Miracle-dz/UniFAR-FTUs huggingface dataset:
LiDAR Iris for Loop-Closure Detection

In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clouds, their similarities can be calculated as the Hamming distance of two corresponding binary signature images extracted from the two point clouds, respectively. Our LiDAR-Iris method can achieve a pose-invariant loop-closure detection at a descriptor level with the Fourier transform of the LiDAR-Iris representation if assuming a 3D (x,y,yaw) pose space, although our method can generally be applied to a 6D pose space by re-aligning point clouds with an additional IMU sensor. Experimental results on five road-scene sequences demonstrate its excellent performance in loop-closure detection.
Is AI different for SE?

What AI tools are needed for SE? Ideally, we should have simple rules that peek at data, then say "use this tool" or "use that tool". To find such a rule, we explored 120 different data sets addressing numerous problems, including bad smell detection, predicting Github issue close time, bug report analysis, defect prediction and dozens of other non-SE problems. To this data, we apply a SE-based tool that (a)~out-performs the state-of-the-art for these SE problems yet (b)~fails very badly on standard AI problems. In those results, we can find a simple rule for when to use/avoid the SE-based tool. SE data is often about infrequent issues, like the occasional defect, or the rarely exploited security violation, or the requirement that holds for one special case. But as we show, standard AI tools work best when the target is relatively more frequent. Also, we can exploit these special properties of SE, to great effect (to rapidly find better optimizations for SE tasks via a tactic called "dodging", explained in this paper). More generally, this result says we need a new kind of SE research for developing new AI tools that are more suited to SE problems.
A new inequality for maximum likelihood estimation in statistical models with latent variables

Maximum-likelihood estimation (MLE) is arguably the most important tool for statisticians, and many methods have been developed to find the MLE. We present a new inequality involving posterior distributions of a latent variable that holds under very general conditions. It is related to the EM algorithm and has a clear potential for being used in a similar fashion.
Label-free biochemical quantitative phase imaging with mid-infrared photothermal effect

Label-free optical imaging is valuable in biology and medicine with its non-destructive property and reduced optical and chemical damages. Quantitative phase (QPI) and molecular vibrational imaging (MVI) are the two most successful label-free methods, providing morphology and biochemistry, respectively, that have pioneered numerous applications along their independent technological maturity over the past few decades. However, the distinct label-free contrasts are inherently complementary and difficult to integrate due to the use of different light-matter interactions. Here, we present a unified imaging scheme that realizes simultaneous and in-situ acquisition of MV-fingerprint contrasts of single cells in the framework of QPI utilizing the mid-infrared photothermal effect. The fully label-free and robust integration of subcellular morphology and biochemistry would have important implications, especially for studying complex and fragile biological phenomena such as drug delivery, cellular diseases and stem cell development, where long-time observation of unperturbed cells are needed under low phototoxicity.
Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction

We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks. And this is implemented by combining graph CNN with an attentive pooling network to extract structural relations between drug pairs and make DDI predictions. The experiment results suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact structurally by varying colored atoms. And this may be helpful for drug design during drug discovery.
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors

We propose to study the generalization error of a learned predictor $\hat h$ in terms of that of a surrogate (potentially randomized) predictor that is coupled to $\hat h$ and designed to trade empirical risk for control of generalization error. In the case where $\hat h$ interpolates the data, it is interesting to consider theoretical surrogate classifiers that are partially derandomized or rerandomized, e.g., fit to the training data but with modified label noise. We also show that replacing $\hat h$ by its conditional distribution with respect to an arbitrary $\sigma$-field is a convenient way to derandomize. We study two examples, inspired by the work of Nagarajan and Kolter (2019) and Bartlett et al. (2019), where the learned classifier $\hat h$ interpolates the training data with high probability, has small risk, and, yet, does not belong to a nonrandom class with a tight uniform bound on two-sided generalization error. At the same time, we bound the risk of $\hat h$ in terms of surrogates constructed by conditioning and denoising, respectively, and shown to belong to nonrandom classes with uniformly small generalization error.
Theory on Covariate-Adaptive Randomized Clinical Trials: Efficiency, Selection bias and Randomization Methods

The theocratical properties of the power of the conventional testing hypotheses and the selection bias are usually unknown under covariate-adaptive randomized clinical trials. In the literature, most studies are based on simulations. In this article, we provide theoretical foundation of the power of the hypothesis testing and the selection bias under covariate-adaptive randomization based on linear models. We study the asymptotic relative loss of power of hypothesis testing to compare the treatment effects and the asymptotic selection bias. Under the covariate-adaptive randomization, (i) the hypothesis testing usually losses power, the more covariates in testing model are not incorporated in the randomization procedure, the more the power is lost; (ii) the hypothesis testing is usually more powerful than the one under complete randomization; and (iii) comparing to complete randomization, most of the popular covariate-adaptive randomization procedures in the literature, for example, Pocock and Simon's marginal procedure, stratified permuted block design, etc, produce nontrivial selection bias. A new family of covariate-adaptive randomization procedures are proposed for considering the power and selection bias simultaneously, under which, the covariate imbalances are small enough so that the power of testing the treatment effects would be asymptotically the largest and at the same time, the selection bias is asymptotically the optimal. The theocratical properties give a full picture how the power of the hypothesis testing, the selection bias of the randomization procedure, and the randomization method affect each other.
Prevalence of Metallo-β-lactamases Encoding Genes Among Pseudomonas aeruginosa Strains Isolated From the Bedridden Patients in the Intensive Care Units

Background: Pseudomonas aeruginosa is one of the most important agents causing nosocomial infections worldwide. Multidrug resistance of isolated bacteria from nosocomial infections makes it difficult and sometimes impossible to treat. Objectives: The aim of the present study was to investigate the antibiotic resistance and association between blaIMP and blaVIM genes with resistance to meropenem and imipenem among P. aeruginosa strains isolated from Iranian patients in ICU wards of Hamadan, IR Iran. Patients and Methods: In this cross-sectional study, 100 P. aeruginosa strains were isolated from bedridden patients in ICU wards of three university hospitals during five months in 2012. Isolates were confirmed at species level using biochemical tests. Their susceptibility to 18 antibiotics was assessed using Kirby-Bauer disc diffusion method. The minimum inhibitory concentration (MIC) was determined by E-test method. Single PCR was used for detecting blaVIM, and blaIMP genes. Results: The highest resistance rates of the isolates to ampicillin/sulbactam, tigecycline, and cefotaxime were 95%, 76%, and 67%, respectively. The lowest resistance rates were observed by colistin sulfate (96%), piperacillin/tazobactam (81%), amikacin (77%), aztreonam (77%), meropenem (76%), imipenem (76%), cefepime (76%), piperacillin (74%), ceftazidime (74%), gentamycin (72%), tobramycin (73%), and levofloxacin (70%) . E-test illustrated that 24% of the isolates were resistant to imipenem (MIC: 32 µg/mL). In contrary to blaVIM gene that was not detected in any isolate, blaIMP gene was detected in 4% of the isolates. No statistically significant association was observed between resistance rates to imipenem and meropenem with aforementioned genes. Conclusions: Prevalence of P. aeruginosa strains drug resistance in our areas was very high and alerting, but the other antibiotics were effective enough to be used in treatment of the infection. Overall, low resistance rate in this region was probably resulted from low prevalence of metallo-β-lactamases producing strains. Permanent monitoring of changes in P. aeruginosa resistance would help to determine national priorities for local intervention efforts.
Extreme first passage times of piecewise deterministic Markov processes

The time it takes the fastest searcher out of N ≫ 1 searchers to find a target determines the timescale of many physical, chemical, and biological processes. This time is called an extreme first passage time (FPT) and is typically much faster than the FPT of a single searcher. Extreme FPTs of diffusion have been studied for decades, but little is known for other types of stochastic processes. In this paper, we study the distribution of extreme FPTs of piecewise deterministic Markov processes (PDMPs). PDMPs are a broad class of stochastic processes that evolve deterministically between random events. Using classical extreme value theory, we prove general theorems which yield the distribution and moments of extreme FPTs in the limit of many searchers based on the short time distribution of the FPT of a single searcher. We then apply these theorems to some canonical PDMPs, including run and tumble searchers in one, two, and three space dimensions. We discuss our results in the context of some biological systems and show how our approach accounts for an unphysical property of diffusion which can be problematic for extreme statistics.
Community Detection and Matrix Completion with Two-Sided Graph Side-Information

We consider the problem of recovering communities of users and communities of items (such as movies) based on a partially observed rating matrix as well as side-information in the form of similarity graphs of the users and items. The user-to-user and item-to-item similarity graphs are generated according to the celebrated stochastic block model (SBM). We develop lower and upper bounds on the minimum expected number of observed ratings (also known as the sample complexity) needed for this recovery task. These bounds are functions of various parameters including the quality of the graph side-information which is manifested in the intra- and inter-cluster probabilities of the SBMs. We show that these bounds match for a wide range of parameters of interest, and match up to a constant factor of two for the remaining parameter regime. Our information-theoretic results quantify the benefits of the two-sided graph side-information for recovery, and further analysis reveals that the two pieces of graph side-information produce an interesting synergistic effect under certain scenarios. This means that if one observes only one of the two graphs, then the required sample complexity worsens to the case in which none of the graphs is observed. Thus both graphs are strictly needed to reduce the sample complexity.
Greed Works: An Improved Analysis of Sampling Kaczmarz-Motkzin

Stochastic iterative algorithms have gained recent interest in machine learning and signal processing for solving large-scale systems of equations, $Ax=b$. One such example is the Randomized Kaczmarz (RK) algorithm, which acts only on single rows of the matrix $A$ at a time. While RK randomly selects a row of $A$ to work with, Motzkin's Method (MM) employs a greedy row selection. Connections between the two algorithms resulted in the Sampling Kaczmarz-Motzkin (SKM) algorithm which samples a random subset of $\beta$ rows of $A$ and then greedily selects the best row of the subset. Despite their variable computational costs, all three algorithms have been proven to have the same theoretical upper bound on the convergence rate. In this work, an improved analysis of the range of random (RK) to greedy (MM) methods is presented. This analysis improves upon previous known convergence bounds for SKM, capturing the benefit of partially greedy selection schemes. This work also further generalizes previous known results, removing the theoretical assumptions that $\beta$ must be fixed at every iteration and that $A$ must have normalized rows.
A full fuzzy method for solving fuzzy fractional differential equations based on the generalized Taylor expansion

In many mathematical researches, in order to solve the fuzzy fractional differential equations we should transform these problems to crisp corresponding problems and by solving them the approximate solution can be obtained. The aim of this paper is to present a new direct method to solve the fuzzy fractional differential equations without this transformation. In this work, the fuzzy generalized Taylor expansion by using the sense of fuzzy Caputo fractional derivative for fuzzy-valued functions is presented. For solving fuzzy fractional differential equations, the fuzzy generalized Euler's method is applied. In order to show the accuracy and efficiency of presented method, the local and global truncation errors are determined. Moreover, the consistence, the convergence and the stability of the generalized Euler's method are proved in details. Eventually, the numerical examples specially in switching point case show the exibility and the capability of the presented method.
Data Collection Versus Data Estimation: A Fundamental Trade-Off in Dynamic Networks

An important question that often arises in the operation of networked systems is whether to collect the real-time data or to estimate them based on the previously collected data. Various factors should be taken into account such as how informative the data are at each time instant for state estimation, how costly and credible the collected data are, and how rapidly the data vary with time. The above question can be formulated as a dynamic decision making problem with imperfect information structure, where a decision maker wishes to find an efficient way to switch between data collection and data estimation while the quality of the estimation depends on the previously collected data (i.e., duality effect). In this paper, the evolution of the state of each node is modeled as an exchangeable Markov process for discrete features and equivariant linear system for continuous features, where the data of interest are defined in the former case as the empirical distribution of the states, and in the latter case as the weighted average of the states. When the data are collected, they may or may not be credible, according to a Bernoulli distribution. Based on a novel planning space, a Bellman equation is proposed to identify a near-optimal strategy whose computational complexity is logarithmic with respect to the inverse of the desired maximum distance from the optimal solution, and polynomial with respect to the number of nodes. A reinforcement learning algorithm is developed for the case when the model is not known exactly, and its convergence to the near-optimal solution is shown subsequently. In addition, a certainty threshold is introduced that determines when data estimation is more desirable than data collection, as the number of nodes increases. For the special case of linear dynamics, a separation principle is constructed wherein the optimal estimate is computed by a Kalman-like filter, irrespective of the probability distribution of random variables. It is shown that the complexity of finding the proposed sampling strategy, in this special case, is independent of the size of the state space and the number of nodes. Examples of a sensor network, a communication network and a social network are provided.
Cost-Sensitive Training for Autoregressive Models

Training autoregressive models to better predict under the test metric, instead of maximizing the likelihood, has been reported to be beneficial in several use cases but brings additional complications, which prevent wider adoption. In this paper, we follow the learning-to-search approach (Daum\'e III et al., 2009; Leblond et al., 2018) and investigate its several components. First, we propose a way to construct a reference policy based on an alignment between the model output and ground truth. Our reference policy is optimal when applied to the Kendall-tau distance between permutations (appear in the task of word ordering) and helps when working with the METEOR score for machine translation. Second, we observe that the learning-to-search approach benefits from choosing the costs related to the test metrics. Finally, we study the effect of different learning objectives and find that the standard KL loss only learns several high-probability tokens and can be replaced with ranking objectives that target these tokens explicitly.
Understanding the coexistence of spin-up and spin-down behaviours in long-period X-ray pulsars

Assuming wind-fed accretion magnetars in long-period X-ray pulsars, we calculated the rotational evolution of neutron stars. Our calculations considered the effects of magnetic field decay in magnetars. The results show that wind-fed accretion magnetars can evolve to long-period X-ray pulsars with a spin period much longer than 1000 s. The spin-down trend observed in 4U 2206+54-like sources is expected when young X-ray binary systems are on the way to their equilibrium period. Detailed calculations showed that the spin-down may be affected by accretion with outflows or accretion while spinning down. Due to magnetic field decay in magnetars, wind-fed accretion magnetars will have a decreasing equilibrium period for a constant mass accretion rate. For 2S 0114+65, the spin-up rate due to magnetic field decay is one order of magnitude smaller than observations. The spin-up rate of 2S 0114+65 may be attributed to the formation of a transient disc during wind accretion. The slowest X-ray pulsar AX J1910.7+0917 would be a link source between 4U 2206+54 and 2S 0114+65.
Subspace power method for symmetric tensor decomposition and generalized PCA

We introduce the Subspace Power Method (SPM) for decomposing symmetric even-order tensors. This algorithm relies on a power method iteration applied to a modified tensor, constructed from a matrix flattening. For tensors of rank up to roughly the square root of the number of tensor entries, we obtain provable guarantees for most steps of SPM, by drawing on results from algebraic geometry and dynamical systems. Numerical simulations indicate that SPM significantly outperforms state-of-the-art algorithms in terms of speed, while performing robustly for low-rank tensors subjected to additive noise. We also extend SPM to compute a certain generalized tensor decomposition, enabling generalized principal component analysis at extremely low SNR.
MITAS: A Compressed Time-Domain Audio Separation Network with Parameter Sharing

Deep learning methods have brought substantial advancements in speech separation (SS). Nevertheless, it remains challenging to deploy deep-learning-based models on edge devices. Thus, identifying an effective way to compress these large models without hurting SS performance has become an important research topic. Recently, TasNet and Conv-TasNet have been proposed. They achieved state-of-the-art results on several standardized SS tasks. Moreover, their low latency natures make them definitely suitable for real-time on-device applications. In this study, we propose two parameter-sharing schemes to lower the memory consumption on TasNet and Conv-TasNet. Accordingly, we derive a novel so-called MiTAS (Mini TasNet). Our experimental results first confirmed the robustness of our MiTAS on two types of perturbations in mixed audio. We also designed a series of ablation experiments to analyze the relation between SS performance and the amount of parameters in the model. The results show that MiTAS is able to reduce the model size by a factor of four while maintaining comparable SS performance with improved stability as compared to TasNet and Conv-TasNet. This suggests that MiTAS is more suitable for real-time low latency applications.
Continuous-Time Discounted Mirror Descent Dynamics in Monotone Concave Games

We consider concave continuous-kernel games characterized by monotonicity properties and propose discounted mirror descent type dynamics. We introduce two classes of dynamics whereby the associated mirror map is constructed based on a strongly convex or a Legendre regularizer. Depending on the properties of the regularizer, we show that these new dynamics can converge asymptotically in concave games with merely monotone (negative) pseudogradient. Furthermore, we show that when the regularizer enjoys strong convexity, the resulting dynamics can converge even in games with hypomonotone (negative) pseudogradient, which corresponds to a shortage of monotonicity.