consists of a wind turbine, a photovoltaic system, a micro turbine, a fuel cell, a diesel engine and a battery energy storage system (BESS). The simulation results show that the BESS is managed rationally and the total operation cost of microgrid over scheduling period is decreased by applying the proposed scheduling method.
Metastatic Adenocarcinoma of Lungs, Mimicking Primary Scapular Tumor: A Case Report and Review of Literature

Introduction: Cancer metastasis to the scapula and tibia is an infrequent clinical presentation. Lung cancer is one of the leading causes of death in the world. Around 80–85% of lung cancer are non-small cell lung cancer and around 30–35% of them are adenocarcinoma which metastasize to bone. Scapular metastasis is a rare event and presents with bone pain. Case Report: In this case report of 56-year-old male, presented with swelling to the scapula as a first symptom followed by swelling in the tibia. On further investigation, it was found to be multiple metastasis primaries as an adenocarcinoma of the lung. He was managed with palliative chemotherapy, which enhanced the patient’s quality of life, slowing down the disease process and clinical improvement. Conclusion: Lung cancer is the most common cancer which is progressing rapidly and spreading commonly to bones. When patients present with a mass or any suspicion of tumor, careful clinical examination and diagnostic tools often catch malignancy.
İç ve Dış Hava Ortamlarında Partiküler Madde (Pm10, Pm2.5 ve Pm1) Konsantrasyonlarının Değerlendirilmesi

Gerceklestirilen bu calisma, 0-2 yas donemi cocuklarin vakitlerinin onemli bir kismini gecirdikleri kapali ortamlardaki partikul madde (PM10, PM2.5 ve PM1) seviyelerinin ve kaynaklarinin belirlenmesi ile sonuclarin ev ve cevre kosullarina gore etkilerinin incelenmesi uzerinedir. Tez calismasi kapsaminda Ankara ilinin cesitli semtlerinde, evlerin oturma ve bebek odasi ile dis ortamlarinda PM olcumleri gerceklestirilmistir. Calisma sirasinda arastirmaya katilan ailelerin evlerinde eszamanli olarak PM10, PM2.5 ve PM1 olcumleri gerceklestirilerek, ayni zamanda bu ortamlarda yasayan bireylerin yasam aliskanliklari, ev ve cevre kosullari da kayit altinda tutulmustur. Bazi olcum donemlerinde sicaklik, bagil nem ve CO2 konsantrasyonlari da anlik olarak olculmustur. Calismada, 6 saniye araliklar ile elektronik kayit saglayan, havadan tasinan partikullerin konsantrasyonunu olcmek icin gelistirilmis Grimm Environmental Dust Monitor (EDM) 107 toz konsantrasyonu olcum cihazi kullanilmistir.
Compensation, stability and losses in the presence of wheeling transactions with use of FACTS devices

Integration of FACTS devices and wheeling transactions into SCADA systems is investigated. The SVC, TCPS and UPFC are explained and modelled. Devices, policies and accounting methods are incorporated into PF and OPF algorithms. In two case studies, the accounting methods are analysed and compared for a wheeling contract across a transmission line. With the aid of FACTS devices the power flow can also be forced along the physical contract path in networks, thereby shielding other network participants. A case study on the 39 bus IEEE New England power system shows that FACTS devices can be used for an electronic fence during wheeling transactions. Accounting methods are shown. The stability and operating cost of the grid are investigated. It is found that TCPSs and UPFCs introduce nonconvexities, causing OPF difficulties.
The Development of A Homecare Fetching Robot for the Elderly

With increasing number of aging people, the healthy care of the elderly will become more important in the future, especially for the elderly who lives alone. The main purpose of this paper was to develop a homecare fetching robot for the elderly. The homecare fetching robot was constructed by the integration of mechanism design and mechatronics techniques. Using the wireless remote controller, the elderly who had difficulty in walk could send a signal to the robot. Then, the robot would follow the designed trail to the desired position and fetch the desired object and brought it to the elderly according to the signal. The operation was easy and simple. Therefore, the quality of the elder’s life and look-after will be improved and the burdens of medical personnel or the family will be reduced. It is expected that the elder’s care can be beneficial from using the appropriate adoption of technology. The experimental results demonstrated the effectiveness and merits of the proposed homecare fetching robot.
Functional Magnetic Resonance Imaging of the amygdala and subregions at 3 Tesla: A scoping review protocol

Abstract Background Functional Magnetic Resonance Imaging (fMRI) is a widely accepted and utilised method of investigating neural activation within the brain. There has been increasing awareness and understanding in the field of neuropsychology over the last 10-15 years that the amygdala plays an important role in many mental health conditions. Functional connectivity (FC) of the amygdala with other parts of the brain is well-documented in the literature; however the role of the amygdala and its reported connections is still not well understood and this can be attributed, in part, to its very small size. It is challenging to achieve adequate spatial resolution to visualise amygdala activation using 3T MRI systems that are in widespread use for this type of clinical research. Optimisation of protocols for improved data accuracy and reproducibility may potentially lead to standardisation and subsequent advancements in overall image quality in this field. Methods The protocol for this scoping review was developed in line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and registered with the Open Science Framework (OSF). A literature search of five databases (Medline, Embase, Web of Science, Google Scholar and Scopus) will be undertaken using a refined search strategy; peer-reviewed publications identified as being relevant will then be imported into Covidence software for abstract screening and data extraction by two reviewers working independently. The quantitative findings will be tabulated to provide an overview of the current methodologies for comparison. This will be accompanied by a narrative report summarising the extracted data in relation to the stated research questions. Discussion The objective of this scoping review is to identify and map the range of existing protocols used in fMRI for imaging the activation and FC patterns of the amygdala at 3 Tesla. This will be achieved by collating and presenting quantitative data relating to protocol parameter choices as well as other qualitative aspects of the data acquisition process.
Accelerated MRI with Deep Linear Convolutional Transform Learning

Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple applications. Unlike CS that is typically implemented with pre-determined linear representations for regularization, DL inherently uses a non-linear representation learned from a large database. Another line of work uses transform learning (TL) to bridge the gap between these two approaches by learning linear representations from data. In this work, we combine ideas from CS, TL and DL reconstructions to learn deep linear convolutional transforms as part of an algorithm unrolling approach. Using end-to-end training, our results show that the proposed technique can reconstruct MR images to a level comparable to DL methods, while supporting uniform undersampling patterns unlike conventional CS methods. Our proposed method relies on convex sparse image reconstruction with linear representation at inference time, which may be beneficial for characterizing robustness, stability and generalizability.
AFSC: Adaptive Fourier Space Compression for Anomaly Detection

Anomaly Detection (AD) on medical images enables a model to recognize any type of anomaly pattern without lesion-specific supervised learning. Data augmentation based methods construct pseudo-healthy images by"pasting"fake lesions on real healthy ones, and a network is trained to predict healthy images in a supervised manner. The lesion can be found by difference between the unhealthy input and pseudo-healthy output. However, using only manually designed fake lesions fail to approximate to irregular real lesions, hence limiting the model generalization. We assume by exploring the intrinsic data property within images, we can distinguish previously unseen lesions from healthy regions in an unhealthy image. In this study, we propose an Adaptive Fourier Space Compression (AFSC) module to distill healthy feature for AD. The compression of both magnitude and phase in frequency domain addresses the hyper intensity and diverse position of lesions. Experimental results on the BraTS and MS-SEG datasets demonstrate an AFSC baseline is able to produce promising detection results, and an AFSC module can be effectively embedded into existing AD methods.
MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration

Multimodal video-audio-text understanding and generation can benefit from datasets that are narrow but rich. The narrowness allows bite-sized challenges that the research community can make progress on. The richness ensures we are making progress along the core challenges. To this end, we present a large-scale video-audio-text dataset MUGEN, collected using the open-sourced platform game CoinRun [11]. We made substantial modifications to make the game richer by introducing audio and enabling new interactions. We trained RL agents with different objectives to navigate the game and interact with 13 objects and characters. This allows us to automatically extract a large collection of diverse videos and associated audio. We sample 375K video clips (3.2s each) and collect text descriptions from human annotators. Each video has additional annotations that are extracted automatically from the game engine, such as accurate semantic maps for each frame and templated textual descriptions. Altogether, MUGEN can help progress research in many tasks in multimodal understanding and generation. We benchmark representative approaches on tasks involving video-audio-text retrieval and generation. Our dataset and code are released at: https://mugen-org.github. io/.
On Ozaki’s theorem realizing prescribed
p-groups as p-class tower groups

We give a streamlined and effective proof of Ozaki's theorem that any finite $p$-group $\Gamma$ is the Galois group of the $p$-Hilbert class field tower of some number field $\rm F$. Our work is inspired by Ozaki's and applies in broader circumstances. While his theorem is in the totally complex setting, we obtain the result in any mixed signature setting for which there exists a number field ${\rm k}_0$ with class number prime to $p$. We construct ${\rm F}/{\rm k}_0$ by a sequence of ${\mathbb Z}/p$-extensions ramified only at finite tame primes and also give explicit bounds on $[{\rm F}:{\rm k}_0]$ and the number of ramified primes of ${\rm F}/{\rm k}_0$ in terms of $\# \Gamma$.
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning

Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep learning based recommendation algorithms nowadays. In this paper we discuss the limited expressiveness of these labels may fail to accommodate varying degrees of user preference, and thus lead to conflicts during model training, which we call annotation bias. To solve this issue, we find the soft-labeling property of pairwise labels could be utilized to alleviate the bias of pointwise labels. To this end, we propose a momentum contrast framework (\method ) that combines pointwise and pairwise learning for recommendation. \method has a three-tower network structure: one user network and two item networks. The two item networks are used for computing pointwise and pairwise loss respectively. To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation. Extensive experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommendation algorithms.
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition

Subject-invariant facial action unit (AU) recognition remains challenging for the reason that the data distribution varies among subjects. In this paper, we propose a causal inference framework for subject-invariant facial action unit recognition. To illustrate the causal effect existing in AU recognition task, we formulate the causalities among facial images, subjects, latent AU semantic relations, and estimated AU occurrence probabilities via a structural causal model. By constructing such a causal diagram, we clarify the causal-effect among variables and propose a plug-in causal intervention module, CIS, to deconfound the confounder Subject in the causal diagram. Extensive experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the effectiveness of our CIS, and the model with CIS inserted, CISNet, has achieved state-of-the-art performance.
Easy-Sec: PUF-Based Rapid and Robust Authentication Framework for the Internet of Vehicles

With the rapid growth of new technological paradigms such as the Internet of Things (IoT), it opens new doors for many applications in the modern era for the betterment of human life. One of the recent applications of the IoT is the Internet of Vehicles (IoV) which helps to see unprecedented growth of connected vehicles on the roads. The IoV is gaining attention due to enhancing traffic safety and providing low route information. One of the most important and major requirements of the IoV is preserving security and privacy under strict latency. Moreover, vehicles are required to be authenticated frequently and fast considering limited bandwidth, high mobility, and density of the vehicles. To address the security vulnerabilities and data integrity, an ultralight authentication scheme has been proposed in this article. Physical Unclonable Function (PUF) and XOR function are used to authenticate both server and vehicle in two message flow which makes the proposed scheme ultralight, and less computation is required. The proposed Easy-Sec can authenticate vehicles maintaining low latency and resisting known security threats. Furthermore, the proposed Easy-Sec needs low overhead so that it does not increase the burden of the IoV network. Computational ( around 4 ms) and Communication (32 bytes) overhead shows the feasibility, efficiency, and also security features are depicted using formal analysis, Burrows, Abadi, and Needham (BAN) logic, and informal analysis to show the robustness of the proposed mechanisms against security threats.
The Z-axis, X-axis, Weight and Disambiguation Methods for Constructing Local Reference Frame in 3D Registration: An Evaluation

—The local reference frame (LRF), as an independent coordinate system generated on a local 3D surface, is widely used in 3D local feature descriptor construction and 3D transformation estimation which are two key steps in the local method-based surface matching. There are numerous LRF methods have been proposed in literatures. In these methods, the x- and z-axis are commonly generated by different methods, and some x-axis methods are implemented on the basis of a z-axis being given. In addition, the weight and disambiguation methods are commonly used in these LRF methods. In existing evaluations of LRF, each LRF method is evaluated with a complete form. However, the merits and demerits of the z-axis, x-axis, weight and disambiguation methods in LRF construction are unclear. In this paper, we comprehensively analyze the z-axis, x-axis, weight and disambiguation methods in existing LRFs, and obtain six z-axis and eight x-axis, five weight and two disambiguation methods. Some axis methods are firstly proposed in this paper. The performance of these methods are comprehensively evaluated on six standard datasets with different application scenarios and nuisances. Considering the evaluation outcomes, the merits and demerits of different weight, disambiguation, z- and x-axis methods are analyzed and summarized. The experimental result also shows that some new designed LRF axes present superior performance compared with the state-of-the-art ones.
StableMoE: Stable Routing Strategy for Mixture of Experts

The Mixture-of-Experts (MoE) technique can scale up the model size of Transformers with an affordable computational overhead. We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i.e., the target expert of the same input may change along with training, but only one expert will be activated for the input during inference. The routing fluctuation tends to harm sample efficiency because the same input updates different experts but only one is finally used. In this paper, we propose StableMoE with two training stages to address the routing fluctuation problem. In the first training stage, we learn a balanced and cohesive routing strategy and distill it into a lightweight router decoupled from the backbone model. In the second training stage, we utilize the distilled router to determine the token-to-expert assignment and freeze it for a stable routing strategy. We validate our method on language modeling and multilingual machine translation. The results show that StableMoE outperforms existing MoE methods in terms of both convergence speed and performance.
CenterNet++ for Object Detection

There are two mainstream approaches for object detection: top-down and bottom-up. The state-of-the-art approaches are mainly top-down methods. In this paper, we demonstrate that bottom-up approaches show competitive performance compared with top-down approaches and have higher recall rates. Our approach, named CenterNet, detects each object as a triplet of keypoints (top-left and bottom-right corners and the center keypoint). We first group the corners according to some designed cues and confirm the object locations based on the center keypoints. The corner keypoints allow the approach to detect objects of various scales and shapes and the center keypoint reduces the confusion introduced by a large number of false-positive proposals. Our approach is an anchor-free detector because it does not need to define explicit anchor boxes. We adapt our approach to backbones with different structures, including 'hourglass'- like networks and 'pyramid'- like networks, which detect objects in single-resolution and multi-resolution feature maps, respectively. On the MS-COCO dataset, CenterNet with Res2Net-101 and Swin-Transformer achieve average precisions (APs) of 53.7% and 57.1%, respectively, outperforming all existing bottom-up detectors and achieving state-of-the-art performance. We also design a real-time CenterNet model, which achieves a good trade-off between accuracy and speed, with an AP of 43.6% at 30.5 frames per second (FPS). The code is available at https://github.com/Duankaiwen/PyCenterNet.
A dynamical systems based framework for dimension reduction

We propose a novel framework for learning a low-dimensional representation of data based on nonlinear dynamical systems, which we call dynamical dimension reduction (DDR). In the DDR model, each point is evolved via a nonlinear flow towards a lower-dimensional subspace; the projection onto the subspace gives the low-dimensional embedding. Training the model involves identifying the nonlinear flow and the subspace. Following the equation discovery method, we represent the vector field that defines the flow using a linear combination of dictionary elements, where each element is a pre-specified linear/nonlinear candidate function. A regularization term for the average total kinetic energy is also introduced and motivated by optimal transport theory. We prove that the resulting optimization problem is well-posed and establish several properties of the DDR method. We also show how the DDR method can be trained using a gradient-based optimization method, where the gradients are computed using the adjoint method from optimal control theory. The DDR method is implemented and compared on synthetic and example datasets to other dimension reductions methods, including PCA, t-SNE, and Umap.