Miracle-dz/UniFAR-FTUs huggingface dataset:
One-Round Communication Efficient Distributed M-Estimation

Communication cost and local computation complexity are two main bottlenecks of the distributed statistical learning. In this paper, we consider the distributed M-estimation problem in both regular and sparse case and propose a novel one-round communication eﬃcient algorithm. For regular distributed M-estimator, the asymptotic normality is provided to conduct statistical inference. For sparse distributed M-estimator, we only require solving a quadratic Lasso problem in the master machine using the same local information as the regular distributed M-estimator. Consequently, the computation complexity of the local machine is suﬃciently reduced compared with the existing debiased sparse estimator. Under mild conditions, the theoretical results guarantee that our proposed distributed estimators achieve (near)optimal statistical convergence rate. The eﬀectiveness of our proposed algorithm is veriﬁed through experiments across diﬀerent M-estimation problems using both synthetic and real benchmark datasets.
Principal Component Regression with Semirandom Observations via Matrix Completion

Principal Component Regression (PCR) is a popular method for prediction from data, and is one way to address the so-called multi-collinearity problem in regression. It was shown recently that algorithms for PCR such as hard singular value thresholding (HSVT) are also quite robust, in that they can handle data that has missing or noisy covariates. However, such spectral approaches require strong distributional assumptions on which entries are observed. Speciﬁcally, every co-variate is assumed to be observed with probability (exactly) p , for some value of p . Our goal in this work is to weaken this requirement, and as a step towards this, we study a “semi-random” model. In this model, every covariate is revealed with probability p , and then an adversary comes in and reveals additional covariates. While the model seems intuitively easier, it is well known that algorithms such as HSVT perform poorly. Our approach is based on studying the closely re-lated problem of Noisy Matrix Completion in a semi-random setting. By considering a new semideﬁnite programming relaxation, we develop new guarantees for matrix completion, which is our core technical contribution.
Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries

While the literature on the theory of pool-based active learning has seen much progress in the past 15 years, and is now fairly mature, much less is known about its cousin problem: online selective sampling. In the stochastic online learning setting, there is a stream of iid data, and the learner is required to predict a label for each instance, and we are interested in the rate of growth of the number of mistakes the learner makes. In the selective sampling variant of this problem, after each prediction, the learner can optionally request to observe the true classiﬁcation of the point. This introduces a trade-oﬀ between the number of these queries and the number of mistakes as a function of the number T of samples in the sequence. This work explores various properties of the optimal trade-oﬀ curve, both abstractly (for general VC classes), and more-concretely for several constructed examples that expose important properties of the trade-oﬀ.
Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers

Volume preservation is usually regarded as a necessary property for the leapfrog transition functions that are used in Hamiltonian Monte Carlo (HMC) and No-U-Turn (NUTS) samplers to guarantee convergence to the target distribution. In this work we rigorously prove that with minimal algorithmic modifications, both HMC and NUTS can be combined with transition functions that are not necessarily volume preserving. In light of these results, we propose a non-volume preserving transition function that conserves the Hamiltonian better than the baseline leapfrog mechanism, on piecewise-continuous distributions. The resulting samplers do not require any assumptions on the geometry of the discontinuity boundaries, and our experimental results show a significant improvement upon traditional HMC and NUTS.
A novel approach of CNN for human motor imagery recognition using the virtual electrode pairs

In order to develop an efficient brain-computer interface system, the brain activity measured by electroencephalography needs to be accurately decoded. In this paper, a motor imagery classification approach is proposed, combining virtual electrodes on the cortex layer with a convolutional neural network; this can effectively improve the decoding performance of the brain-computer interface system. A three layer (cortex, skull, and scalp) head volume conduction model was established by using the symmetric boundary element method to map the scalp signal to the cortex area. Nine pairs of virtual electrodes were created on the cortex layer, and the features of the time and frequency sequence from the virtual electrodes were extracted by performing time-frequency analysis. Finally, the convolutional neural network was used to classify motor imagery tasks. The results show that the proposed approach is convergent in both the training model and the test model. Based on the Physionet motor imagery database, the averaged accuracy can reach 98.32% for a single subject, while the averaged values of accuracy, Kappa, precision, recall, and F1-score on the group-wise are 96.23%, 94.83%, 96.21%, 96.13%, and 96.14%, respectively. Based on the High Gamma database, the averaged accuracy has achieved 96.37% and 91.21% at the subject and group levels, respectively. Moreover, this approach is superior to those of other studies on the same database, which suggests robustness and adaptability to individual variability.
Comparison of six derivatizing agents for the determination of nine synthetic cathinones using gas chromatography-mass spectrometry

Six acylation reagents have been compared for their derivatisation potential towards nine synthetic cathinones by gas chromatography-mass spectrometry (GC-MS). The evaluated reagents were pentafluoropropionic anhydride (PFPA), trifluoroacetic anhydride (TFA), chlorodifluoroacetic anhydride (CLF2AA), heptafluorobutyric anhydride (HFBA), acetic anhydride (AA) and propionic anhydride (PA). The synthetic cathinones included flephedrone (4-fluoromethcathinone or 4-FMC), mephedrone (4-methylmethcathinone or 4-MMC), pentedrone (also known as α-methylamino-valerophenone), methedrone (4-methoxy-N-methcathinone, p-methoxymethcathinone), methylone (3,4-methylenedioxy-N-methylcathinone or bk-MDMA), butylone (β-keto-N-methylbenzodioxolylbutanamine or bk-MBDB), ethylone (3,4-methylenedioxy-N-ethylcathinone MDEC or bk-MDEA), pyrovalerone (4-methyl-β-keto-prolintane) and 3,4-methylenedioxypyrovalerone (MDPV). The derivatizing agents were optimised for incubation time and temperature with some important validation parameters studied to evaluate derivatisation reactions. The anhydrides studied proved to be suitable for synthetic cathinones – all of them showing RSD and accuracy below 20%. PFPA and HFBA followed by TFA are the best choice of derivatising agents based on validation parameters. Five internal standards were evaluated with good results. Three way ANOVA, interference, fragmentation patterns and high peak area values at a concentration of 0.50 μg ml−1 were evaluated and discussed. AA and PA derivatives give high relative abundance for most drugs examined. HFBA gives more ions and multi-fragmentation patterns.
Rao-Blackwellised parallel MCMC

Multiple proposal Markov chain Monte Carlo (MP-MCMC) as introduced by Calderhead (2014) allow for computationally eﬃcient and parallelisable inference, whereby multiple states are proposed and computed simultaneously. In this paper, we improve the resulting integral estimators by sequentially using the multiple states within a Rao-Blackwellised estimator. We further pro-pose a novel adaptive Rao-Blackwellised MP-MCMC algorithm, which generalises the adaptive MCMC algorithm introduced by Haario et al. (2001) to allow for multiple proposals. We prove its asymptotic unbiasedness, and demonstrate signiﬁcant improvements in sampling eﬃciency through numerical studies.
Latent Derivative Bayesian Last Layer Networks

Bayesian neural networks (BNN) are powerful parametric models for nonlinear regression with uncertainty quantiﬁcation. However, the approximate inference techniques for weight space priors suﬀer from several drawbacks. The ‘Bayesian last layer’ (BLL) is an alternative BNN approach that learns the feature space for an exact Bayesian linear model with explicit predictive distributions. However, its predictions outside of the data distribution (OOD) are typically overconﬁdent, as the marginal likelihood objective results in a learned feature space that overﬁts to the data. We overcome this weakness by introducing a functional prior on the model’s derivatives w.r. t. the inputs. Treating these Jacobians as latent variables, we incorporate the prior into the objective to inﬂuence the smoothness and diversity of the features, which enables greater predictive uncertainty. For the BLL, the Jaco-bians can be computed directly using forward mode automatic diﬀerentiation, and the distribution over Jacobians may be obtained in closed-form. We demonstrate this method enhances the BLL to Gaussian process-like performance on tasks where calibrated uncertainty is critical: OOD regression, Bayesian optimization and active learning, which include high-dimensional real-world datasets.
One-pass Stochastic Gradient Descent in overparametrized two-layer neural networks

There has been a recent surge of interest in understanding the convergence of gradient descent (GD) and stochastic gradient descent (SGD) in overparameterized neural networks. Most previous works assume that the training data is provided a priori in a batch, while less attention has been paid to the important setting where the training data arrives in a stream. In this paper, we study the streaming data setup and show that with overparamterization and random initialization, the prediction error of two-layer neural networks under one-pass SGD converges in expectation. The convergence rate depends on the eigen-decomposition of the integral operator associated with the so-called neural tangent kernel (NTK). A key step of our analysis is to show a random kernel function converges to the NTK with high probability using the VC dimension and McDiarmid's inequality.
Differentially Private Monotone Submodular Maximization Under Matroid and Knapsack Constraints

Numerous tasks in machine learning and artiﬁ-cial intelligence have been modeled as submodular maximization problems. These problems usually involve sensitive data about individuals, and in addition to maximizing the utility, privacy concerns should be considered. In this paper, we study the general framework of non-negative monotone submodular maximization subject to matroid or knapsack constraints in both oﬄine and online settings. For the oﬄine setting, we propose a diﬀerentially private (1 − κe ) -approximation algorithm, where κ ∈ [0 , 1] is the total curvature of the submodular set function, which improves upon prior works in terms of approximation guarantee and query complexity under the same privacy budget. In the online setting, we propose the ﬁrst diﬀerentially private algorithm, and we specify the conditions under which the regret bound scales as O ( √ T ) , i.e., privacy could be ensured while maintaining the same regret bound as the optimal regret guarantee in the non-private setting.
Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms

State-of-the-art subspace clustering methods are based on convex formulations whose theoretical guarantees require the subspaces to be low-dimensional. Dual Principal Component Pursuit (DPCP) is a non-convex method that is speciﬁcally designed for learning high-dimensional subspaces, such as hyperplanes. However, existing analyses of DPCP in the multi-hyperplane case lack a precise characterization of the distribution of the data and involve quantities that are diﬃcult to interpret. Moreover, the provable algorithm based on recursive linear programming is not ef-ﬁcient. In this paper, we introduce a new notion of geometric dominance , which explicitly captures the distribution of the data, and derive both geometric and probabilistic conditions under which a global solution to DPCP is a normal vector to a geometrically dominant hyperplane. We then prove that the DPCP problem for a union of hyperplanes satisﬁes a Riemannian regularity condition, and use this result to show that a scalable Riemannian subgradient method exhibits (local) linear convergence to the normal vector of the geometrically dominant hyperplane. Finally, we show that integrating DPCP into popular subspace clustering schemes, such as K -ensembles, leads to superior or competitive performance over the state-of-the-art in clustering hyperplanes.
On the Faster Alternating Least-Squares for CCA

We study alternating least-squares (ALS) for canonical correlation analysis (CCA). Recent research shows that the alternating least-squares solver for k -CCA can be directly accelerated with momentum and prominent performance gain has been observed in practice for the resulting simple algorithm. However, despite the simplicity, it is diﬃcult for the accelerated rate to be analyzed in theory in order to explain and match the empirical performance gain. By looking into two neighboring iterations, in this work, we propose an even simpler variant of the faster alternating least-squares solver. Instead of applying momentum to each update for acceleration, the proposed variant only leverages momentum at every other iteration and can converge at a provably faster linear rate of nearly square-root dependence on the singular value gap of the whitened cross-covariance matrix. In addition to the high consistency between theory and practice, experimental studies also show that our variant of the alternating least-squares algorithm as a block CCA solver is even more pass eﬃcient than other variants.
Simultaneous spectrophotometric estimation of norethindrone acetate and ethinyl estradiol in formulation

A combination of second order derivative spectrophotometric method and absorbance correction method has been proposed for simultaneous spectrophotometric estimation of Norethindrone Acetate and Ethinyl Estradiol. Amplitude of second order derivative at 266.8 nm was used for quantitation of NorethindroneAcetate and Ethinyl Estradiol was estimated by absorbance correction method after correcting the absorbance for Norethindrone Acetate at 280 nm. The content of Ethinyl Estradiol and Norethindrone Acetate were found about 105.77% and 101.56% in tablet mixture respectively. The method has been validated in accordance with ICH guidelines and the results for all parameters were found to be within the acceptance criteria.
Deep Learning based video analytics solutions running on board of smart cameras

—A.I. Tech is a spinoff company of the University of Salerno, designing and developing cutting edge video analytics solutions based on deep learning able to run directly on board of smart cameras, equipped with limited resources capabilities. A.I. Tech solutions that will be presented during the demo help to manage several vertical markets, from retail and business intelligence to security and safety, from smart parking to smart city and smart roads.
Proportionality in Spatial Keyword Search

More often than not, spatial objects are associated with some context, in the form of text, descriptive tags (e.g. points of interest, flickr photos), or linked entities in semantic graphs (e.g. Yago2, DBpedia). Hence, location-based retrieval should be extended to consider not only the locations but also the context of the objects, especially when the retrieved objects are too many and the query result is overwhelming. In this paper, we study the problem of selecting a subset of the query result, which is the most representative. We argue that objects with similar context and nearby locations should proportionally be represented in the selection. Proportionality dictates the pairwise comparison of all retrieved objects and hence bears a high cost. We propose novel algorithms which greatly reduce the cost of proportional object selection in practice. Extensive empirical studies on real datasets show that our algorithms are effective and efficient. A user evaluation verifies that proportional selection is more preferable than random selection and selection based on object diversification.
Evaluation of Al‐based nanoparticle‐impregnated sawdust as an adsorbent from byproduct for the removal of arsenic(V) from aqueous solutions

In this research, sawdust was modified with chemical reagents to produce an adsorbent from byproduct, namely, Al‐based nanoparticle‐impregnated sawdust (ANIS) to remove arsenate [As(V)] from aqueous solutions. Several methods, including scanning electron microscopy with an energy‐dispersive spectrometry, Brunauer–Emmett–Teller surface area analysis, and thermogravimetric analysis, were performed to characterize this material. Results showed that Al‐based nanoparticles were successfully impregnated in the sawdust and thus significantly changed their surface characteristics. Batch adsorption studies were conducted to determine the properties and mechanisms of As(V) adsorption onto ANIS. The maximum adsorption capacity of As(V) was 17.76 mg/g, which was higher than that of most adsorbents from byproducts. However, the adsorption of As(V) was adversely affected by alkaline conditions. Some interfering anions, especially phosphate, inhibited the ANIS‐induced As(V) removal. Fourier transform infrared spectroscopy and X‐ray photoelectron spectroscopy suggested that the oxygen‐containing functional groups of Al oxides were the main contributors to As(V) adsorption, and the main adsorption mechanism was surface complexation. Approximately 2664 bed volumes of simulated groundwater containing 150 µg/L As(V) were treated before a breakthrough was observed in the small‐scale column experiments. Thus, ANIS could be an efficient material for As(V) removal. This study indicated that ANIS can be used as a reliable option for As decontamination. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1314–1322, 2017
Light Field Reconstruction using a Generic Imaging Model

Light ﬁeld cameras play an increasingly important role in computer vision and optical metrology. However, due to their complex design, their calibration is very difﬁcult and usually precisely adapted to the respective light ﬁeld camera type. We present a method that extracts a light ﬁeld from an arbitrary light ﬁeld imaging system without knowing and without modelling the internal optical elements. We calibrate the camera using a generic calibration procedure, transform the obtained set of rays into an equivalent light ﬁeld representation and ﬁnally, reconstruct a rectiﬁed light ﬁeld from the irregularly sampled data. Experimental results validate the method and demonstrate that the geometrical structure of the light ﬁeld is preserved by an adequate rectiﬁcation.
Efficient polynomial analysis of MAS spinning sidebands and application to order parameter determination in anisotropic samples

Abstract. Chemical shift tensors in 13C solid-state NMR provide valuable localized information on the chemical bonding environment in organic matter, and deviations from isotropic static-limit powder lineshapes sensitively encode dynamic-averaging or orientation effects. Studies in 13C natural abundance require magic-angle spinning (MAS), where the analysis must thus focus on spinning sidebands. We propose an alternative fitting procedure for spinning sidebands based upon a polynomial expansion that is more efficient 5 than the common numerical solution of the powder average. The approach plays out its advantages in the determination of CST principal values from spinning-sideband intensities and order parameters in non-isotropic samples, which is here illustrated on the example of stretched glassy polycarbonate.
POT: Python Optimal Transport

Optimal transport has recently been reintroduced to the machine learning community thanks in part to novel eﬃcient optimization procedures allowing for medium to large scale applications. We propose a Python toolbox that implements several key optimal transport ideas for the machine learning community. The toolbox contains implementations of a number of founding works of OT for machine learning such as Sinkhorn algorithm and Wasserstein barycenters, but also provides generic solvers that can be used for conducting novel fundamental research. This toolbox, named POT for Python Optimal Transport, is open source with an MIT license.
Mid-infrared trace-gas sensing with a quasi-continuous-wave Peltier-cooled distributed feedback quantum cascade laser

A recently developed distributed feedback quantum cascade laser (QCL) capable of thermoelectric-cooled (TEC) continuous-wave (cw) operation and emitting at ∼ 9 µ mi s u s e d to perform laser chemical sensing by tunable infrared spec- troscopy. A quasi-continuous-wave mode of operation relying on long current pulses (∼ 5H z , ∼ 50% duty cycle) is utilized rather than pure cw operation in order to extend the contin- uous frequency tuning range of the quantum cascade laser. Sulfur dioxide and ammonia were selected as convenient tar- get molecules to evaluate the performance of the cw TEC QCL based sensor. Direct absorption spectroscopy and wavelength- modulation spectroscopy were performed to demonstrate chem- ical sensing applications with this novel type of quantum cas- cade laser. For ammonia detection, a 18-ppm noise-equivalent sensitivity (1 σ) was achieved for a 1-m absorption path length and a 25-ms data-acquisition time using direct absorption spec- troscopy. The use of second-harmonic-detection wavelength- modulation spectroscopy instead of direct absorption increased the sensitivity by a factor of three, achieving a normalized noise- equivalent sensitivity of 82 ppb Hz −1/2 for a 1-m absorption path length, which corresponds to 2 × 10 −7 cm −1 Hz −1/2 .
Peripheral Macrophage-derived Exosomes promote repair after Spinal Cord Injury by inducing Local Anti-inflammatory type Microglial Polarization via Increasing Autophagy

Treatment for spinal cord injury (SCI) remains a challenge worldwide, and inflammation is a major cause of secondary injury after SCI. Peripheral macrophages (PMs) have been verified as a key factor that exert anti-inflammatory effects after SCI, but the mechanism is unidentified. As local macrophages, microglia also exert significant effects after SCI, especially polarization. Exosomes show source cell-like biological functions to target cells and have been the subject of much research in recent years. Thus, we hypothesized the PM-derived exosomes (PM-Exos) play an important role in signal transmission with local microglia and can be used therapeutic agents for SCI in a series of in vivo and in vitro studies. For the in vivo experiment, three groups of Sprague-Dawley (SD) rats subjected to spinal cord contusion injury were injected with 200 µg/ml PM-Exos, 20 µg/ml PM-Exos or PBS via the tail vein. Recovery of the rats and of spinal cord function were observed. In vitro, we investigated the potential anti-inflammatory mechanism of PM-Exos and evaluated microglial autophagy, anti-inflammatory type microglia polarization and the upstream signaling pathway. The results showed that spinal cord function and recovery were better in the PM-Exo groups than the control group. In the in vitro study, microglial autophagy levels and the expression of anti-inflammatory type microglia were higher in the experimental groups than the control group. Moreover, the expression of proteins related to the PI3K/AKT/mTOR autophagic signaling pathway was suppressed in the PM-Exo groups. PM-Exos have a beneficial effect in SCI, and activation of microglial autophagy via inhibition of the PI3K/AKT/mTOR signaling pathway, enhancing the polarization of anti-inflammatory type microglia, that may play a major role in the anti-inflammatory process.