Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm

Statistical machine learning models trained with stochastic gradient algorithms are increasingly being deployed in critical scientific applications. However, computing the stochastic gradient in several such applications is highly expensive or even impossible at times. In such cases, derivative-free or zeroth-order algorithms are used. An important question which has thus far not been addressed sufficiently in the statistical machine learning literature is that of equipping stochastic zeroth-order algorithms with practical yet rigorous inferential capabilities so that we not only have point estimates or predictions but also quantify the associated uncertainty via confidence intervals or sets. Towards this, in this work, we first establish a central limit theorem for Polyak-Ruppert averaged stochastic zeroth-order gradient algorithm. We then provide online estimators of the asymptotic covariance matrix appearing in the central limit theorem, thereby providing a practical procedure for constructing asymptotically valid confidence sets (or intervals) for parameter estimation (or prediction) in the zeroth-order setting.
CaPC Learning: Confidential and Private Collaborative Learning

Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data. In many contexts, such as healthcare and finance, separate parties may wish to collaborate and learn from each other's data but are prevented from doing so due to privacy regulations. Some regulations prevent explicit sharing of data between parties by joining datasets in a central location (confidentiality). Others also limit implicit sharing of data, e.g., through model predictions (privacy). There is currently no method that enables machine learning in such a setting, where both confidentiality and privacy need to be preserved, to prevent both explicit and implicit sharing of data. Federated learning only provides confidentiality, not privacy, since gradients shared still contain private information. Differentially private learning assumes unreasonably large datasets. Furthermore, both of these learning paradigms produce a central model whose architecture was previously agreed upon by all parties rather than enabling collaborative learning where each party learns and improves their own local model. We introduce Confidential and Private Collaborative (CaPC) learning, the first method provably achieving both confidentiality and privacy in a collaborative setting. We leverage secure multi-party computation (MPC), homomorphic encryption (HE), and other techniques in combination with privately aggregated teacher models. We demonstrate how CaPC allows participants to collaborate without having to explicitly join their training sets or train a central model. Each party is able to improve the accuracy and fairness of their model, even in settings where each party has a model that performs well on their own dataset or when datasets are not IID and model architectures are heterogeneous across parties.
Regularization Strategies for Quantile Regression

We investigate different methods for regularizing quantile regression when predicting either a subset of quantiles or the full inverse CDF. We show that minimizing an expected pinball loss over a continuous distribution of quantiles is a good regularizer even when only predicting a specific quantile. For predicting multiple quantiles, we propose achieving the classic goal of non-crossing quantiles by using deep lattice networks that treat the quantile as a monotonic input feature, and we discuss why monotonicity on other features is an apt regularizer for quantile regression. We show that lattice models enable regularizing the predicted distribution to a location-scale family. Lastly, we propose applying rate constraints to improve the calibration of the quantile predictions on specific subsets of interest and improve fairness metrics. We demonstrate our contributions on simulations, benchmark datasets, and real quantile regression problems.
An Optimal Witness Function for Two-Sample Testing

We propose data-dependent test statistics based on a one-dimensional witness function, which we call witness two-sample tests (WiTS tests). We first optimize the witness function by maximizing an asymptotic test-power objective and then use as the test statistic the difference in means of the witness evaluated on two held-out test samples. When the witness function belongs to a reproducing kernel Hilbert space, we show that the optimal witness is given via kernel Fisher discriminant analysis, whose solution we compute in closed form. We show that the WiTS test based on a characteristic kernel is consistent against any fixed alternative. Our experiments demonstrate that the WiTS test can achieve higher test power than existing two-sample tests with optimized kernels, suggesting that learning a highor infinitedimensional representation of the data may not be necessary for two-sample testing. The proposed procedure works beyond kernel methods, allowing practitioners to apply it within their preferred machine learning framework.
Enabling multi-programming mechanism for quantum computing in the NISQ era

NISQ devices have several physical limitations and unavoidable noisy quantum operations, and only small circuits can be executed on a quantum machine to get reliable results. This leads to the quantum hardware under-utilization issue. Here, we address this problem and improve the quantum hardware throughput by proposing a Quantum Multi-programming Compiler (QuMC) to execute multiple quantum circuits on quantum hardware simultaneously. This approach can also reduce the total runtime of circuits. We first introduce a parallelism manager to select an appropriate number of circuits to be executed at the same time. Second, we present two different qubit partitioning algorithms to allocate reliable partitions to multiple circuits – a greedy and a heuristic. Third, we use the Simultaneous Randomized Benchmarking protocol to characterize the crosstalk properties and consider them in the qubit partition process to avoid the crosstalk effect during simultaneous executions. Finally, we enhance the mapping transition algorithm to make circuits executable on hardware using a decreased number of inserted gates. We demonstrate the performance of our QuMC approach by executing circuits of different sizes on IBM quantum hardware simultaneously. We also investigate this method on VQE algorithm to reduce its overhead.
The different responses of universities to introduction of performance-based research funding

Governments and organizations design performance-based research funding systems (PBFRS) for strategic aims, such as to selectively allocate scarce resources and stimulate research efficiency. In this work we analyze the relative change in research productivity of Italian universities after the introduction of such a system, featuring financial and reputational incentives. Using a bibliometric approach, we compare the relative research performance of universities before and after introduction of PBFRS, at the overall, discipline and field levels. The findings show convergence in the universities’ performance, due above all to the remarkable improvement of the lowest performers. Geographically, the universities of the south (versus central and northern Italy) achieved the greatest improvement in relative performance. The methodology, and results, should be of use to university management and policy-makers.
Repositório ISCTE-IUL

—The Department of Homeland Security in the United States estimates that 90% of software vulnerabilities can be traced back to defects in design and software coding. The ﬁnancial impact of these vulnerabilities has been shown to exceed 380 million USD in industrial control systems alone. Since software developers write software, they also introduce these vulnerabilities into the source code. However, secure coding guidelines exist to prevent software developers from writing vulnerable code. This study focuses on the human factor, the software developer, and secure coding, in particular secure coding guidelines. We want to understand the software developers’ awareness and compliance to secure coding guidelines and why, if at all, they aren’t compliant or aware. We base our results on a large-scale survey on secure coding guidelines, with more than 190 industrial software developers. Our work’s main contribution motivates the need to educate industrial software developers on secure coding guidelines, and it gives a list of ﬁfteen actionable items to be used by practitioners in the industry. We also make our raw data openly available for further research.
Once is Never Enough: Foundations for Sound Statistical Inference in Tor Network Experimentation

Tor is a popular low-latency anonymous communication system that focuses on usability and performance: a faster network will attract more users, which in turn will improve the anonymity of everyone using the system. The standard practice for previous research attempting to enhance Tor performance is to draw conclusions from the observed results of a single simulation for standard Tor and for each research variant. But because the simulations are run in sampled Tor networks, it is possible that sampling error alone could cause the observed effects. Therefore, we call into question the practical meaning of any conclusions that are drawn without considering the statistical significance of the reported results. In this paper, we build foundations upon which we improve the Tor experimental method. First, we present a new Tor network modeling methodology that produces more representative Tor networks as well as new and improved experimentation tools that run Tor simulations faster and at a larger scale than was previously possible. We showcase these contributions by running simulations with 6,489 relays and 792k simultaneously active users, the largest known Tor network simulations and the first at a network scale of 100%. Second, we present new statistical methodologies through which we: (i) show that running multiple simulations in independently sampled networks is necessary in order to produce informative results; and (ii) show how to use the results from multiple simulations to conduct sound statistical inference. We present a case study using 420 simulations to demonstrate how to apply our methodologies to a concrete set of Tor experiments and how to analyze the results.
Radiative shocks around super-eddington accreting black holes

We examine radiative standing shocks in advective accretion flows around stellarmass black holes by 2D radiation hydrodynamic simulations, focusing on the superEddington accreting flow. Under a set of input flow parameters responsible for the standing shock, the shock location on the equator decreases toward the event horizon with an increasing accretion rate. The optically thin and hot gas in the narrow funnel region along the rotational axis changes gradually into a dense and optically thick state with the increasingly dense gas transported from the base of the radiative shock near the equator. As a result, the luminosity becomes as high as ∼ 10 erg s, and the radiation shows a strongly anisotropic distribution around the rotational axis and then very low edge-on luminosity as ∼ 10 erg s. The mass outflow rate from the outer boundary is high as ∼ 10 and 10−4M⊙ yr −1 but most of the outflow is originated through the radial outer boundary and may be observed over a wide wind region. The models show approximately black body spectra with a temperature of 5×10 – 3×10 K at the vertical outer boundary surface. The radiative shock models with the super-Eddington luminosities show a possible model for the superaccretor SS 433 and Ultraluminous X-ray sources with stellar-mass black holes.
Downlink Channel Reconstruction for Spatial Multiplexing in Massive MIMO Systems

To get channel state information (CSI) at a base station (BS), most of researches on massive multiple-input multiple-output (MIMO) systems consider time division duplexing (TDD) to get benefit from the uplink and downlink channel reciprocity. Even in TDD, however, the BS still needs to transmit downlink training signals, which are referred to as channel state information reference signals (CSI-RSs) in the 3GPP standard, to support spatial multiplexing in practice. This is because there are many cases that the number of transmit antennas is less than the number of receive antennas at a user equipment (UE) due to power consumption and circuit complexity issues. Because of this mismatch, uplink sounding reference signals (SRSs) from the UE are not enough for the BS to obtain full downlink MIMO CSI. Therefore, after receiving the downlink CSI-RSs, the UE needs to feedback quantized CSI to the BS using a pre-defined codebook to support spatial multiplexing. In this paper, possible approaches to reconstruct full downlink MIMO CSI at the BS are proposed by exploiting both the SRS and quantized downlink CSI considering practical antenna structures with reduced downlink CSI-RS overhead. Numerical results show that the spectral efficiencies by spatial multiplexing based on the proposed downlink MIMO CSI reconstruction techniques outperform the conventional methods solely based on the quantized CSI.
On complete multipartite derangement graphs

Given a finite transitive permutation group $G\leq \operatorname{Sym}(\Omega)$, with $|\Omega|\geq 2$, the derangement graph $\Gamma_G$ of $G$ is the Cayley graph $\operatorname{Cay}(G,\operatorname{Der}(G))$, where $\operatorname{Der}(G)$ is the set of all derangements of $G$. Meagher et al. [On triangles in derangement graphs, J. Combin. Theory, Ser. A, 180:105390, 2021] recently proved that $\operatorname{Sym}(2)$ acting on $\{1,2\}$ is the only transitive group whose derangement graph is bipartite and any transitive group of degree at least three has a triangle in its derangement graph. They also showed that there exist transitive groups whose derangement graphs are complete multipartite. This paper gives two new families of transitive groups with complete multipartite derangement graphs. In addition, we construct an infinite family of transitive groups whose derangement graphs are multipartite but not complete.
Student sentiment Analysis Using Classification With Feature Extraction Techniques

Technical growths have empowered, numerous revolutions in the educational system by acquainting with technology into the classroom and by elevating the learning experience. Nowadays Webbased learning is getting much popularity. This paper describes the web-based learning and their effectiveness towards students. One of the prime factors in education or learning system is feedback; it is beneficial to learning if it must be used effectively. In this paper, we worked on how machine learning techniques like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) can be applied over Web-based learning, emphasis given on sentiment present in the feedback students. We also work on two types of Feature Extraction Technique (FETs) namely Count Vector (CVr) or Bag of Words) (BoW) and Term Frequency and Inverse Document Frequency (TF-IDF) Vector. In the research study, it is our goal for our proposed LR, SVM, NB, and DT models to classify the presence of Student Feedback Dataset (SFB) with improved accuracy with cleaned dataset and feature extraction techniques. The SFB is one of the significant concerns among the student sentimental analysis. Keywords—Sentiment Analysis, Web-Based Learning System (WBLS), Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Bag of Words (BoWs) and Term Frequency Inverse Document Frequency (TF-IDF) Vector
$\mathcal{P}$, $\mathcal{T}$-odd Faraday rotation in intracavity absorption spectroscopy with molecular beam as a possible way to improve the sensitivity of the search for the time reflection noninvariant effects in nature

The present constraint on the space parity (P) and time reflection invariance (T ) violating electron electric dipole moment (eEDM) is based on the observation of the electron spin precession in an external electric field using the ThO molecule. We propose an alternative approach: observation of the P, T -odd Faraday effect in an external electric field using the cavity-enhanced polarimetric scheme in combination with a molecular beam crossing the cavity. Our theoretical simulation of the proposed experiment with the PbF and ThO molecular beams show that the present constraint on the eEDM in principle can be improved by a few orders of magnitude.
Hot Jupiters: Origins, Structure, Atmospheres

We provide a brief review of many aspects of the planetary physics of hot Jupiters. Our aim is to cover most of the major areas of current study while providing the reader with additional references for more detailed follow‐up. We first discuss giant planet formation and subsequent orbital evolution via disk‐driven torques or dynamical interactions. More than one formation pathway is needed to understand the population. Next, we examine our current understanding of the evolutionary history and current interior structure of the planets, where we focus on bulk composition as well as viable models to explain the inflated radii of the population. Finally, we discuss aspects of their atmospheres in the context of observations and 1D and 3D models, including atmospheric structure and escape, spectroscopic signatures, and complex atmospheric circulation. The major opacity sources in these atmospheres, including alkali metals, water vapor, and others, are discussed. We discuss physics that control the 3D atmospheric circulation and day‐to‐night temperature structures. We conclude by suggesting important future work for still‐open questions.
Synthesis of Hydroxylapatite from Sand Dollar and β-Tricalcium Phosphate by Solid-State Method

Abstract Synthesis of porous hydroxylapatite from the sand dollar (Mellita eduardobarrosoi sp. nov.) and β-tricalcium phosphate (β-TCP) by solid-state reaction was performed. Reaction conditions were explored at different molar ratios of the precursors and at different temperatures. X-ray diffraction was used to determine the crystalline structure of the sample at molar ratio of 1.25 TCP : 1 sand dollar at 1473 K. The morphology of the product obtained under these conditions is a porous structure, which is a requirement for better clinical application of the hydroxylapatite as a biomaterial. Chemical analysis carried out by energy-dispersive spectroscopy on the product presented a molar ratio of Ca/P closer to the stoichiometric molar ratio of the pure hydroxylapatite. This crystalline structure was analyzed by transmission electron microscopy, and large single hydroxylapatite crystals were observed. Electron diffraction patterns in different directions are discussed.
Delay-Phase Precoding for Wideband THz Massive MIMO

Benefiting from tens of GHz of bandwidth, terahertz (THz) communication has become a promising technology for future 6G network. To deal with the serious propagation loss of THz signals, massive multiple-input multiple-output (MIMO) with hybrid precoding is utilized to generate directional beams with high array gains. However, the standard hybrid precoding architecture based on frequency-independent phase-shifters cannot cope with the beam split effect in THz massive MIMO caused by the large bandwidth and the large number of antennas, where the beams split into different physical directions at different frequencies. The beam split effect will result in a serious array gain loss across the entire bandwidth, which has not been well investigated in THz massive MIMO. In this paper, we first quantify the seriousness of the beam split effect in THz massive MIMO by analyzing the array gain loss it causes. Then, we propose a new precoding architecture called delay-phase precoding (DPP) to mitigate this effect. Specifically, the proposed DPP introduces a time delay network composed of a small number of time delay elements between radio-frequency chains and phase-shifters in the standard hybrid precoding architecture. Unlike frequency-independent phase shifts, the time delay network introduced in the DPP can realize frequency-dependent phase shifts, which can be designed to generate frequency-dependent beams towards the target physical direction across the entire bandwidth. Due to the joint control of delay and phase, the proposed DPP can alleviate the array gain loss caused by the beam split effect. Furthermore, we propose a hardware structure by using true-time-delayers to realize frequency-dependent phase shifts for realizing the concept of DPP. A corresponding precoding algorithm is proposed to realize the precoding design. Theoretical analysis and simulations show that the proposed DPP can mitigate the beam split effect and achieve near-optimal rate with higher energy efficiency.
Voice Cloning: a Multi-Speaker Text-to-Speech Synthesis Approach based on Transfer Learning

Deep learning models are becoming predominant in many fields of machine learning. Text-to-Speech (TTS), the process of synthesizing artificial speech from text, is no exception. To this end, a deep neural network is usually trained using a corpus of several hours of recorded speech from a single speaker. Trying to produce the voice of a speaker other than the one learned is expensive and requires large effort since it is necessary to record a new dataset and retrain the model. This is the main reason why the TTS models are usually single speaker. The proposed approach has the goal to overcome these limitations trying to obtain a system which is able to model a multi-speaker acoustic space. This allows the generation of speech audio similar to the voice of different target speakers, even if they were not observed during the training phase.
Medical image quality metrics for foveated model observers

Abstract. Purpose: A recently proposed model observer mimics the foveated nature of the human visual system by processing the entire image with varying spatial detail, executing eye movements, and scrolling through slices. The model can predict how human search performance changes with signal type and modality (2D versus 3D), yet its implementation is computationally expensive and time-consuming. Here, we evaluate various image quality metrics using extensions of the classic index of detectability expression and assess foveated model observers for search tasks. Approach: We evaluated foveated extensions of a channelized Hotelling and nonprewhitening matched filter model with an eye filter. The proposed methods involve calculating a model index of detectability (d ′ ) for each retinal eccentricity and combining these with a weighting function into a single detectability metric. We assessed different versions of the weighting function that varied in the required measurements of the human observers’ search (no measurements, eye movement patterns, size of the image, and median search times). Results: We show that the index of detectability across eccentricities weighted using the eye movement patterns of observers best predicted human performance in 2D versus 3D search performance for a small microcalcification-like signal and a larger mass-like. The metric with a weighting function based on median search times was the second best predicting human results. Conclusions: The findings provide a set of model observer tools to evaluate image quality in the early stages of imaging system evaluation or design without implementing the more computationally complex foveated search model.