Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian Approach to Bandwidth Selection in Univariate Associate Kernel Estimation

The fundamental problem in the associate kernel estimation of density or probability mass function (pmf) is the choice of the bandwidth. In this paper, we use a Bayesian approach based upon likelihood cross-validation and a Monte Carlo Markov chain (MCMC) method for deriving the global optimal bandwidth. A comparative simulation study of the MCMC method and the classical methods that adopt the asymptotic mean integrated square error (AMISE) as criterion and the cross validation is presented for data generated from known densities and pmf, using standard AMISE and the practical integrated squared error. The simulation results show the superiority of the MCMC method over the classical methods.
Ejaculation Frequency and Prostate Cancer: CAPLIFE Study

Purpose To evaluate the association between ejaculation frequency (EF) during four stages of life and prostate cancer (PCa) according to tumor aggressiveness, PCa stage, and urinary symptomatology. Materials and Methods A total of 456 incident PCa cases histologically confirmed, and 427 controls aged 40–80 years from the CAPLIFE study were analyzed. This study is a population-based case-control study carried out in the south of Spain. Average EF was measured for: (1) 20s, (2) 30s, (3) 40s, and (4) one year before the interview. EF was categorized into: (1) 0–3, (2) 4, and (3) >4 ejaculations/month. Sociodemographic, lifestyle, and medical information were also collected. To estimate the association between EF and PCa, adjusted ORs (aORs) and 95% CIs were calculated by logistic regression models. Results A year before the interview, PCa cases ejaculated less frequently than the controls. An inverse association was observed between the EF a year before and PCa, aOR=1.64 (95% CI 1.03–2.61) for men with 4 ejaculations/month, and aOR=2.38 (95% CI 1.57–3.60) for men with 0–3 ejaculations/month, compared to men with >4. The association was higher for cases with ISUP 3–5 (aOR=2.76 [95% CI 1.34–5.67] for men with 0–3 ejaculations/month) or with a locally advanced-metastatic tumor (aOR=4.70 [95% CI 1.55–14.29]). Moreover, men with moderate urinary symptoms and 0–3 ejaculations/month had the highest risk, aOR=3.83 (95% CI 1.84–7.95). Conclusions A low EF could be associated with a higher risk of PCa, especially for cases with ISUP 3–5 or with a locally advanced-metastatic tumor.
On “epistemic injustice” and victimization in prisons

The article looks for reasons that explain the different results of criminal investigations conducted inside and outside prisons. We ask if and how the concept of epistemic injustice, as developed by Miranda Fricker, helps to understand those variations. The underlying hypothesis is that epistemic injustice is a symptom of a wider problem. The authors assume that the treatment of victims of violent crime inside prison has structural rather than interpersonal explanations. In a qualitative approach the study uses data from a series of semi-structured interviews with prisoners and prison officers (40 interviews in total). It explores the dynamics of the decision to report crime committed inside prisons and the role of different institutions involved in the investigation of these crimes from the perspective and experience of prisoners and prison officers. As result it is argued, that Fricker’s concept of epistemic injustice is not very helpful when it comes to understand epistemic injustice suffered by victims of violent crime inside prison. It can be better understood in the terms of epistemic oppression used by Dotson. Thus, it is not about assigning blame but how to change the underlying social relations and institutions that subordinate prisoners on epistemic grounds.
Spectral Correlation-Based Diverse Band Selection for Hyperspectral Image Classification

Band selection (BS), which can reduce the spectral dimensionality effectively, has become one of the most popular topics in hyperspectral image (HSI) analysis. Recently, sparse representation-based BS has emerged as a popular tool. The existing sparse models mainly focus on minimizing reconstruction error and sparsity, while do not fully exploit the unique correlations among hundreds of continuous bands, which may cause representative bands missed and highly correlated bands selected. Therefore, this article proposes the spectral correlation-based diverse BS (SCDBS) for HSIs to improve representativeness and diversity of the selected bands. Specifically, a correlation derived weight is used to perform weighted sparse reconstruction to select the bands that are more correlated with the whole HSI, and a correlation minimization term is designed to remove the highly correlated bands simultaneously. In addition, the proposed method imposes an adjustable sparse constraint by using an $\ell _{2,0< p\leq 1}$ norm, which extends and unifies the commonly used $\ell _{2,1}$ norm to provide more flexible sparsity level. To optimize the proposed BS model, an iteration algorithm with relatively low computational cost is designed, of which the convergence is theoretically presented. Experimental results on three benchmark datasets have demonstrated that the proposed SCDBS outperforms state-of-the-art methods in HSI classification.
Theory of the linewidth of semiconductor lasers

A theory of the spectral width of a single-mode semiconductor laser is presented and used to explain the recent measurements of Fleming and Mooradian on AlGaAs lasers. They found the linewidth to be inversely proportional to power and to have a value of 114 MHz at 1 mW per facet. This value is 30 times greater than can be explained by existing theories. The enhanced linewidth is attributed to the variation of the real refractive index n' with carrier density. Spontaneous emission induces phase and intensity changes in the laser field. The restoration of the laser to its steady-state intensity results in changes in the imaginary part of the refractive index \Delta n" . These changes are accompanied by changes in the real part of the refractive index \Delta n' , which cause additional phase fluctuations and line broadening. The linewidth enhancement is shown to be 1 + \alpha^{2} , where \alpha = \Delta n'/\Delta n" . A value of \alpha \approx 5.4 , needed to explain the observed linewidth, is close to the experimental values of a of 4.6 and 6.2.
Review of Artificial Neural Networks and Structural Equation Modeling Marketing and Consumer Research Applications.

The book “Artificial Neural Networks and Structural Equation Modeling: Marketing and Consumer Research Applications” by Alhamzah Alnoor, Khaw Khai Wah, and Azizul Hassan explores the use of an artificial neural network (ANN) and structural equation modeling (SEM) in marketing and consumer research. The authors aim to improve the validity of predictions and understand linear and non-compensated relationships by combining the SEM and ANN methods. The book is divided into four parts, covering the use of ANN and SEM in social commerce, marketing technology, sustainability of marketing, and future research directions.
Assessment of Oral Health Knowledge, Attitude, Practice and DMFT Scores among Patients at King Faisal University, Al-Ahsa

Background and Objectives: Oral health is one of the most significant issues in public health. The Decayed, Missing, and Filled Teeth (DMFT) Index is a useful tool for assessing and measuring the state of oral health in a community. This study aimed to evaluate oral health knowledge, attitudes, and practices among participants who visited a dental clinic at King Faisal University and to evaluate their DMFT scores. Materials and Methods: This cross-sectional, questionnaire-based study was conducted at the King Faisal University dental complex, Kingdom of Saudi Arabia, using a simple random sampling technique. The data were collected using a self-administered structured questionnaire in English and Arabic. All statistical analyses were carried out using the SPSS 20 software. A chi square and ANOVA test were used to assess the association. A p value of <0.05 was considered statistically significant. Results: There were a total of 260 participants, of whom 193 (74.2%) were male and 67 (25.8%) were female. Most participants, 173 (66.5%), were between the ages of 18 and 28. The majority of the participants 191 (73.5%) believed that bad oral hygiene led to gum disease. Additionally, major issues while visiting dental clinics, the importance of routine dental clinic visits, the existence of a connection between oral and general health, brushing time and frequency of change of used brush were significantly influenced by gender (p < 0.05). In terms of the DMFT index, mean numbers of decaying teeth (D) were 4.82 ± 4.15, mean numbers of missing teeth (M) were 1.56 ± 2.94, mean numbers of filled teeth (F) were 5.17 ± 5.28 and mean DMFT score was 11.56 ± 6.32, with a statistically significant difference observed (p < 0.001). Conclusions: This study concluded that, although some of the study participants neglected oral hygiene practices, the majority of participants had good knowledge and attitudes regarding the significance of oral hygiene. Owing to inadequate practices, the decayed, missing, and filled teeth scores increased with increasing age. Additionally, gender had no significant impact on the mean scores for decayed, missing, and filled teeth, although there were significant differences between age groups.
Approximation Schemes for Inferring Rankings and Clusterings from Pairwise Data

of “Approximation Schemes for Inferring Rankings and Clusterings from Pairwise Data” by Warren Schudy, Ph.D., Brown University, May 2011. In correlation clustering, given similarity or dissimilarity information for all pairs of data items, the goal is to find a clustering of the items into similarity classes, with the fewest inconsistencies with the input. This problem is hard to approximate in general but we give arbitrarily good approximation algorithms (PTASs) for two interesting special cases: when there are few clusters, and when the input is generated from a natural noisy model. In the feedback arc set problem in tournaments, given comparison information (a better than b) for all pairs of data items, the goal is to find a ranking of the items with the fewest inconsistencies with the input. We give the first PTAS for this problem. We then extend our techniques to a more general class of problems called fragile dense problems. Approximation Schemes for Inferring Rankings and Clusterings from Pairwise Data by Warren Schudy B. S. , Worcester Polytechnic Institute, 2005 Sc. M. , Brown University, 2007 A dissertation submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in the Department of Computer Science at Brown University Providence, Rhode Island May 2011 c © Copyright 2010 by Warren Schudy This work is based on earlier works: • How to rank with few errors, in STOC 2007, c © ACM, 2007. http://doi.acm. org/10.1145/1250790.1250806 • Yet another algorithm for dense max cut: go greedy, in SODA 2008, c © SIAM, 2008. • Linear time approximation schemes for the Gale-Berlekamp game and related minimization problems, in STOC 2009, c © ACM, 2009. http://doi.acm. org/10.1145/1536414.1536458 • Bounding and Comparing Methods for Correlation Clustering Beyond ILP, in ILP for NLP 2009, c © ACL, 2009. • Correlation Clustering with Noisy Input, in SODA 2010, c © SIAM, 2010. This dissertation by Warren Schudy is accepted in its present form by the Department of Computer Science as satisfying the dissertation requirement for the degree of Doctor of Philosophy. Date Claire Mathieu, Director Recommended to the Graduate Council Date Amy Greenwald, Reader Date Marek Karpinski, Reader (University of Bonn) Date Phillip Klein, Reader Approved by the Graduate Council Date Peter Weber Dean of the Graduate School
DEMPSTER-SHAFER THEORY WITH INDISCERNIBLE STATES AND OBSERVATIONS

An internal state s of a system is compatible with an empirical observation value x, if the possibility that s is the actual internal state of the system when x was observed cannot be logically avoided on the grounds of those laws and rules governing the system and its environment which are known to the investigator. The set of states and the set of empirical values are divided into equivalence classes by corresponding indiscernibility relations so that two states (two empirical values, resp.) in the same class are indistinguishable from each other and instead of a compatibility relation between particular states and empirical values only a compatibility relation between classes of states and classes of empirical values is given. It will be shown how to define, under such conditions, an approximation of the belief function definable when the original compatibility relation is at hand.
Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning

Generative Adversarial Networks (GAN) offer a promising approach for Neural Machine Translation (NMT). However, feeding multiple morphologically languages into a single model during training reduces the NMT's performance. In GAN, similar to bilingual models, multilingual NMT only considers one reference translation for each sentence during model training. This single reference translation limits the GAN model from learning sufficient information about the source sentence representation. Thus, in this article, we propose Denoising Adversarial Auto-encoder-based Sentence Interpolation (DAASI) approach to perform sentence interpolation by learning the intermediate latent representation of the source and target sentences of multilingual language pairs. Apart from latent representation, we also use the Wasserstein-GAN approach for the multilingual NMT model by incorporating the model generated sentences of multiple languages for reward computation. This computed reward optimizes the performance of the GAN-based multilingual model in an effective manner. We demonstrate the experiments on low-resource language pairs and find that our approach outperforms the existing state-of-the-art approaches for multilingual NMT with a performance gain of up to 4 BLEU points. Moreover, we use our trained model on zero-shot language pairs under an unsupervised scenario and show the robustness of the proposed approach.
A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks

Model extraction attacks have been widely applied, which can normally be used to recover confidential parameters of neural networks for multiple layers. Recently, side-channel analysis of neural networks allows parameter extraction even for networks with several multiple deep layers with high effectiveness. It is therefore of interest to implement a certain level of protection against these attacks. In this paper, we propose a desynchronization-based countermeasure that makes the timing analysis of activation functions harder. We analyze the timing properties of several activation functions and design the desynchronization in a way that the dependency on the input and the activation type is hidden. We experimentally verify the effectiveness of the countermeasure on a 32-bit ARM Cortex-M4 microcontroller and employ a t-test to show the side-channel information leakage. The overhead ultimately depends on the number of neurons in the fully-connected layer, for example, in the case of 4096 neurons in VGG-19, the overheads are between 2.8% and 11%.
Pair Programming with Large Language Models for Sampling and Estimation of Copulas

Without writing a single line of code by a human, an example Monte Carlo simulation based application for stochastic dependence modeling with copulas is developed using a state-of-the-art large language model (LLM) fine-tuned for conversations. This includes interaction with ChatGPT in natural language and using mathematical formalism, which, under careful supervision by a human-expert, led to producing a working code in MATLAB, Python and R for sampling from a given copula model, evaluation of the model's density, performing maximum likelihood estimation, optimizing the code for parallel computing for CPUs as well as for GPUs, and visualization of the computed results. In contrast to other emerging studies that assess the accuracy of LLMs like ChatGPT on tasks from a selected area, this work rather investigates ways how to achieve a successful solution of a standard statistical task in a collaboration of a human-expert and artificial intelligence (AI). Particularly, through careful prompt engineering, we separate successful solutions generated by ChatGPT from unsuccessful ones, resulting in a comprehensive list of related pros and cons. It is demonstrated that if the typical pitfalls are avoided, we can substantially benefit from collaborating with an AI partner. For example, we show that if ChatGPT is not able to provide a correct solution due to a lack of or incorrect knowledge, the human-expert can feed it with the correct knowledge, e.g., in the form of mathematical theorems and formulas, and make it to apply the gained knowledge in order to provide a solution that is correct. Such ability presents an attractive opportunity to achieve a programmed solution even for users with rather limited knowledge of programming techniques.
Learning Internal Representations of 3D Transformations from 2D Projected Inputs

When interacting in a three dimensional world, humans must estimate 3D structure from visual inputs projected down to two dimensional retinal images. It has been shown that humans use the persistence of object shape over motion-induced transformations as a cue to resolve depth ambiguity when solving this underconstrained problem. With the aim of understanding how biological vision systems may internally represent 3D transformations, we propose a computational model, based on a generative manifold model, which can be used to infer 3D structure from the motion of 2D points. Our model can also learn representations of the transformations with minimal supervision, providing a proof of concept for how humans may develop internal representations on a developmental or evolutionary time scale. Focused on rotational motion, we show how our model infers depth from moving 2D projected points, learns 3D rotational transformations from 2D training stimuli, and compares to human performance on psychophysical structure-from-motion experiments.
Rethinking interpretation: Input-agnostic saliency mapping of deep visual classifiers

Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby failing to answer input-independent inquiries about the model. We also show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. Current attempts to use `general' input features for model interpretation assume access to a dataset containing those features, which biases the interpretation. Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs. These features are geometrically correlated, and are computed by accumulating model's gradient information with respect to an unrestricted data distribution. To compute these features, we nudge independent data points over the model loss surface towards the local minima associated by a human-understandable concept, e.g., class label for classifiers. With a systematic projection, scaling and refinement process, this information is transformed into an interpretable visualization without compromising its model-fidelity. The visualization serves as a stand-alone qualitative interpretation. With an extensive evaluation, we not only demonstrate successful visualizations for a variety of concepts for large-scale models, but also showcase an interesting utility of this new form of saliency mapping by identifying backdoor signatures in compromised classifiers.
Visual Response to Emotional State of User Interaction

This work proposes an interactive art installation"Mood spRing"designed to reflect the mood of the environment through interpretation of language and tone. Mood spRing consists of an AI program that controls an immersive 3D animation of the seasons. If the AI program perceives the language and tone of the users as pleasant, the animation progresses through idealized renditions of seasons. Otherwise, it slips into unpleasant weather and natural disasters of the season. To interpret the language and tone of the user interaction, hybrid state-of-the-art emotion detection methods are applied to the user audio and text inputs. The emotional states detected separately from tone and language are fused by a novel approach that aims at minimizing the possible model disparity across diverse demographic groups.
On the nature of compact stars determined by gravitational waves, radio-astronomy, x-ray emission and nuclear physics

We investigate the question of the nature of compact stars, considering they may be neutron stars or hybrid stars containing a quark core, within the present constraints given by gravitational waves, radio-astronomy, X-ray emissions from millisecond pulsars and nuclear physics. A Bayesian framework is used to combine together all these constraints and to predict tidal deformabilities and radii for a 1.4~M$_\odot$ compact star. We find that present gravitation wave and radio-astronomy data favors asy-stiff EoS compatible with nuclear physics and that GW170817 waveform is best described for binary hybrid stars. In addition, this data favors stiff quark matter, independently of the nuclear EoS. Combining this result with constraints from X-ray observation supports the existence of canonical $1.4$~M$_\odot$ mass hybrid star, with a radius predicted to be $R_{1.4}=12.02(8)$~km.
Deformation of D-branes in three-dimensional anti-de Sitter black holes

This talk discusses the possible extension of the relation between string theory and noncommutative geometry to the case of BTZ black holes. After emphasizing some geometrical properties of these black holes, we show that the non-rotating massive BTZ black holes supports winding D1-brane configurations, which cross the black hole horizons and singularities. We then discuss strict deformation quantization of these D-branes worldvolumes.
Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces

This paper proposes a new method for anomaly detection in time-series data by incorporating the concept of difference subspace into the singular spectrum analysis (SSA). The key idea is to monitor slight temporal variations of the difference subspace between two signal subspaces corresponding to the past and present time-series data, as anomaly score. It is a natural generalization of the conventional SSA-based method which measures the minimum angle between the two signal subspaces as the degree of changes. By replacing the minimum angle with the difference subspace, our method boosts the performance while using the SSA-based framework as it can capture the whole structural difference between the two subspaces in its magnitude and direction. We demonstrate our method's effectiveness through performance evaluations on public time-series datasets.