Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Cost Lung Cancer Detection Using Machine Learning on Breath Samples

In recent years, electronic nose devices have become a popular approach for identifying respiratory disorders including lung cancer. Traditional e-nose systems have had very consistent principles and patterns of sensor responses. After coming to the realization that detecting cancer at early stages can save 99 percent of lives, it has become imperative to design a machine that can easily detect for lung cancer(the most common type of cancer) in a cost-effective and accurate way. Designing an Al Nose was a perfect way to counteract the problem. A tiny e-nose system with 14 gas sensors of four types was created and fifty breath samples were analyzed. Five feature extraction techniques and two classifiers were used to test the system's efficiency in recognizing and discriminating lung cancer from other respiratory disorders and healthy controls. Finally, the impact of different sensor types on the capacity of e-nose systems to identify objects was investigated. The sensitivity, specificity, and accuracy of distinguishing lung cancer patients from healthy controls are 91.58 percent, 91.72 percent, and 91.59 percent, respectively, when utilizing the DA fuzzy 5-NIN classification approach. The findings imply that type-specific sensors might improve the diagnostic accuracy of e-nose devices greatly. These findings indicated that the e-nose system described in this work might be used in lung cancer screening with good results. Furthermore, while creating e-nose systems, it is critical to consider type-different sensors. This machine covers all aspects to most effectively develop a machine that can detect lung cancer that is user-friendly and low cost.
Fast Knowledge Graph Completion using Graphics Processing Units

Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate $N\times N \times R$ vector operations, where $N$ is the number of entities and $R$ is the number of relation types. It is very costly. In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define"transformable to a metric space"and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is"transformable to a metric space". After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem.
Self-Supervised Learning for Audio-Based Emotion Recognition

Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich, a major barrier to achieve consistently high-performance models is the paucity of available training labels. Self-supervised learning (SSL) is a family of methods which can learn despite a scarcity of supervised labels by predicting properties of the data itself. To understand the utility of self-supervised learning for audio-based emotion recognition, we have applied self-supervised learning pre-training to the classification of emotions from the CMU- MOSEI's acoustic modality. Unlike prior papers that have experimented with raw acoustic data, our technique has been applied to encoded acoustic data. Our model is first pretrained to uncover the randomly-masked timestamps of the acoustic data. The pre-trained model is then fine-tuned using a small sample of annotated data. The performance of the final model is then evaluated via several evaluation metrics against a baseline deep learning model with an identical backbone architecture. We find that self-supervised learning consistently improves the performance of the model across all metrics. This work shows the utility of self-supervised learning for affective computing, demonstrating that self-supervised learning is most useful when the number of training examples is small, and that the effect is most pronounced for emotions which are easier to classify such as happy, sad and anger. This work further demonstrates that self-supervised learning works when applied to embedded feature representations rather than the traditional approach of pre-training on the raw input space.
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis

Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of inductive bias on HTML. We introduce WebAgent, an LLM-driven agent that learns from self-experience to complete tasks on real websites following natural language instructions. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites via Python programs generated from those. We design WebAgent with Flan-U-PaLM, for grounded code generation, and HTML-T5, new pre-trained LLMs for long HTML documents using local and global attention mechanisms and a mixture of long-span denoising objectives, for planning and summarization. We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks; achieving 18.7% higher success rate than the prior method on MiniWoB web automation benchmark, and SoTA performance on Mind2Web, an offline task planning evaluation.
Information Geometry of Wasserstein Statistics on Shapes and Affine Deformations

Information geometry and Wasserstein geometry are two main structures introduced in a manifold of probability distributions, and they capture its different characteristics. We study characteristics of Wasserstein geometry in the framework of Li and Zhao (2023) for the affine deformation statistical model, which is a multi-dimensional generalization of the location-scale model. We compare merits and demerits of estimators based on information geometry and Wasserstein geometry. The shape of a probability distribution and its affine deformation are separated in the Wasserstein geometry, showing its robustness against the waveform perturbation in exchange for the loss in Fisher efficiency. We show that the Wasserstein estimator is the moment estimator in the case of the elliptically symmetric affine deformation model. It coincides with the information-geometrical estimator (maximum-likelihood estimator) when and only when the waveform is Gaussian. The role of the Wasserstein efficiency is elucidated in terms of robustness against waveform change.
Towards Generic and Controllable Attacks Against Object Detection

Existing adversarial attacks against Object Detectors (ODs) suffer from two inherent limitations. Firstly, ODs have complicated meta-structure designs, hence most advanced attacks for ODs concentrate on attacking specific detector-intrinsic structures, which makes it hard for them to work on other detectors and motivates us to design a generic attack against ODs. Secondly, most works against ODs make Adversarial Examples (AEs) by generalizing image-level attacks from classification to detection, which brings redundant computations and perturbations in semantically meaningless areas (e.g., backgrounds) and leads to an emergency for seeking controllable attacks for ODs. To this end, we propose a generic white-box attack, LGP (local perturbations with adaptively global attacks), to blind mainstream object detectors with controllable perturbations. For a detector-agnostic attack, LGP tracks high-quality proposals and optimizes three heterogeneous losses simultaneously. In this way, we can fool the crucial components of ODs with a part of their outputs without the limitations of specific structures. Regarding controllability, we establish an object-wise constraint that exploits foreground-background separation adaptively to induce the attachment of perturbations to foregrounds. Experimentally, the proposed LGP successfully attacked sixteen state-of-the-art object detectors on MS-COCO and DOTA datasets, with promising imperceptibility and transferability obtained. Codes are publicly released in https://github.com/liguopeng0923/LGP.git
Delving into the $ B_s \to \ell \ell^{\prime}$, $B_{(s)} \to (K^{(*)}, \phi, f_2^{\prime}, K_2^*) \ell \ell ^{\prime}$ processes

To shed light on the indirect search for new physics beyond the standard model, the long standing discrepancies between the theory and experiment mediated by FCNC $b\to s \ell \ell$ quark level transitions set an ideal testing ground. Though the very recent measurements of $R_K$ and $R_{K^*}$ are consistent with the standard model, still the excitements remain on the measurements of LHCb experiment with the observables $\mathcal{B} (B_s \to \phi \mu ^+ \mu ^-)$ which has deviations at the level of $3.6 \sigma$. Additionally, standard deviation of $\sim 3.3 \sigma$ and $1.2 \sigma$, respectively for $P_5^{\prime}$ in $B \to K^* \mu ^+ \mu ^-$ and the branching ratio in $B_s \to \mu^+ \mu^-$ processes are observed. Inspired by these discrepancies, we work out the constraints on the new physics coupling parameters in the presence of a non-universal $Z'$ model. We then probe the exclusive leptonic decay channels $ B_s \to \ell \ell^{\prime}$, $B_{(s)} \to (K^{(*)}, \phi, f_2^{\prime}, K_2^*) \ell \ell ^{\prime}$ induced by the neutral current transition $b\to s \ell \ell^{\prime}$. We find that the $q^2$ variation of the observables, such as, branching ratio, forward-backward asymmetry, lepton polarization asymmetry, and the very sensible observable, so called non-universality observables for LFV decays display the sensitivity of new physics. In this analysis. we estimate above mentioned observables that could shed light on the window of new physics in the near future.
Variational inference machines for semiparametric regression

Variational approximation methods are enjoying an increasing amount of development and use in statistical problems. In the Bayesian field, we develop mean field variational Bayes (MFVB) algorithms that perform variable selection and fit complicated regression models. We also produce a new Bayesian inference software, InferMachine(), which can perform the MFVB inference using BRugs model code. Finally, a new computational framework, Infer.NET, for approximate Bayesian inference in hierarchical Bayesian models is demonstrated. We assess the accuracy of MFVB via comparison with a Markov chain Monte Carlo (MCMC) baseline. The simulation results show that the results of the MFVB inference agree with those of the MCMC approach. In the non-Bayesian field, the precise asymptotic distributional behaviour of Gaussian variational approximate estimators in a single predictor Poisson mixed model is derived. A simulation study shows that the Gaussian variational approximate confidence intervals possess good to excellent coverage properties.
Structural analysis of compression helical spring used in suspension system

The main aim of this work has to develop a helical spring for shock absorber used in suspension system which is designed to reduce shock impulse and liberate kinetic energy. In a vehicle, it increases comfort by decreasing amplitude of disturbances and it improves ride quality by absorbing and dissipating energy. When a vehicle is in motion on a road and strikes a bump, spring comes into action quickly. After compression, spring will attempt to come to its equilibrium state which is on level road. Helical springs can be made lighter with more strength by reducing number of coils and increasing the area. In this research work, a helical spring is modeled and analyzed to substitute the existing steel spring which is used in suspension. By using different materials, stress and deflection of helical spring can be varied. Comparability between existing spring and newly replaced spring is used to verify the results. For finding detailed stress distribution, finite element analysis is used to find stresses and d.
A detail-preserved and luminance-consistent multi-exposure image fusion algorithm

When irradiance across a scene varies greatly, we can hardly get an image of the scene without over- or underexposure area, because of the constraints of cameras. Multi-exposure image fusion (MEF) is an effective method to deal with this problem by fusing multi-exposure images of a static scene. A novel MEF method is described in this paper. In the proposed algorithm, coarser-scale luminance consistency is preserved by contribution adjustment using the luminance information between blocks; detail-preserved smoothing filter can stitch blocks smoothly without losing details. Experiment results show that the proposed method performs well in preserving luminance consistency and details.
Eden clusters in three-dimensions and the KPZ universality class

We present large-scale simulations of radial Eden clusters in three-dimensions and show that the growth exponent is in agreement with the value $\beta=0.242$ accepted for the Kardar-Parisi-Zhang (KPZ) universality class. Our results refute a recent assertion proposing that radial Eden growth belongs to a universality class distinct from KPZ. We associate the previously reported discrepancy to a slow convergence to the asymptotic limit. We also present the skewness and kurtosis in the roughening regime for flat geometry in 2+1 dimensions.
Polytope samplers for network tomography

Network tomography is concerned with reconstruction and estimation of properties of traffic flow that are linked to the observed data through an underdetermined linear system. The likelihood function for such problems can be expressed only as the sum over integer‐valued points in a convex polytope. Typically this set is too large to enumerate, so that statistical inference must proceed by sampling from the polytope. Recent progress has seen the development of polytope sampling algorithms that operate well when the network link‐path incidence matrix is totally unimodular. In this paper we examine whether this property is likely to hold in practical applications. We find that total unimodularity is assured for certain simple networks, but that it can fail in more complex cases. We show that when it does fail, the existing polytope samplers may not generate the requisite irreducible Markov chain. As a remedy, a modified algorithm is proposed in which the basis for the polytope adapts to ensure adequate mixing over the entire polytope. The operation of this algorithm is illustrated by a numerical example.
Curve contour detection based on Helmholtz principle and application to road white line detection

We propose curve contour detection algorithm for road white line detection based on Helmholtz principle. White line detection is widely used in driver support systems used mainly in highway or major arterial road. As the common road will be the target of operational area for autonomous vehicle, it is thought to be necessary to develop a new detection algorithm that can deal with various types of road. This paper proposes model-less algorithm that is constructed on a new edge feature inspired by Helmholtz principle through the analysis of the limit of Hough transform. This feature is basically same as Hough defined feature of edge count on the line except two remarkable points. The one is the restriction of count area and the other is the way of count which affords to detect curve line as well as straight line. Implementation by convolutional neural network is explained and the relation between tunable parameters and the detection performance as well as the processing time are discussed. Comparison between conventional methods such as Hough transform or machine learned contour detection algorithm BEL is explained for test image and images taken by on-board camera to show the superiority of proposed algorithm. We demonstrate that proposed algorithm that can apply to diverse road environments but is hardly affected by noise can be realized.
A Secret Sharing Scheme based on Residue Class Ring

A (t, n)- threshold secret sharing scheme is a method for distributi ng a secret amongst a group of participants (1). Each of participants is allocated a share of the secret. The secret c an only be reconstructed when the shares are combined together (6). In this paper, we propose a secret sharing scheme based on residue class ring Fp(x)/( f), where p is a prime and deg( f) = n. Then we call it a (p n − 1, p n )− threshold secret sharing scheme.
Measurement of Single and Double Spin Asymmetries in pa (e, e'pi +/-,0)X Semi-Inclusive Deep-Inelastic Scattering

PAGE Measurements in the late 1980s at CERN revealed that quark spins account for a small fraction of the proton's spin. This so-called spin crisis spurred a number of new experiments to identify the proton's silent spin contributors, namely, the spin of the gluons, which hold the quarks together, and the orbital angular momentum of both quarks and gluons. One such experiment was egl-dvcs at the Thomas Jefferson National Accelerator Facility in Newport News, Va. , which ran in 2009 and collected approximately 19 billion electron triggers for hydrogen. I will present new measurements of the single and double-spin asymmetries ALu, AuL and ALL for n+ , nand n° , measured as a function of Bjorken XB, squared momentum transfer Q2 , hadron energy fraction z, and hadron transverse momentum Phl_. These asymmetries, which are convolutions of transversemomentum-dependent parton distributions and fragmentation functions, correlate with the transverse momentum, and therefore with the orbital motion, of the struck quark. Acknowledgments List of Tables . List of Figures CHAPTER
Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments

ABSTRACT This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Adopting a Neymanian repeated sampling approach that integrates such causal inference with finite population survey sampling, an inferential framework is developed for general mechanisms of assigning experimental units to multiple treatments. This framework extends classical methods by allowing the possibility of randomization restrictions and unequal replications. Novel conditions that are “milder” than strict additivity of treatment effects, yet permit unbiased estimation of the finite population sampling variance of any treatment contrast estimator, are derived. The consequences of departures from such conditions are also studied under the criterion of minimax bias, and a new justification for using the Neymanian conservative sampling variance estimator in experiments is provided. The proposed approach can readily be extended to the case of treatments with a general factorial structure.
Exact observability and controllability for a nonsimple elastic rod

ABSTRACT In this paper, we prove exact observability, boundary and internal exact controllability results for a nonsimple elastic rod. By the spectral analysis method, it is shown that the considered problem has a sequence of eigenfunctions, which forms a Riesz basis for the state space. The exact observability is proved by application of a modified version of Ingham’s Theorem. Then, via the Hilbert Uniqueness method, we prove a boundary controllability result with only one controller and an internal controllability result.
Enhancing the performance of tungsten doped InZnO thin film transistors via sequential ambient annealing

This study suggests a sequential ambient annealing process as an excellent post-treatment method to enhance the device performance and stability of W (tungsten) doped InZnO thin film transistors (WIZO-TFTs). Sequential ambient annealing at 250 °C significantly enhanced the device performance and stability of WIZO-TFTs, compared with other post-treatment methods, such as air ambient annealing and vacuum ambient annealing at 250 °C. To understand the enhanced device performance and stability of WIZO-TFT with sequential ambient annealing, we investigate the correlations between device performance and stability and electronic structures, such as band alignment, a feature of the conduction band, and band edge states below the conduction band. The enhanced performance of WIZO-TFTs with sequential ambient annealing is related to the modification of the electronic structure. In addition, the dominant mechanism responsible for the enhanced device performance and stability of WIZO-TFTs is considered to be a change .
A frequency‐localized recursive partial least squares ensemble for soft sensing

We report the use of a frequency‐localized adaptive soft sensor ensemble using the wavelet coefficients of the responses from the physical sensors. The proposed method is based on building recursive, partial least squares soft sensor models on each of the wavelet coefficient matrices representing different frequency content of the signals from the physical sensors, combining the predictions from these models via static weights determined from an inverse‐variance weighting approach, and recursively adapting each of the soft sensor models in the ensemble when new data are received. Wavelet‐induced boundary effects are handled by using the undecimated wavelet transform with the Haar wavelet, an approach that is not subject to wavelet boundary effects that would otherwise arise on the most recent sensor data. An additional advantage of the undecimated wavelet transform is that the wavelet function is defined for a signal of arbitrary length, thus avoiding the need to either trim or pad the training signals to dyadic length, which is required with the basic discrete wavelet transform. The new method is tested against a standard recursive partial least squares soft sensor on 3 soft‐sensing applications from 2 real industrial processes. For the datasets we examined, we show that results from the new method appear to be statistically superior to those from a soft sensor based only on a recursive partial least squares model with additional advantages arising from the ability to examine performance of each localized soft sensor in the ensemble.
Making biology count: integrating mathematics into the teaching of inheritance

Abstract In this paper, I explore how seamless embedding of four mathematical concepts: counting, ratio, distribution, and statistical significance, into a unit on inheritance enables students to gain a deeper insight into biological phenomena than a qualitative only approach achieves. The topic of Mendelian inheritance is used to illustrate this proposal. An introductory activity is presented that separates the specialized genetics vocabulary from the inheritance process, allowing the initial mathematical relationship to be developed and subsequently followed by the language requirements.