Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Network-based Fault Detection and Identification for Quadrotors using Dynamic Symmetry

Autonomous robotic systems, such as quadrotors, are susceptible to actuator faults, and for the safe operation of such systems, timely detection and isolation of these faults is essential. Neural networks can be used for verification of actuator performance via online actuator fault detection with high accuracy. In this paper, we develop a novel model-free fault detection and isolation (FDI) framework for quadrotor systems using long-short-term memory (LSTM) neural network architecture. The proposed framework only uses system output data and the commanded control input and requires no knowledge of the system model. Utilizing the symmetry in quadrotor dynamics, we train the FDI for fault in just one of the motors (e.g., motor #2), and the trained FDI can predict faults in any of the motors. This reduction in search space enables us to design an FDI for partial fault as well as complete fault scenarios. Numerical experiments illustrate that the proposed NN-FDI correctly verifies the actuator performance and identifies partial as well as complete faults with over 90% prediction accuracy. We also illustrate that model-free NN-FDI performs at par with model-based FDI, and is robust to model uncertainties as well as distribution shifts in input data.
A Novel Method of Fuzzy Topic Modeling based on Transformer Processing

Topic modeling is admittedly a convenient way to monitor markets trend. Conventionally, Latent Dirichlet Allocation, LDA, is considered a must-do model to gain this type of information. By given the merit of deducing keyword with token conditional probability in LDA, we can know the most possible or essential topic. However, the results are not intuitive because the given topics cannot wholly fit human knowledge. LDA offers the first possible relevant keywords, which also brings out another problem of whether the connection is reliable based on the statistic possibility. It is also hard to decide the topic number manually in advance. As the booming trend of using fuzzy membership to cluster and using transformers to embed words, this work presents the fuzzy topic modeling based on soft clustering and document embedding from state-of-the-art transformer-based model. In our practical application in a press release monitoring, the fuzzy topic modeling gives a more natural result than the traditional output from LDA.
Integrated phononic waveguides in diamond

Efficient generation, guiding, and detection of phonons, or mechanical vibrations, are of interest in various fields including radio frequency communication, sensing, and quantum information. Diamond is an important platform for phononics because of the presence of strain-sensitive spin qubits, and its high Young's modulus which allows for low-loss gigahertz devices. We demonstrate a diamond phononic waveguide platform for generating, guiding, and detecting gigahertz-frequency surface acoustic wave (SAW) phonons. We generate SAWs using interdigital transducers integrated on AlN/diamond and observe SAW transmission at 4-5 GHz through both ridge and suspended waveguides, with wavelength-scale cross sections (~1 {\mu}m2) to maximize spin-phonon interaction. This work is a crucial step for developing acoustic components for quantum phononic circuits with strain-sensitive color centers in diamond.
Conditional Mutual Information Constrained Deep Learning for Classification

The concepts of conditional mutual information (CMI) and normalized conditional mutual information (NCMI) are introduced to measure the concentration and separation performance of a classification deep neural network (DNN) in the output probability distribution space of the DNN, where CMI and the ratio between CMI and NCMI represent the intra-class concentration and inter-class separation of the DNN, respectively. By using NCMI to evaluate popular DNNs pretrained over ImageNet in the literature, it is shown that their validation accuracies over ImageNet validation data set are more or less inversely proportional to their NCMI values. Based on this observation, the standard deep learning (DL) framework is further modified to minimize the standard cross entropy function subject to an NCMI constraint, yielding CMI constrained deep learning (CMIC-DL). A novel alternating learning algorithm is proposed to solve such a constrained optimization problem. Extensive experiment results show that DNNs trained within CMIC-DL outperform the state-of-the-art models trained within the standard DL and other loss functions in the literature in terms of both accuracy and robustness against adversarial attacks. In addition, visualizing the evolution of learning process through the lens of CMI and NCMI is also advocated.
Smart Home Automation System

This paper proposes the design of Inter of Things (IoT) based home automation system using Raspberry pi. Currently in day today’s life we can hardly find a house without a home automation system. This project is intended to construct a home automation system that uses any mobile device to control the home appliances. This home automation system is based on IoT. Home automation is very exciting field when it uses new technologies like Internet of Things (IoT). Raspberry pi is credit card size computer. Raspberry pi supports large number of peripherals. Raspberry pi is having different communication media like Ethernet port, HDMI port, USB port, Display Serial Interface, Camera Serial Interface, Bluetooth, Bluetooth low energy. It allows controlling number of home appliances simultaneously. Here local server is created on Raspberry pi. User required to use different mobile devices like smart phones, Laptops, Tablets to operate the home appliances with the help of UI created on web page.
Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks

We investigate a new paradigm that uses differentiable SLAM architectures in a self-supervised manner to train end-to-end deep learning models in various LiDAR based applications. To the best of our knowledge there does not exist any work that leverages SLAM as a training signal for deep learning based models. We explore new ways to improve the efficiency, robustness, and adaptability of LiDAR systems with deep learning techniques. We focus on the potential benefits of differentiable SLAM architectures for improving performance of deep learning tasks such as classification, regression as well as SLAM. Our experimental results demonstrate a non-trivial increase in the performance of two deep learning applications - Ground Level Estimation and Dynamic to Static LiDAR Translation, when used with differentiable SLAM architectures. Overall, our findings provide important insights that enhance the performance of LiDAR based navigation systems. We demonstrate that this new paradigm of using SLAM Loss signal while training LiDAR based models can be easily adopted by the community.
Satisfiability.jl: Satisfiability Modulo Theories in Julia

Satisfiability modulo theories (SMT) is a core tool in formal verification. While the SMT-LIB specification language can be used to interact with theorem proving software, a high-level interface allows for faster and easier specifications of complex SMT formulae. In this paper we present a novel open-source package for interacting with SMT-LIB compliant solvers in the Julia programming language.
Patients hospitalized with alcohol‐related liver disease and prior bariatric surgery are more prone to develop acute‐on‐chronic liver failure

Patients with a history of bariatric surgery (BS) are susceptible to developing alcohol use disorder. We and others have previously shown that these patients can develop severe alcohol‐related liver disease (ARLD). Our aim was to describe the demographics, co‐morbidities and mortality of a hospitalized population diagnosed with alcohol‐related liver disease, in relation to BS.
Automatically Detecting Incompatible Android APIs

Fragmentation is a serious problem in the Android ecosystem, which is mainly caused by the fast evolution of the system itself and the various system customizations. Many efforts have attempted to mitigate its impact via approaches to automatically pinpointing compatibility issues in Android apps. We conducted a literature review to identify all the currently available approaches to addressing this issue. Within the nine identified approaches, the four issue detection tools and one incompatible API harvesting tool could be successfully executed. We tried to reproduce them based on their original datasets, and then empirically compared those approaches against common datasets. Our experimental results show that existing tool capabilities are quite distinct with only a small overlap in the compatibility issues being identified. Moreover, these detection tools commonly detect compatibility issues via two separate steps including incompatible APIs gathering and compatibility issues (induced by the incorrect invocations of the identified incompatible APIs) determination. To help developers better identify compatibility issues in Android apps, we developed a new approach, AndroMevol, to systematically spot incompatible APIs as they play a crucial role in issue detection. AndroMevol was able to pinpoint 397,678 incompatible APIs against the full history of the official Android framework and 52 customized Android frameworks spanning five popular device manufacturers. Our approach could enhance the ability of the state-of-the-art detection tools by identifying many more incompatible APIs that may cause compatibility issues in Android apps and foster more advanced approaches to pinpointing all types of compatibility issues.
Whole blood mitochondrial copy number in clinical populations with mood disorders: a meta-analysis

Background Major depressive disorder (MDD) and bipolar disorder (BD), are globally prevalent, contributing to significant disease burden and adverse health outcomes. These mood disorders are associated with changes in many aspects of brain reward pathways, yet cellular and molecular changes in the brain are not readily available in clinical populations. Therefore, the use of biomarkers as proxies for changes in the brain are necessary. The proliferation of mitochondria in blood has emerged as a potentially useful biomarker, yet a clear consensus on how these mood disorders impact mitochondrial DNA copy number (mtDNAcn) has not been reached. Methods Following PRISMA guidelines for a systematic search, 22 papers met inclusion criteria for meta-analysis (10 MDD, 10 BD, 2 both MDD and BD). We extracted demographic, disorder, and methodological information with mtDNAcn. Using the metafor package for R, calculated effect sizes were used in random effects or meta regression models for MDD and BD. Results Our results show a trending increase in mtDNAcn in patients with MDD, which reaches significance when one study with outlying demographic characteristics is excluded. Overall, there was no effect of BD on mtDNAcn, however, further subgroup and meta-regression analysis indicated the effects on mtDNAcn are dependent on BD type. Conclusions Together our data suggest whole blood/leukocyte mtDNAcn may be a useful biomarker for mood disorders, with MDD and BD Type II associated with higher mtDNAcn, and BD Type I associated with lower mtDNAcn. Further study of blood mtDNAcn could predict downstream health outcomes or treatment responsivity in individuals with mood disorders.
Cycling in the city: Understanding urban cycling behaviours and constraints in space-time

As cycling in British cities increases, so do conflicts between cyclists and other road users, as well as debates with city planners who are trying to balance cities’ transport infrastructures in the face of public spending cuts. In Tyne and Wear (North East England), incremental steps are taken by the local authorities to provide cycling infrastructure, albeit at a slow rate compared to the uptake of cycling in the commuting area. As a result, there appears to be increasing dissatisfaction from local campaign groups about the lack of a coherent cycling strategy, negative attitudes towards cyclists and even the refusal of the local public transport providers to allow bicycles on the metro system. This paper reports on a detailed survey of 72 commuter cyclists, while cycling in the area around Tyne and Wear. Data collection methods involved day-long GPS tracking alongside diaries for trip clarification and classification, over a period of 7 days. Secondary data involves the analysis of vehicle congestion data from traffic counters, superimposed on the main traffic arteries, cycling pathways and recently established 20 miles-per-hour zones. The aim of the paper is to present a comparative geographical analysis of primary tracks on everyday utility cycling, in comparison to “official” cycling network data of the study area. The purpose of this research is to provide evidence on the use of the area’s cycling infrastructure by experienced commuter cyclists, by estimating the cycle-miles on the cycling network as a percentage of the total, for the given sample. In order to fully comprehend the constraints imposed on cyclists, the tracks are analysed alongside vehicle congestion data. Space-time methods are used to understand what time of the day the trips are within or outside the cycling network in comparison to peak traffic times from traffic counters.
UNIDEAL: Curriculum Knowledge Distillation Federated Learning

Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, where clients possess data from different domains or distributions, remain a challenging problem due to the inherent heterogeneity. In this paper, we present UNIDEAL, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios and heterogeneous model architectures. The proposed method introduces Adjustable Teacher-Student Mutual Evaluation Curriculum Learning, which significantly enhances the effectiveness of knowledge distillation in FL settings. We conduct extensive experiments on various datasets, comparing UNIDEAL with state-of-the-art baselines. Our results demonstrate that UNIDEAL achieves superior performance in terms of both model accuracy and communication efficiency. Additionally, we provide a convergence analysis of the algorithm, showing a convergence rate of O(1/T) under non-convex conditions.
EGFE: End-to-end Grouping of Fragmented Elements in UI Designs with Multimodal Learning

When translating UI design prototypes to code in industry, automatically generating code from design prototypes can expedite the development of applications and GUI iterations. However, in design prototypes without strict design specifications, UI components may be composed of fragmented elements. Grouping these fragmented elements can greatly improve the readability and maintainability of the generated code. Current methods employ a two-stage strategy that introduces hand-crafted rules to group fragmented elements. Unfortunately, the performance of these methods is not satisfying due to visually overlapped and tiny UI elements. In this study, we propose EGFE, a novel method for automatically End-to-end Grouping Fragmented Elements via UI sequence prediction. To facilitate the UI understanding, we innovatively construct a Transformer encoder to model the relationship between the UI elements with multi-modal representation learning. The evaluation on a dataset of 4606 UI prototypes collected from professional UI designers shows that our method outperforms the state-of-the-art baselines in the precision (by 29.75\%), recall (by 31.07\%), and F1-score (by 30.39\%) at edit distance threshold of 4. In addition, we conduct an empirical study to assess the improvement of the generated front-end code. The results demonstrate the effectiveness of our method on a real software engineering application. Our end-to-end fragmented elements grouping method creates opportunities for improving UI-related software engineering tasks.
Design and Development of a Novel Soft and Inflatable Tactile Sensing Balloon for Early Diagnosis of Colorectal Cancer Polyps

In this paper, with the goal of addressing the high early-detection miss rate of colorectal cancer (CRC) polyps during a colonoscopy procedure, we propose the design and fabrication of a unique inflatable vision-based tactile sensing balloon (VTSB). The proposed soft VTSB can readily be integrated with the existing colonoscopes and provide a radiation-free, safe, and high-resolution textural mapping and morphology characterization of CRC polyps. The performance of the proposed VTSB has been thoroughly characterized and evaluated on four different types of additively manufactured CRC polyp phantoms with three different stiffness levels. Additionally, we integrated the VTSB with a colonoscope and successfully performed a simulated colonoscopic procedure inside a tube with a few CRC polyp phantoms attached to its internal surface.
TOPr: Enhanced Static Code Pruning for Fast and Precise Directed Fuzzing

Directed fuzzing is a dynamic testing technique that focuses exploration on specific, pre targeted program locations. Like other types of fuzzers, directed fuzzers are most effective when maximizing testing speed and precision. To this end, recent directed fuzzers have begun leveraging path pruning: preventing the wasteful testing of program paths deemed irrelevant to reaching a desired target location. Yet, despite code pruning's substantial speedup, current approaches are imprecise failing to capture indirect control flow requiring additional dynamic analyses that diminish directed fuzzers' speeds. Thus, without code pruning that is both fast and precise, directed fuzzers' effectiveness will continue to remain limited. This paper aims to tackle the challenge of upholding both speed and precision in pruning-based directed fuzzing. We show that existing pruning approaches fail to recover common case indirect control flow; and identify opportunities to enhance them with lightweight heuristics namely, function signature matching enabling them to maximize precision without the burden of dynamic analysis. We implement our enhanced pruning as a prototype, TOPr (Target Oriented Pruning), and evaluate it against the leading pruning based and pruning agnostic directed fuzzers SieveFuzz and AFLGo. We show that TOPr's enhanced pruning outperforms these fuzzers in (1) speed (achieving 222% and 73% higher test case throughput, respectively); (2) reachability (achieving 149% and 9% more target relevant coverage, respectively); and (3) bug discovery time (triggering bugs faster 85% and 8%, respectively). Furthermore, TOPr's balance of speed and precision enables it to find 24 new bugs in 5 open source applications, with 18 confirmed by developers, 12 bugs labelled as"Priority - 1. High", and 12 bugs fixed, underscoring the effectiveness of our framework.
Association of affected and non-affected side ability with postoperative outcomes in patients undergoing total hip arthroplasty

Background: Although several reports have examined the association between preoperative function and postoperative outcomes in patients undergoing total hip arthroplasty (THA), it is unclear whether the ability of the affected or non-affected side particularly impacts on outcomes. We aimed to investigate the association between affected and non-affected side ability and walking independence. Methods: We prospectively enrolled 721 consecutive patients who underwent THA. Preoperatively, quadriceps isometric strength (QIS) and one-leg standing time (OLST) were measured. The endpoints were walking independence within 3, 5, 7, 10, and 14 days postoperatively. The associations between preoperative abilities and outcomes were examined using multivariate Cox hazard model, and the area under the curves (AUCs) for outcomes were compared. Results: We analysed 540 patients after excluding patients who met the exclusion criteria. Both affected and non-affected QIS predicted walking independence within 3 (p = 0.006 and 0.001, respectively), 5, 7, 10, and 14 (both p < 0.001) days postoperatively. For OLST, only the affected side did not predict walking independence within 3 days postoperatively (p = 0.154 and 0.012, respectively), and both sides did at days 5 (p = 0.019 and <0.001, respectively), 7, 10, and 14 (both p < 0.001). The AUCs of the non-affected side ability for walking independence were significantly greater than those of the affected side on postoperative days 3 (0.66 vs. 0.73; p = 0.021) and 5 (0.67 vs. 0.71; p = 0.040), with no significant difference after day 7. Conclusions: Both sides abilities were associated with walking independence after THA, but non-affected side was found to be particularly crucial for early walking independence.
Multi-Dictionary Tensor Decomposition

Tensor decomposition methods are popular tools for analysis of multi-way datasets from the social media, healthcare, spatio-temporal domains, and others. Widely adopted models such as Tucker and canonical polyadic decomposition (CPD) follow a data-driven philosophy: they decompose a tensor into factors that approximate the observed data well. In some cases side information is available about the tensor modes. For example, in a temporal user-item purchases tensor a user influence graph, an item similarity graph, and knowledge about seasonality or trends in the temporal mode may be available. Such side information may enable more succinct and interpretable tensor decomposition models and improved quality in downstream tasks. We propose a framework for Multi-Dictionary Tensor Decomposition (MDTD) which takes advantage of prior structural information about tensor modes in the form of coding dictionaries to obtain sparsely coded tensor factors. We derive a general optimization algorithm for MDTD that handles both complete inputs and inputs with missing values. MDTD handles large sparse tensors typical in many real-world application domains. We experimentally demonstrate its utility in both synthetic and real-world datasets. It learns more concise models than dictionary-free counterparts and improves (i) reconstruction quality (up to 60% smaller models coupled with reduced representation error); (ii) missing values imputation quality (two-fold MSE reduction with up to orders of magnitude time savings) and (iii) the estimation of the tensor rank. MDTD’s quality improvements do not come with a running time premium: it can decompose 19GB datasets in less than a minute. It can also impute missing values in sparse billion-entry tensors more accurately and scalably than state-of-the-art competitors.
On The Stabilizer Formalism And Its Generalization

The standard stabilizer formalism provides a setting to show that quantum computation restricted to operations within the Clifford group are classically efficiently simulable: this is the content of the well-known Gottesman-Knill theorem. This work analyzes the mathematical structure behind this theorem to find possible generalizations and derivation of constraints required for constructing a non-trivial generalized Clifford group. We prove that if the closure of the stabilizing set is dense in the set of $SU(d)$ transformations, then the associated Clifford group is trivial, consisting only of local gates and permutations of subsystems. This result demonstrates the close relationship between the density of the stabilizing set and the simplicity of the corresponding Clifford group. We apply the analysis to investigate stabilization with binary observables for qubits and find that the formalism is equivalent to the standard stabilization for a low number of qubits. Based on the observed patterns, we conjecture that a large class of generalized stabilizer states are equivalent to the standard ones. Our results can be used to construct novel Gottesman-Knill-type results and consequently draw a sharper line between quantum and classical computation.