Miracle-dz/UniFAR-FTUs huggingface dataset:
Aggregating Incomplete and Noisy Rankings

We consider the problem of learning the true ordering of a set of alternatives from largely incomplete and noisy rankings. We introduce a natural generalization of both the classical Mallows model of ranking distributions and the extensively studied model of noisy pairwise comparisons. Our selective Mallows model outputs a noisy ranking on any given subset of alternatives, based on an underlying Mallows distribution. Assuming a sequence of subsets where each pair of alternatives appears frequently enough, we obtain strong asymptotically tight upper and lower bounds on the sample complexity of learning the underlying complete ranking and the (identities and the) ranking of the top-k alternatives from selective Mallows rankings. Moreover, building on the work of (Braverman and Mossel, 2009), we show how to efficiently compute the maximum likelihood complete ranking from selective Mallows rankings.
Strategic Recourse in Linear Classification

In algorithmic decision making, recourse refers to individuals' ability to systematically reverse an unfavorable decision made by an algorithm. Meanwhile, individuals subjected to a classification mechanism are incentivized to behave strategically in order to gain a system's approval. However, not all strategic behavior necessarily leads to adverse results: through appropriate mechanism design, strategic behavior can induce genuine improvement in an individual's qualifications. In this paper, we explore how to design a classifier that achieves high accuracy while providing recourse to strategic individuals so as to incentivize them to improve their features in non-manipulative ways. We capture these dynamics using a two-stage game: first, the mechanism designer publishes a classifier, with the goal of optimizing classification accuracy and providing recourse to incentivize individuals' improvement. Then, agents respond by potentially modifying their input features in order to obtain a favorable decision from the classifier, while trying to minimize the cost of making such modifications. Under this model, we provide analytical results characterizing the equilibrium strategies for both the mechanism designer and the agents. Our empirical results show the effectiveness of our mechanism in three real-world datasets: compared to a baseline classifier that only considers individuals' strategic behavior without explicitly incentivizing improvement, our algorithm can provide recourse to a much higher fraction of individuals in the direction of improvement while maintaining relatively high prediction accuracy. We also show that our algorithm can effectively mitigate disparities caused by differences in manipulation costs. Our results provide insights for designing a machine learning model that focuses not only on the static distribution as of now, but also tries to encourage future improvement.
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset. An additional qualitative analysis revealed the interpretability introduced by the memory module.
Differentially Private ADMM Algorithms for Machine Learning

In this paper, we study efficient differentially private alternating direction methods of multipliers (ADMM) via gradient perturbation for many centralized machine learning problems. For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with the performance guarantee of <inline-formula> <tex-math notation="LaTeX">$(\epsilon,\delta)$ </tex-math></inline-formula>-differential privacy (<inline-formula> <tex-math notation="LaTeX">$(\epsilon,\delta)$ </tex-math></inline-formula>-DP). From the viewpoint of theoretical analysis, we use the Gaussian mechanism and the conversion relationship between Rényi Differential Privacy (RDP) and DP to perform a comprehensive privacy analysis for our algorithm. Then we establish a new criterion to prove the convergence of the proposed algorithms including DP-ADMM. We also give the utility analysis of our DP-ADMM. Moreover, we propose a new accelerated DP-ADMM (DP-AccADMM) algorithm with the Nesterov’s acceleration technique. Finally, we conduct numerical experiments on many real-world datasets to show the privacy-utility tradeoff of the two proposed algorithms, and all the comparative analysis shows that DP-AccADMM converges faster and has a better utility than DP-ADMM, when the privacy budget <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> is larger than a threshold.
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization

This paper proposes a new paradigm for handling far-field multi-speaker data in an end-to-end (E2E) neural network manner, called directional automatic speech recognition (D-ASR), which explicitly models source speaker locations. In D-ASR, the azimuth angle of the sources with respect to the microphone array is defined as a latent variable. This angle controls the quality of separation, which in turn determines the ASR performance. All three functionalities of D-ASR: localization, separation, and recognition are connected as a single differentiable neural network and trained solely based on ASR error minimization objectives. The advantages of D-ASR over existing methods are threefold: (1) it provides explicit speaker locations, (2) it improves the explainability factor, and (3) it achieves better ASR performance as the process is more streamlined. In addition, D-ASR does not require explicit direction of arrival (DOA) supervision like existing data-driven localization models, which makes it more appropriate for realistic data. For the case of two source mixtures, D-ASR achieves an average DOA prediction error of less than three degrees. It also outperforms a strong far-field multi-speaker end-to-end system in both separation quality and ASR performance.
Adapting Neural Networks for Uplift Models

Uplift is a particular case of individual treatment effect modeling. Such models deal with cause-and-effect inference for a specific factor, such as a marketing intervention. In practice, these models are built on customer data who purchased products or services to improve product marketing. Uplift is estimated using either i) conditional mean regression or ii) transformed outcome regression. Most existing approaches are adaptations of classification and regression trees for the uplift case. However, in practice, these conventional approaches are prone to overfitting. Here we propose a new method using neural networks. This representation allows to jointly optimize the difference in conditional means and the transformed outcome losses. As a consequence, the model not only estimates the uplift, but also ensures consistency in predicting the outcome. We focus on fully randomized experiments, which is the case of our data. We show our proposed method improves the state-of-the-art on synthetic and real data.
Estimating State of Charge for xEV Batteries Using 1D Convolutional Neural Networks and Transfer Learning

In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability.
Method of the coherence evaluation of Ukrainian text

Due to the growing role of the SEO technologies, it is necessary to perform an automated analysis of the article's quality. Such approach helps both to return the most intelligible pages for the user's query and to raise the web sites positions to the top of query results. An automated assessment of a coherence is a part of the complex analysis of the text. In this article, main methods for text coherence measurements for Ukrainian language are analyzed. Expediency of using the semantic similarity graph method in comparison with other methods are explained. It is suggested the improvement of that method by the pre-training of the neural network for vector representations of sentences. Experimental examination of the original method and its modifications is made. Training and examination procedures are made on the corpus of Ukrainian texts, which were previously retrieved from abstracts and full texts of Ukrainian scientific articles. The testing procedure is implemented by performing of two typical tasks for the text coherence assessment: document discrimination task and insertion task. Accordingly to the analysis it is defined the most effective combination of method's modification and its parameter for the measurement of the text coherence.
Can adequate analgesia be achieved in patients with cirrhosis without precipitating hepatic encephalopathy? A prospective study

Aim of the study Analgesic use in patients with liver cirrhosis can be associated with increased morbidity and mortality and presents clinicians with a significant and challenging management issue. We evaluated the efficacy of opiate analgesia in patients with cirrhosis, while closely monitoring the side effect profile. Material and methods This prospective cohort pilot study compared inpatients with cirrhosis who required regular opiate analgesia to non-cirrhotic patients requiring opiates and patients with cirrhosis who did not require opiates. Participants completed daily surveys to assess for analgesic efficacy and encephalopathy. Results Fifty-two patients were initially recruited, of whom 50 patients were analysed in three groups (40 male, 10 female, median age 52 years). These included 13 cirrhotic patients (69% Child-Pugh B or C) requiring regular opiate analgesia, 18 cirrhotic patients (67% Child-Pugh B or C) not receiving regular opiate analgesia, and 19 non-cirrhotic controls. Two patients were excluded due to past stroke and acquired brain injury. All cirrhotic patients received regular lactulose. There was no statistical difference in the adequacy of analgesia between the three groups. The modified orientation log score for encephalopathy remained in the normal range in all but two cirrhotic patients receiving regular opiate analgesia. Conclusions Effective pain control is achievable with opiate analgesia in most patients with advanced liver disease without precipitating hepatic encephalopathy.
Mitigating Write Disturbance Errors of Phase-Change Memory as In-Module Approach

With the growing demand for technology scaling and storage capacity in server systems to support high-performance computing, phase-change memory (PCM) has garnered attention as the next-generation non-volatile memory to satisfy these requirements. However, write disturbance error (WDE) appears as a serious reliability problem preventing PCM from general commercialization. WDE occurs on the neighboring cells of a written cell due to heat dissipation. Previous studies for the prevention of WDEs are based on the write cache or verify-n-correction while they often suffer from significant area overhead and performance degradation, making it unsuitable for high-performance computing. Therefore, an on-demand correction is required to minimize the performance overhead. In this paper, an in-module disturbance barrier (IMDB) mitigating WDEs is proposed. IMDB includes two sets of SRAMs into two levels and evicts entries with a policy that leverages the characteristics of WDE. In this work, the comparator dedicated to the replacement policy requires significant hardware resources and latency. Thus, an approximate comparator is designed to reduce the area and latency considerably. Furthermore, the exploration of architecture parameters is conducted to obtain cost-effective design. The proposed work significantly reduces WDEs without a noticeable speed degradation and additional energy consumption compared to previous methods.
EEG-Based Brain–Computer Interfaces are Vulnerable to Backdoor Attacks

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it.
Technical Report: A New Hopping Controller for Highly Dynamical Bipeds

We present angle of attack control, a novel control strategy for a hip energized Penn Jerboa. The energetic losses from damping are counteracted by aligning most of the velocity at touchdown in the radial direction and the fore-aft velocity is controlled by using the hip torque to control to a target angular momentum. The control strategy results in highly asymmetric leg angle trajectories, thus avoiding the traction issues that plague hip actuated SLIP. Using a series of assumptions we find an analytical expression for the fixed points of an approximation to the hopping return map relating the design parameters to steady state gait performance. The hardware robot demonstrates stable locomotion with speeds ranging from 0.4 m/s to 2.5 m/s (2 leg lengths/s to 12.5 leg lengths/s) and heights ranging from 0.21 m to 0.27 m (1.05 leg lengths to 1.35 leg lengths). The performance of the empirical trials is well approximated by the analytical predictions.
Multi-task learning predicts drug combination synergy in cells and in the clinic

Combination therapies for various cancers have been shown to increase efficacy, lower toxicity, and circumvent resistance. However, despite the promise of combinatorial therapies, the biological mechanisms behind drug synergy have not been fully characterized, and the systematic testing of all possible synergistic therapies is experimentally infeasible due to the sheer volume of potential combinations. Here we apply a novel big data approach in the evaluation and prediction of drug synergy by using the recently released NCI-ALMANAC. We found that each traditional drug synergy metric (Bliss, Loewe, ZIP, HSA, ALMANAC Score) identified unique synergistic drug pairs with distinct underlying joint mechanisms of action. Leveraging these findings, we developed a suite of context specific drug synergy predictive models for each distinct synergy type and achieved significant predictive performance (AUC = 0.89-0.953). Furthermore, our models accurately identified clinically tested drug pairs and characterized the clinical relevance of each drug synergy metric, with Bliss Independence capturing clinically tested combinations best. Our findings demonstrate that drug synergy can be obtained from numerous unique joint mechanisms of action, captured by different synergy metrics. Additionally, we show that drug synergy, of all kinds, can be predicted with high degrees of accuracy with significant clinical potential. This breakthrough understanding of joint mechanisms of action will allow for the design of rational combinatorial therapeutics on a large scale, across various cancer types.
Context-based Image Segment Labeling (CBISL)

Working with images, one often faces problems with incomplete or unclear information. Image inpainting can be used to restore missing image regions but focuses, however, on low-level image features such as pixel intensity, pixel gradient orientation, and color. This paper aims to recover semantic image features (objects and positions) in images. Based on published gated PixelCNNs, we demonstrate a new approach referred to as quadro-directional PixelCNN to recover missing objects and return probable positions for objects based on the context. We call this approach context-based image segment labeling (CBISL). The results suggest that our four-directional model outperforms one-directional models (gated PixelCNN) and returns a human-comparable performance.
Millimeter-Wave Antenna Array Diagnosis with Partial Channel State Information

Large antenna arrays enable directional precoding for Millimeter-Wave (mmWave) systems and provide sufficient link budget to combat the high path-loss at these frequencies. Due to atmospheric conditions and hardware malfunction, outdoor mmWave antenna arrays are prone to blockages or complete failures. This results in a modified array geometry, distorted far-field radiation pattern, and system performance degradation. Recent remote array diagnostic techniques have emerged as an effective way to detect defective antenna elements in large antenna arrays with few diagnostic measurements. These techniques, however, are dependent on full and perfect channel state information (CSI), which can be challenging to acquire in the presence of antenna faults. This paper proposes a new remote array diagnosis technique that relaxes the need for full CSI and only requires knowledge of the incident angles-of-arrival, i.e. partial channel knowledge. Numerical results demonstrate the effectiveness of the proposed technique and show that fault detection can be achieved with comparable number of diagnostic measurements required by diagnostic techniques based on full channel knowledge. In presence of channel estimation errors, the proposed technique is shown to out-perform recently proposed array diagnostic techniques.
Beelines: Evaluating Motion Prediction Impact on Self-Driving Safety and Comfort

The commonly used metrics for motion prediction do not correlate well with a self-driving vehicle's system-level performance. The most common metrics are average displacement error (ADE) and final displacement error (FDE), which omit many features, making them poor self-driving performance indicators. Since high-fidelity simulations and track testing can be resource-intensive, the use of prediction metrics better correlated with full-system behavior allows for swifter iteration cycles. In this paper, we offer a conceptual framework for prediction evaluation highly specific to self-driving. We propose two complementary metrics that quantify the effects of motion prediction on safety (related to recall) and comfort (related to precision). Using a simulator, we demonstrate that our safety metric has a significantly better signal-to-noise ratio than displacement error in identifying unsafe events.
Spectral splitting and concentration of broadband light using neural networks

Compact photonic elements that control both the diffraction and interference of light offer superior performance at ultra-compact dimensions. Unlike conventional optical structures, these diffractive optical elements can provide simultaneous control of spectral and spatial profile of light. However, the inverse-design of such a diffractive optical element is time-consuming with current algorithms, and the designs generally lack experimental validation. Here, we develop a neural network model to experimentally design and validate SpliCons; a special type of diffractive optical element that can achieve simultaneous concentration and spectral splitting of broadband light. We use neural networks to exploit nonlinear operations that result from wavefront reconstruction through a phase plate. Our results show that the neural network model yields enhanced spectral splitting performance for phase plates with quantitative assessment compared to phase plates that are optimized via local search optimization algorithm. The capabilities of the phase plates optimized via neural network are experimentally validated by comparing the intensity distribution at the output plane. Once the neural networks are trained, we manage to design SpliCons with 96.8% accuracy within 2 seconds, which is orders of magnitude faster than iterative search algorithms. We openly share the fast and efficient framework that we develop in order to contribute to the design and implementation of diffractive optical elements that can lead to transformative effects in microscopy, spectroscopy, and solar energy applications.
Numerical solution of the wave propagation problem in a plate

In this work, we use the phase velocity method in combination with finite element method to compute the dispersion curve for phase velocity of an ultrasonic pulse traveling in a thin isotropic plate. This method is based on the numerical solution of the wave propagation equations for several selected frequencies. To solve these equations, a second order difference scheme is used to discretize the temporal variable, while spatial variables are discretized using the finite element method. The variational formulation of the problem corresponding to a fixed value of time is obtained and the existence and uniqueness of the solution is proved. A priori error estimates in the energy norm and in the [Formula: see text] norm are also obtained. The open software FreeFem++ is used with quadratic triangular elements to compute the displacements. Numerical experiments show that the velocities computed from the approximated displacements for different frequency values are in good agreement with analytical dispersion curve. This confirms that the proposed symbiosis between finite element and phase velocity method is suitable for computing dispersion curves in more general wave propagation problems, where the geometry is complex and the material is anisotropic.