Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Algorithms for Minimizing Compositions of Convex Functions and Random Functions and Its Applications in Network Revenue Management

In this paper, we study a class of nonconvex stochastic optimization in the form of $\min_{x\in\mathcal{X}} F(x):=\mathbb{E}_\xi [f(\phi(x,\xi))]$, where the objective function $F$ is a composition of a convex function $f$ and a random function $\phi$. Leveraging an (implicit) convex reformulation via a variable transformation $u=\mathbb{E}[\phi(x,\xi)]$, we develop stochastic gradient-based algorithms and establish their sample and gradient complexities for achieving an $\epsilon$-global optimal solution. Interestingly, our proposed Mirror Stochastic Gradient (MSG) method operates only in the original $x$-space using gradient estimators of the original nonconvex objective $F$ and achieves $\tilde{\mathcal{O}}(\epsilon^{-2})$ sample and gradient complexities, which matches the lower bounds for solving stochastic convex optimization problems. Under booking limits control, we formulate the air-cargo network revenue management (NRM) problem with random two-dimensional capacity, random consumption, and routing flexibility as a special case of the stochastic nonconvex optimization, where the random function $\phi(x,\xi)=x\wedge\xi$, i.e., the random demand $\xi$ truncates the booking limit decision $x$. Extensive numerical experiments demonstrate the superior performance of our proposed MSG algorithm for booking limit control with higher revenue and lower computation cost than state-of-the-art bid-price-based control policies, especially when the variance of random capacity is large. KEYWORDS: stochastic nonconvex optimization, hidden convexity, air-cargo network revenue management, gradient-based algorithms
Rates of estimation for high-dimensional multi-reference alignment

We study the continuous multi-reference alignment model of estimating a periodic function on the circle from noisy and circularly-rotated observations. Motivated by analogous high-dimensional problems that arise in cryo-electron microscopy, we establish minimax rates for estimating generic signals that are explicit in the dimension $K$. In a high-noise regime with noise variance $\sigma^2 \gtrsim K$, for signals with Fourier coefficients of roughly uniform magnitude, the rate scales as $\sigma^6$ and has no further dependence on the dimension. This rate is achieved by a bispectrum inversion procedure, and our analyses provide new stability bounds for bispectrum inversion that may be of independent interest. In a low-noise regime where $\sigma^2 \lesssim K/\log K$, the rate scales instead as $K\sigma^2$, and we establish this rate by a sharp analysis of the maximum likelihood estimator that marginalizes over latent rotations. A complementary lower bound that interpolates between these two regimes is obtained using Assouad's hypercube lemma. We extend these analyses also to signals whose Fourier coefficients have a slow power law decay.
Deep Sequence Modeling for Anomalous ISP Traffic Prediction

Internet traffic in the real world is susceptible to various external and internal factors which may abruptly change the normal traffic flow. Those unexpected changes are considered outliers in traffic. However, deep sequence models have been used to predict complex IP traffic, but their comparative performance for anomalous traffic has not been studied extensively. In this paper, we investigated and evaluated the performance of different deep sequence models for anomalous traffic prediction. Several deep sequences models were implemented to predict real traffic without and with outliers and show the significance of outlier detection in real-world traffic prediction. First, two different outlier detection techniques, such as the Three-Sigma rule and Isolation Forest, were applied to identify the anomaly. Second, we adjusted those abnormal data points using the Backward Filling technique before training the model. Finally, the performance of different models was compared for abnormal and adjusted traffic. LSTM_Encoder_Decoder (LSTM_En_De) is the best prediction model in our experiment, reducing the deviation between actual and predicted traffic by more than 11% after adjusting the outliers. All other models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM_En_De with Attention layer (LSTM_En_De_Atn), Gated Recurrent Unit (GRU), show better prediction after replacing the outliers and decreasing prediction error by more than 29%, 24%, 19%, and 10% respectively. Our experimental results indicate that the outliers in the data can significantly impact the quality of the prediction. Thus, outlier detection and mitigation assist the deep sequence model in learning the general trend and making better predictions.
An Empirical Study on Maintainable Method Size in Java

Code metrics have been widely used to estimate software maintenance effort. Metrics have generally been used to guide developer effort to reduce or avoid future maintenance burdens. Size is the simplest and most widely deployed metric. The size metric is pervasive because size correlates with many other common metrics (e.g., McCabe complexity, readability, etc.). Given the ease of computing a method's size, and the ubiquity of these metrics in industrial settings, it is surprising that no systematic study has been performed to provide developers with meaningful method size guidelines with respect to future maintenance effort. In this paper we examine the evolution of ∼785K Java methods and show that developers should strive to keep their Java methods under 24 lines in length. Additionally, we show that decomposing larger methods to smaller methods also decreases overall maintenance efforts. Taken together, these findings provide empirical guidelines to help developers design their systems in a way that can reduce future maintenance.
Cross-lingual Word Embeddings in Hyperbolic Space

Cross-lingual word embeddings can be applied to several natural language processing applications across multiple languages. Unlike prior works that use word embeddings based on the Euclidean space, this short paper presents a simple and effective cross-lingual Word2Vec model that adapts to the Poincar\'e ball model of hyperbolic space to learn unsupervised cross-lingual word representations from a German-English parallel corpus. It has been shown that hyperbolic embeddings can capture and preserve hierarchical relationships. We evaluate the model on both hypernymy and analogy tasks. The proposed model achieves comparable performance with the vanilla Word2Vec model on the cross-lingual analogy task, the hypernymy task shows that the cross-lingual Poincar\'e Word2Vec model can capture latent hierarchical structure from free text across languages, which are absent from the Euclidean-based Word2Vec representations. Our results show that by preserving the latent hierarchical information, hyperbolic spaces can offer better representations for cross-lingual embeddings.
Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing

We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Developing practical algorithms that can estimate price elasticities robustly, especially when information about no purchases (losses) is not available, to drive such automated pricing systems is a challenge faced by many industries. Based on the Poisson semi-parametric approach, we construct a flexible yet interpretable demand model where the price related part is parametric while the remaining (nuisance) part of the model is non-parametric and can be modeled via sophisticated machine learning (ML) techniques. The estimation of price-sensitivity parameters of this model via direct one-stage regression techniques may lead to biased estimates due to regularization. To address this concern, we propose a two-stage estimation methodology which makes the estimation of the price-sensitivity parameters robust to biases in the estimators of the nuisance parameters of the model. In the first-stage we construct estimators of observed purchases and prices given the feature vector using sophisticated ML estimators such as deep neural networks. Utilizing the estimators from the first-stage, in the second-stage we leverage a Bayesian dynamic generalized linear model to estimate the price-sensitivity parameters. We test the performance of the proposed estimation schemes on simulated and real sales transaction data from the Airline industry. Our numerical studies demonstrate that our proposed two-stage approach reduces the estimation error in price-sensitivity parameters from 25\% to 4\% in realistic simulation settings. The two-stage estimation techniques proposed in this work allows practitioners to leverage modern ML techniques to robustly estimate price-sensitivities while still maintaining interpretability and allowing ease of validation of its various constituent parts.
Radio Spectral Energy Distributions for Single Massive Star Winds with Free–Free and Synchrotron Emission

The mass-loss rates from single massive stars are high enough to form radio photospheres at large distances from the stellar surface, where the wind is optically thick to (thermal) free–free opacity. Here we calculate the far-infrared, millimeter, and radio band spectral energy distributions (SEDs) that can result from the combination of free–free processes and synchrotron emission, to explore the conditions for nonthermal SEDs. Simplifying assumptions are adopted in terms of scaling relations for the magnetic field strength and the spatial distribution of relativistic electrons. The wind is assumed to be spherically symmetric, and we consider the effect of Razin suppression on the synchrotron emission. Under these conditions, long-wavelength SEDs with synchrotron emission can be either more steep or more shallow than the canonical asymptotic power-law SED from a nonmagnetic wind. When nonthermal emission is present, the resultant SED shape is generally not a power law; however, the variation in the slope can change slowly with wavelength. Consequently, over a limited range of wavelengths, the SED can masquerade as approximately a power law. While most observed nonthermal long-wavelength spectra are associated with binarity, synchrotron emission can have only a mild influence on single-star SEDs, requiring finer levels of wavelength sampling for the detection of the effect.
Washout control of a cyclic vehicular traffic flow

Abstract In this paper, we consider a decentralized control problem for suppressing the traffic jam in a cyclic traffic ow. In recent years, to explain the mechanism that causes the traffic jam, several experiments have been done for multiple vehicles on the circle. The traffic jam can be explained by the so-called optimal velocity model, the optimal velocity function which is a nonlinear function of the headway of the preceding vehicle and describes driver's characteristics. In this paper, we apply washout control to suppress the traffic jam in a cyclic traffic ow not to disturb driver's characteristics. Then, we show a method to select parameters to keep stability of the closed-loop system. We find that our proposed method for selecting parameters is better than the conservative method using the small gain theorem. In addition, we illustrate the effectiveness with several simulations.
ImAiR: Airwriting Recognition Framework Using Image Representation of IMU Signals

The problem of airwriting recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in human–computer interaction (HCI). Recognition of in-air trajectories using such wrist-worn devices is limited in literature and forms the basis of the current work. In this letter, we propose an airwriting recognition framework by first encoding the time-series data obtained from a wearable inertial measurement unit (IMU) on the wrist as images and then utilizing deep learning-based models for identifying the written alphabets. The signals recorded from 3-axis accelerometer and gyroscope in IMU are encoded as images using different techniques such as self-similarity matrix (SSM), Gramian angular field (GAF), and Markov transition field (MTF) to form two sets of 3-channel images. These are then fed to two separate classification models, and letter prediction is made based on an average of the class conditional probabilities obtained from the two models. Several standard model architectures for image classification such as variants of ResNet, DenseNet, VGGNet, AlexNet, and GoogleNet have been utilized. Experiments performed on two publicly available datasets demonstrate the efficacy of the proposed strategy. The code for our implementation will be made available at https://github.com/ayushayt/ImAiR.
Optimal Network Charge for Peer-to-Peer Energy Trading: A Grid Perspective

Peer-to-peer (P2P) energy trading is a promising market scheme to accommodate the increasing distributed energy resources (DERs). However, how P2P to be integrated into the existing power systems remains to be investigated. In this paper, we apply network charge as a means for the grid operator to attribute transmission loss and ensure network constraints for empowering P2P transaction. The interaction between the grid operator and the prosumers is modeled as a Stackelberg game which yields a bi-level optimization problem. We prove that the Stackelberg game admits an equilibrium network charge price. Besides, we propose a method to obtain the network charge price by converting the bi-level optimization into a single-level mixed-integer quadratic programming (MIQP) that can handle a reasonable scale of prosumers efficiently. Simulations on the IEEE bus systems show that the network charge mechanism is favorable as it can benefit both the grid operator while securing the prosumers’ profit, and achieves near-optimal social welfare. Moreover, the results show that the presence of energy storage will make the prosumers more sensitive to the network charge price changes and impact the P2P market.
Nonparametric inference under a monotone hazard ratio order

The ratio of the hazard functions of two populations or two strata of a single population plays an important role in time-to-event analysis. Cox regression is commonly used to estimate the hazard ratio under the assumption that it is constant in time, which is known as the proportional hazards assumption. However, this assumption is often violated in practice, and when it is violated, the parameter estimated by Cox regression is difficult to interpret. The hazard ratio can be estimated in a nonparametric manner using smoothing, but smoothing-based estimators are sensitive to the selection of tuning parameters, and it is often difficult to perform valid inference with such estimators. In some cases, it is known that the hazard ratio function is monotone. In this article, we demonstrate that monotonicity of the hazard ratio function defines an invariant stochastic order, and we study the properties of this order. Furthermore, we introduce an estimator of the hazard ratio function under a monotonicity constraint. We demonstrate that our estimator converges in distribution to a mean-zero limit, and we use this result to construct asymptotically valid confidence intervals. Finally, we conduct numerical studies to assess the finite-sample behavior of our estimator, and we use our methods to estimate the hazard ratio of progression-free survival in pulmonary adenocarcinoma patients treated with Gefitinib or carboplatin-paclitaxel.
Themes of Revenge: Automatic Identification of Vengeful Content in Textual Data

Revenge is a powerful motivating force reported to underlie the behavior of various solo perpetrators, from school shooters to right wing terrorists. In this paper, we develop an automated methodology for identifying vengeful themes in textual data. Testing the model on four datasets (vengeful texts from social media, school shooters, Right Wing terrorist and Islamic terrorists), we present promising results, even when the methodology is tested on extremely imbalanced datasets. The paper not only presents a simple and powerful methodology that may be used for the screening of solo perpetrators but also validate the simple theoretical model of revenge. (another term for recall) against the false positive rate as the classification threshold is allowed to vary from 0 to 1; the AUC is the value obtained by integrating over the curve. Each classifier used represents a different ‘classification rule’ used to assign the predicted probability of observations that are compared against the classification threshold. As our goal is to optimize accurate classification of vengeful texts, we test a variety of different classifiers to determine the most successful. Each model is estimated with ten-fold cross-validation; this process involves partitioning the dataset into ten equally sized subsamples, where each tenth serves once as the test dataset on which the classifier trained on the remainder of the dataset is evaluated. ML algorithms we tested include: k-nearest neighbors, a naïve classification rule that assigns observations to the same class the majority class among the n closest observations in feature space; random forest, an ensemble of decision trees; AdaBoost, an ensemble method that aggregates results from several weak learners to produce a strong learner; linear discriminate analysis, a linear combination method whose process involves dimensionality reduction; another ensemble decision tree algorithm.
Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

With the advent of digital therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship between DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.
Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification

In this paper, we ask the research question of whether all the datasets in the benchmark are necessary. We approach this by first characterizing the distinguishability of datasets when comparing different systems. Experiments on 9 datasets and 36 systems show that several existing benchmark datasets contribute little to discriminating top-scoring systems, while those less used datasets exhibit impressive discriminative power. We further, taking the text classification task as a case study, investigate the possibility of predicting dataset discrimination based on its properties (e.g., average sentence length). Our preliminary experiments promisingly show that given a sufficient number of training experimental records, a meaningful predictor can be learned to estimate dataset discrimination over unseen datasets. We released all datasets with features explored in this work on DataLab.
A Broadband High-Power Five-Way Power Divider/Combiner

ABSTRACT A high-power five-way power divider/combiner utilizing strip-line architecture in the X-band is proposed. In this work, broadband non-uniform strip-line couplers are connected in a tree structure. A power-handling capability of 100 watts is verified by full-wave electromagnetic simulation. The simulation results indicate that the power divider/combiner exhibits a good amplitude, phase balance characteristics, and isolation between output ports, as well as a return loss at input and output ports better than 12 dB, while insertion loss is about 1.1 dB. A prototype of the power divider/combiner is fabricated and also measured. The measured results show very good agreement with those obtained by simulation.
Vocal changes in a zebra finch model of Parkinson’s disease characterized by alpha-synuclein overexpression in the song-dedicated anterior forebrain pathway

Deterioration in the quality of a person’s voice and speech is an early marker of Parkinson’s disease (PD). In humans, the neural circuit that supports vocal motor control consists of a cortico-basal ganglia-thalamo-cortico loop. The basal ganglia regions, striatum and globus pallidus, in this loop play a role in modulating the acoustic features of vocal behavior such as loudness, pitch, and articulatory rate. In PD, this area is implicated in pathogenesis. In animal models of PD, the accumulation of toxic aggregates containing the neuronal protein alpha-synuclein (αsyn) in the midbrain and striatum result in limb and vocal motor impairments. It has been challenging to study vocal impairments given the lack of well-defined cortico-basal ganglia circuitry for vocalization in rodent models. Furthermore, whether deterioration of voice quality early in PD is a direct result of αsyn-induced neuropathology is not yet known. Here, we take advantage of the well-characterized vocal circuits of the adult male zebra finch songbird to experimentally target a song-dedicated pathway, the anterior forebrain pathway, using an adeno-associated virus expressing the human wild-type αsyn gene, SNCA. We found that overexpression of αsyn in this pathway coincides with higher levels of insoluble, monomeric αsyn compared to control finches. Impairments in song production were also detected along with shorter and poorer quality syllables, which are the most basic unit of song. These vocal changes are similar to the vocal abnormalities observed in individuals with PD.
Protective Effects of Grapefruit Essential Oil against Staphylococcus Aureus‐Induced Inflammation and Cell Damage in Human Epidermal Keratinocytes

Staphylococcus aureus (S. aureus) is a common skin pathogenic bacterium, over‐colonization can induce skin infections, while its metabolites can also produce irritation to the skin, often accompanied by eczema dermatitis, specific dermatitis and other skin diseases. Grapefruit essential oil is extracted from the fruit of grapefruit (Citrus maxima (Burm) Merr. ), a citrus plant that is rich in flavonoids, phenolic acids and high flavanones. Due to its good odor and biological activity such as anti‐inflammatory, antibacterial, etc., grapefruit essential oil has been widely used as an additive in food. To evaluate the potential application of grapefruit essential oil as raw materials in cosmetics products and health foods, we developed a cell damage model of skin inflammation stimulated by S. aureus metabolites. Compared to that of lime essential oil, an internal control, we found that grapefruit essential oil could significantly promote HaCaT cells proliferation, reduce reactive oxygen species (ROS) production induced by S. aureus metabolites, inhibit the upregulated expression of IL‐1 and COX‐2. In the 3D epidermal model, grapefruit essential oil could recover the decreased LOR and FLG contents caused by S. aureus metabolites. These results demonstrated pharmacological evidence for the anti‐inflammatory effect of grapefruit essential oil, suggesting a potential application of grapefruit essential oil as cosmetic raw materials for repair and alleviating of skin inflammation caused by S. aureus.