Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Deep Learning and Traditional Machine Learning Techniques for Classification of Pap Smear Images.

A comprehensive study on machine and deep learning techniques for classification of normal and abnormal cervical cells by using pap smear images from Herlev dataset results are presented. This dataset includes 917 images and 7 different classes. All techniques used in this study are modeled by using Google Colab platform with scikit-learn and Keras library inside TensorFlow. In the first study, traditional machine learning methods such as logistic regression, k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Tree, Random Forest and eXtreme Gradient Boosting (XGBoost) are used and compared with each other to find binary classification as normal and abnormal cervical cells. Better results are observed by XGBoost and kNN classifiers among the others with an accuracy of 85%. In the second study, a deep learning model based on Convolutional Neural Network(CNN) is used for the same dataset. Accordingly, accuracies of 99% and 93% are obtained for the training and the test dataset, respectively. In this model, it takes 50 epochs to have these accuracies within 20 minutes of computational time.
Risk Bounds for Robust Deep Learning

It has been observed that certain loss functions can render deep-learning pipelines robust against flaws in the data. In this paper, we support these empirical findings with statistical theory. We especially show that empirical-risk minimization with unbounded, Lipschitz-continuous loss functions, such as the least-absolute deviation loss, Huber loss, Cauchy loss, and Tukey's biweight loss, can provide efficient prediction under minimal assumptions on the data. More generally speaking, our paper provides theoretical evidence for the benefits of robust loss functions in deep learning.
Power Efficiency, Overhead, and Complexity Tradeoff in IRS-Assisted Communications - Quadratic Phase-Shift Design

In this paper, we focus on large intelligent reflecting surfaces (IRSs) and propose a new codebook construction method to obtain a set of predesigned phase-shift configurations for the IRS unit cells. Since the overhead for channel estimation and the complexity of online optimization for IRS-assisted communications scale with the size of the phase-shift codebook, the design of small codebooks is of high importance. We show that there exists a fundamental tradeoff between power efficiency and the size of the codebook. We first analyze this tradeoff for baseline designs that employ a linear phase-shift across the IRS. Subsequently, we show that an efficient design for small codebooks mandates higher-order phase-shift variations across the IRS. Consequently, we propose a quadratic phase-shift design, derive its coefficients as a function of the codebook size, and analyze its performance. Our simulation results show that the proposed design yields a higher power efficiency for small codebooks than the linear baseline designs.
Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach

Choice overload is a common problem in many online settings, including healthcare. Online healthcare platforms tend to provide a large variety of behavior intervention information or programs to help individuals modify their lifestyles to improve wellness. However, having too many options can significantly increase searching cost, prevent users from discovering the truly relevant interventions, and harm users’ long-term healthcare decision-making efficiency. This motivates us to propose a personalized healthcare recommendation system to provide tailored support for individuals’ intervention participation. The proposed framework, a deep-learning and diversity-enhanced multiarmed bandit (DLDE-MAB), integrates several predictive and prescriptive analytics components to combat the unique challenges presented in the healthcare recommendation setting. It leverages online machine learning to provide adaptive and real-time support, a theory-guided diversity promotion scheme to cover multiple healthcare needs, and deep learning to further enhance dynamic context representation. Through extensive experiments, we show that the proposed framework outperforms various competing models in terms of its adaptivity to data dynamics, diversity, and uncertainty. The proposed model and evaluation results provide important implications for business intelligence and personalized, contextualized, and agile healthcare decision making.
Sufficient and insufficient conditions for the stochastic convergence of Ces\`{a}ro means

We study the stochastic convergence of the Cesaro mean of a sequence of random variables. These arise naturally in statistical problems that have a sequential component, where the sequence of random variables is typically derived from a sequence of estimators computed on data. We show that establishing a rate of convergence in probability for a sequence is not sufficient in general to establish a rate in probability for its Cesaro mean. We also present several sets of conditions on the sequence of random variables that are sufficient to guarantee a rate of convergence for its Cesaro mean. We identify common settings in which these sets of conditions hold.
Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation

Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed for performance enhancement. However, the large size of 3D volume images poses a great computational challenge to traditional attention methods. In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images. The main idea is that we apply tensor folding and unfolding operations to construct four small sub-affinity matrices to approximate the original affinity matrix. Through four consecutive sub-attention modules of FA, each element in the feature tensor can aggregate spatial-channel information from all other elements. Compared to traditional attention methods, with the moderate improvement of accuracy, FA can substantially reduce the computational complexity and GPU memory consumption. We demonstrate the superiority of our method on two challenging tasks for 3D MIR and MIS, which are quantitative susceptibility mapping and multiple sclerosis lesion segmentation.
DistilE: Distiling Knowledge Graph Embeddings for Faster and Cheaper Reasoning

Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and usually a higher dimensional one ensures better reasoning capability. However, high-dimensional KGEs pose huge challenges to storage and computing resources and are not suitable for resource-limited or time-constrained applications, for which faster and cheaper reasoning is necessary. To address this problem, we propose DistilE, a knowledge distillation method to build low-dimensional student KGE from pre-trained high-dimensional teacher KGE. We take the original KGE loss as hard label loss and design specific soft label loss for different KGEs in DistilE. We also propose a two-stage distillation approach to make the student and teacher adapt to each other and further improve the reasoning capability of the student. Our DistilE is general enough to be applied to various KGEs. Experimental results of link prediction show that our method successfully distills a good student which performs better than a same dimensional one directly trained, and sometimes even better than the teacher, and it can achieve 2 times - 8 times embedding compression rate and more than 10 times faster inference speed than the teacher with a small performance loss. We also experimentally prove the effectiveness of our two-stage training proposal via ablation study.
Machine Learning for Temporal Data in Finance: Challenges and Opportunities

Temporal data are ubiquitous in the financial services (FS) industry -- traditional data like economic indicators, operational data such as bank account transactions, and modern data sources like website clickstreams -- all of these occur as a time-indexed sequence. But machine learning efforts in FS often fail to account for the temporal richness of these data, even in cases where domain knowledge suggests that the precise temporal patterns between events should contain valuable information. At best, such data are often treated as uniform time series, where there is a sequence but no sense of exact timing. At worst, rough aggregate features are computed over a pre-selected window so that static sample-based approaches can be applied (e.g. number of open lines of credit in the previous year or maximum credit utilization over the previous month). Such approaches are at odds with the deep learning paradigm which advocates for building models that act directly on raw or lightly processed data and for leveraging modern optimization techniques to discover optimal feature transformations en route to solving the modeling task at hand. Furthermore, a full picture of the entity being modeled (customer, company, etc.) might only be attainable by examining multiple data streams that unfold across potentially vastly different time scales. In this paper, we examine the different types of temporal data found in common FS use cases, review the current machine learning approaches in this area, and finally assess challenges and opportunities for researchers working at the intersection of machine learning for temporal data and applications in FS.
Stochastic Volterra integral equations with jumps and non-Lipschitz coefficients

Stochastic Volterra integral equations with jumps (SVIEs) have become very common and widely used in numerous branches of science, due to their connections with mathematical finance, biology, engineering and so on. In this paper, we apply the successive approximation method to investigate the existence and uniqueness of solutions to the SVIEs driven by Brownian motion and compensated Poisson random measure under non-Lipschitz condition.
Estimating individual treatment effects using non‐parametric regression models: A review

Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction. In this paper, we examine the problem of estimating heterogeneous treatment effects using non‐parametric regression‐based methods, starting from an empirical study aimed at investigating the effect of participation in school meal programs on health indicators. First, we introduce the setup and the issues related to conducting causal inference with observational or non‐fully randomized data, and how these issues can be tackled with the help of statistical learning tools. Then, we review and develop a unifying taxonomy of the existing state‐of‐the‐art frameworks that allow for individual treatment effects estimation via non‐parametric regression models. After presenting a brief overview on the problem of model selection, we illustrate the performance of some of the methods on three different simulated studies. We conclude by demonstrating the use of some of the methods on an empirical analysis of the school meal program data.
Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)

We present a method for extracting information about facial expressions from digital images. The method codes facial expression images using a multi-orientation, multi-resolution set of Gabor filters that are topographically ordered and approximately aligned with the face. A similarity space derived from this code is compared with one derived from semantic ratings of the images by human observers. Interestingly the low-dimensional structure of the image-derived similarity space shares organizational features with the circumplex model of affect, suggesting a bridge between categorical and dimensional representations of facial expression. Our results also indicate that it would be possible to construct a facial expression classifier based on a topographically-linked multi-orientation, multi-resolution Gabor coding of the facial images at the input stage. The significant degree of psychological plausibility exhibited by the proposed code may also be useful in the design of human-computer interfaces.
OneStopTuner: An End to End Architecture for JVM Tuning of Spark Applications

Java is the backbone of widely used big data frameworks, such as Apache Spark, due to its productivity, portability from JVM-based execution, and support for a rich set of libraries. However, the performance of these applications can widely vary depending on the runtime flags chosen out of all existing JVM flags. Manually tuning these flags is both cumbersome and error-prone. Automated tuning approaches can ease the task, but current solutions either require considerable processing time or target a subset of flags to avoid time and space requirements. In this paper, we present OneStopTuner, a Machine Learning based novel framework for autotuning JVM flags. OneStopTuner controls the amount of data generation by leveraging batch mode active learning to characterize the user application. Based on the user-selected optimization metric, OneStopTuner then discards the irrelevant JVM flags by applying feature selection algorithms on the generated data. Finally, it employs sample efficient methods such as Bayesian optimization and regression guided Bayesian optimization on the shortlisted JVM flags to find the optimal values for the chosen set of flags. We evaluated OneStopTuner on widely used Spark benchmarks and compare its performance with the traditional simulated annealing based autotuning approach. We demonstrate that for optimizing execution time, the flags chosen by OneStopTuner provides a speedup of up to 1.35x over default Spark execution, as compared to 1.15x speedup by using the flag configurations proposed by simulated annealing. OneStopTuner was able to reduce the number of executions for data-generation by 70% and was able to suggest the optimal flag configuration 2.4x faster than the standard simulated annealing based approach, excluding the time for data-generation.
Inverse mapping of face GANs

Generative adversarial networks (GANs) synthesize realistic images from a random latent vector. While many studies have explored various training configurations and architectures for GANs, the problem of inverting a generative model to extract latent vectors of given input images has been inadequately investigated. Although there is exactly one generated image per given random vector, the mapping from an image to its recovered latent vector can have more than one solution. We train a ResNet architecture to recover a latent vector for a given face that can be used to generate a face nearly identical to the target. We use a perceptual loss to embed face details in the recovered latent vector while maintaining visual quality using a pixel loss. The vast majority of studies on latent vector recovery perform well only on generated images, we argue that our method can be used to determine a mapping between real human faces and latent-space vectors that contain most of the important face style details. In addition, our proposed method projects generated faces to their latent-space with high fidelity and speed. At last, we demonstrate the performance of our approach on both real and generated faces.
The ULX source X-3 in NGC 4258: a search for its X-ray and optical properties

We present the results of a search for the nature of ultraluminous X-ray source (ULX) X-3 in the nearby galaxy NGC 4258. We use archival data from {\it XMM-Newton}, {\it Chandra}, {\it NuSTAR} and {\it HST} observations. Total X-ray data analysed to find the model parameters of the system is indicative of a stellar mass black hole, $\sim$ 10 M$_{\sun}$, as the central compact object. Furthermore, analyses of the optical data from {\it HST} reveal two optical candidates with the 90 per cent confidence level of error radius of 0$\farcs$28. Assuming the optical emission is dominated by the donor star, both of these candidates are found to have spectral types that lie between B3$-$F1 with absolute magnitudes of $M_{\rm V}$ $\approx$ -6.4. Moreover, the age and mass estimates for the candidates are found to be of 10 and 18 Myr and of 13 and 20 M$_{\sun}$, respectively.
The Villafranca catalog of Galactic OB groups

CONTEXT. The GOSSS spectral classifications and Gaia data have significantly improved our ability to measure distances and determine memberships of stellar groups with OB stars. AIMS. We have started a program to identify, measure distances, and determine the membership of Galactic stellar groups with OB stars. We start with the identification and distance determinations of groups with O stars. In this paper we concentrate on groups that contain stars with the earliest spectral subtypes. METHODS. We use GOSSS to select Galactic stellar groups with O2-O3.5 stars and a method that combines Gaia DR2 photometry, positions, proper motions, and parallaxes to assign robust memberships and measure distances. We also include the two clusters in that paper to generate our first list of 16 O-type Galactic stellar groups. RESULTS. We derive distances, determine the membership, and analyze the structure of sixteen Galactic stellar groups with O stars, Villafranca O-001 to O-016, including the 14 groups with the earliest-O-type optically-accessible stars known in the Milky Way. We compare our distance with previous results and establish that the best consistency is with VLBI parallaxes and the worst is with kinematic distances. Our results indicate that massive stars can form in relatively low-mass clusters or even in near-isolation, as is the case for the Bajamar star in the North America nebula. This lends support to the hierarchical scenario of star formation, where some stars are born in well-defined bound clusters but others are born in associations that are unbound from the beginning: groups of newborn stars come in many shapes and sizes. We propose that HD 64 568 and HD 64 315 AB could have been ejected simultaneously from Villafranca O-012 S. Our results are consistent with a difference of approx. 20 microas in the Gaia DR2 parallax zero point between bright and faint stars. (ABRIDGED)
MERAM: Non-Volatile Cache Memory Based on Magneto-Electric FETs

Magneto-Electric FET (MEFET) is a recently developed post-CMOS FET, which offers intriguing characteristics for high speed and low-power design in both logic and memory applications. In this paper, for the first time, we propose a non-volatile 2T-1MEFET memory bit-cell with separate read and write paths. We show that with proper co-design at the device, cell and array levels, such a design is a promising candidate for fast non-volatile cache memory, termed as MERAM. To further evaluate its performance in memory system, we, for the first time, build a device-to-architecture cross-layer evaluation framework based on an experimentally-calibrated MEFET device model to quantitatively analyze and benchmark the proposed MERAM design with other memory technologies, including both volatile memory (i.e. SRAM, eDRAM) and other popular non-volatile emerging memory (i.e. ReRAM, STT-MRAM, and SOT-MRAM). The experiment results show that MERAM has a high state distinguishability with almost 36x magnitude difference in sense current. Results for the PARSEC benchmark suite indicate that as an L2 cache alternative, MERAM reduces Energy Area Latency (EAT) product on average by ~98\% and ~70\% compared with typical 6T SRAM and 2T SOT-MRAM platforms, respectively.
Analyzing Performance Properties Collected by the PerSyst Scalable HPC Monitoring Tool

The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify: execution patterns, possible code optimizations and improvements to the system monitoring. We also identify candidates for employing machine learning techniques to predict the performance of similar scientific codes.
A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios

The presence and coexistence of human operators and collaborative robots in shop-floor environments raises the need for assigning tasks to either operators or robots, or both. Depending on task characteristics, operator capabilities and the involved robot functionalities, it is of the utmost importance to design strategies allowing for the concurrent and/or sequential allocation of tasks related to object manipulation and assembly. In this paper, we extend the FLEXHRC framework presented in [1] to allow a human operator to interact with multiple, heterogeneous robots at the same time in order to jointly carry out a given task. The extended FLEXHRC framework leverages a concurrent and sequential task representation framework to allocate tasks to either operators or robots as part of a dynamic collaboration process. In particular, we focus on a use case related to the inspection of product defects, which involves a human operator, a dual-arm Baxter manipulator from Rethink Robotics and a Kuka youBot mobile manipulator.
In vitro assessment of retention and resistance failure loads of complete coverage restorations made for anterior maxillary teeth restored with two different cast post and core designs

Background The purpose of this in vitrostudy was to evaluate the retention and resistance form of complete coverage restorations supported by two different cast post and core designs. Material and Methods Forty extracted maxillary central incisors were randomly divided into four groups of 10 specimens each (namely A, B, C and D). All specimens were endodontically treated and a uniform post space of 9mm was created. All prepared teeth had a 360o chamfer ferrule of 2mm in axial height measured 0.5mm coronally from the cementoenamel junction (CEJ) and an axial wall thickness of 1.5 mm. Specimens in groups A and C received cast post and cores with the standardized core design, where the core ended at the coronal part of the ferrule, while specimens in groups B and D received cores that were encircling the ferrule. Cemented complete coverage restorations in groups A and B underwent tensile load stress, while the restorations in groups C and D underwent compressive load stress until failure. Results Teeth in group A exhibited a mean failure load of 326.14±83.67 N under tension, while teeth in group B exhibited a mean failure load of 332.79±80.38 N (p=0.858). Teeth in group C recorded a mean failure load of 1042.81±205.07 N, and in group D a mean failure load of 875.15±167.64 N (p=0.061) under compression was registered. Conclusions The standard cast post and core design with a 2 mm of ferrule height offers superior resistance, although not statistically significant (p=0.061), when compared to the core design encircling the axial wall ferrule. Both cast post and core designs offer equal retention. However, different failure modes of decementation were noted. Key words:Endodontically treated teeth, Post-and-core technique, Endodontic-post, Decementation, Root fracture, Ferrule effect.
SeqROCTM: A Matlab toolbox for the analysis of Sequence of Random Objects driven by Context Tree Models

In several research problems we face probabilistic sequences of inputs (e.g., sequence of stimuli) from which an agent generates a corresponding sequence of responses and it is of interest to model/discover some kind of relation between them. To model such relation in the context of statistical learning in neuroscience, a new class of stochastic process have been introduced [5], namely sequences of random objects driven by context tree models. In this paper we introduce a freely available Matlab toolbox (SeqROCTM) that implements three model selection methods to make inference about the parameters of this kind of stochastic process.