Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Cell counting with Neural Arithmetic Logic Units

The big problem for neural network models which are trained to count instances is that whenever test range goes high training range generalization error increases i.e. they are not good generalizers outside training range. Consider the case of automating cell counting process where more dense images with higher cell counts are commonly encountered as compared to images used in training data. By making better predictions for higher ranges of cell count we are aiming to create better generalization systems for cell counting. With architecture proposal of neural arithmetic logic units (NALU) for arithmetic operations, task of counting has become feasible for higher numeric ranges which were not included in training data with better accuracy. As a part of our study we used these units and different other activation functions for learning cell counting task with two different architectures namely Fully Convolutional Regression Network and U-Net. These numerically biased units are added in the form of residual concatenated layers to original architectures and a comparative experimental study is done with these newly proposed changes . This comparative study is described in terms of optimizing regression loss problem from these models trained with extensive data augmentation techniques. We were able to achieve better results in our experiments of cell counting tasks with introduction of these numerically biased units to already existing architectures in the form of residual layer concatenation connections. Our results confirm that above stated numerically biased units does help models to learn numeric quantities for better generalization results.
AiR-ViBeR: Exfiltrating Data from Air-Gapped Computers via Covert Surface ViBrAtIoNs

Air-gap covert channels are special types of covert communication channels that enable attackers to exfiltrate data from isolated, network-less computers. Various types of air-gap covert channels have been demonstrated over the years, including electromagnetic, magnetic, acoustic, optical, and thermal. In this paper, we introduce a new type of vibrational (seismic) covert channel. We observe that computers vibrate at a frequency correlated to the rotation speed of their internal fans. These inaudible vibrations affect the entire structure on which the computer is placed. Our method is based on malware's capability of controlling the vibrations generated by a computer, by regulating its internal fan speeds. We show that the malware-generated covert vibrations can be sensed by nearby smartphones via the integrated, sensitive \textit{accelerometers}. Notably, the accelerometer sensors in smartphones can be accessed by any app without requiring the user permissions, which make this attack highly evasive. We implemented AiR-ViBeR, malware that encodes binary information, and modulate it over a low frequency vibrational carrier. The data is then decoded by malicious application on a smartphone placed on the same surface (e.g., on a desk). We discuss the attack model, provide technical background, and present the implementation details and evaluation results. Our results show that using AiR-ViBeR, data can be exfiltrated from air-gapped computer to a nearby smartphone on the same table, or even an adjacent table, via vibrations. Finally, we propose a set of countermeasures for this new type of attack.
Materials Requirements of High-Speed and Low-Power Spin-Orbit-Torque Magnetic Random-Access Memory

As spin-orbit-torque magnetic random-access memory (SOT-MRAM) is gathering great interest as the next-generation low-power and high-speed on-chip cache memory applications, it is critical to analyze the magnetic tunnel junction (MTJ) properties needed to achieve sub-ns, and ~fJ write operation when integrated with CMOS access transistors. In this paper, a 2T-1MTJ cell-level modeling framework for in-plane type Y SOT-MRAM suggests that high spin Hall conductivity and moderate SOT material sheet resistance are preferred. We benchmark write energy and speed performances of type Y SOT cells based on various SOT materials experimentally reported in the literature, including heavy metals, topological insulators and semimetals. We then carry out detailed benchmarking of SOT material Pt, $\beta $ -W, and BixSe(1-x) with different thickness and resistivity. We further discuss how our 2T-1MTJ model can be expanded to analyze other variations of SOT-MRAM, including perpendicular (type Z) and type X SOT-MRAM, two-terminal SOT-MRAM, as well as spin-transfer-torque (STT) and voltage-controlled magnetic anisotropy (VCMA)-assisted SOT-MRAM. This work will provide essential guidelines for SOT-MRAM materials, devices, and circuits research in the future.
Universal approximation on the hypersphere

Abstract The approximation properties of finite mixtures of location-scale distributions on Euclidean space have been well studied. It has been shown that mixtures of location-scale distributions can approximate arbitrary probability density function up to any desired level of accuracy provided the number of mixture components is sufficiently large. However, analogous results are not available for probability density functions defined on the unit hypersphere. The von-Mises-Fisher distribution, defined on the unit hypersphere Sm in plays the central role in directional statistics. We prove that any continuous probability density function on Sm can be approximated to arbitrary degrees of accuracy in sup norm by a finite mixture of von-Mises-Fisher distributions. Our proof strategy and result are also useful in studying the approximation properties of other finite mixtures of directional distributions.
The R Package stagedtrees for Structural Learning of Stratified Staged Trees

stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data. Score-based and distance-based algorithms are implemented, as well as various functionalities to plot the models and perform inference. The capabilities of stagedtrees are illustrated using mainly two datasets both included in the package or bundled in R.
The two-parameter Debye model

When analyzing thermodynamic and kinetic properties of crystals whose anisotropy is not large and the considered effects do not relate to the existence of singled-out directions in crystals, one may use a more simple model of an isotropic medium with a good accuracy, after having chosen its parameters in an optimal way. Based on the quantum mechanical description it is shown that the method of approximation of the moduli of elasticity of a crystal by the model of an isotropic medium, proposed earlier in [2], follows from the requirement of the minimal difference between the free energies of a crystal and an approximating isotropic medium. The two-parametric Debye model is formulated, which, in contrast to the standard model where the average speed of phonons is introduced, takes into account the existence in an isotropic medium of both longitudinal and transverse phonons. The proposed model contains, except the Debye energy, an additional dimensionless parameter and, consequently, the law of corresponding states for the heat capacity being characteristic of the standard model does not hold. With taking account of the two phonon branches the structure of the density of phonon states proves to be more complex as compared to the standard model and has a singularity that resembles Van Hove singularities in real crystals. As an example, an application of the two-parametric Debye theory to such crystals of the cubic system as tungsten, copper, lead is considered. It is shown that the calculation of the low-temperature heat capacity of these crystals by means of the approximated moduli of elasticity within the framework of the two-parametric model leads to a considerably better agreement with experiment than in the case of the standard Debye model.
Estimating state occupation and transition probabilities in non-Markov multi-state models subject to both random left-truncation and right-censoring

The Aalen-Johansen estimator generalizes the Kaplan-Meier estimator for independently left-truncated and right-censored survival data to estimating the transition probability matrix of a time-inhomogeneous Markov model with finite state space. Such multi-state models have a wide range of applications for modelling complex courses of a disease over the course of time, but the Markov assumption may often be in doubt. If censoring is entirely unrelated to the multi-state data, it has been noted that the Aalen-Johansen estimator, standardized by the initial empirical distribution of the multi-state model, still consistently estimates the state occupation probabilities. Recently, this result has been extended to transition probabilities using landmarking, which is, inter alia, useful for dynamic prediction. We complement these results in three ways. Firstly, delayed study entry is a common phenomenon in observational studies, and we extend the earlier results to multi-state data also subject to left-truncation. Secondly, we present a rigorous proof of consistency of the Aalen-Johansen estimator for state occupation probabilities, on which also correctness of the landmarking approach hinges, correcting, simplifying and extending the earlier result. Thirdly, our rigorous proof motivates wild bootstrap resampling. Our developments for left-truncation are motivated by a prospective observational study on the occurrence and the impact of a multi-resistant infectious organism in patients undergoing surgery. Both the real data example and simulation studies are presented. Studying wild bootstrap is motivated by the fact that, unlike drawing with replacement from the data, it is desirable to have a technique that works both with non-Markov models subject to random left-truncation and right-censoring and with Markov models where left-truncation and right-censoring need not be entirely random.
Characterization of the probabilistic models that can be embedded in quantum theory

Quantum bits can be isolated to perform useful information-theoretic tasks, even though physical systems are fundamentally described by very high-dimensional operator algebras. This is because qubits can be consistently embedded into higher-dimensional Hilbert spaces. A similar embedding of classical probability distributions into quantum theory enables the emergence of classical physics via decoherence. Here, we ask which other probabilistic models can similarly be embedded into finite-dimensional quantum theory. We show that the embeddable models are exactly those that correspond to the Euclidean special Jordan algebras: quantum theory over the reals, the complex numbers, or the quaternions, and "spin factors" (qubits with more than three degrees of freedom), and direct sums thereof. Among those, only classical and standard quantum theory with superselection rules can arise from a physical decoherence map. Our results have significant consequences for some experimental tests of quantum theory, by clarifying how they could (or could not) falsify it. Furthermore, they imply that all unrestricted non-classical models must be contextual.
Gravitational field of a pit and maximal mass defects

A general relativistic solution, composed of a Zel'dovich-Letelier interior made of radial strings matched through a spherical thin shell at radius $r_0$ to an exterior Schwarzschild solution with mass $m$, is presented. It is the Zel'dovich-Letelier-Schwarzschild star. When the radius $r_0$ of the star is shrunk to its own gravitational radius $2m$, $r_0=2m$, the solutions that appear have very interesting properties. There are solutions with $m=0$ and $r_0=0$ that further obey $\frac{2m}{r_0}=1$. So, these solutions have a horizon, but they are not exactly black holes, they are quasiblack holes, though untypical ones. Moreover, the proper mass $m_p$ of the interior is nonzero and made of one string. Hence, a Minkowski exterior space hides an interior with matter in a pit. These are the pit solutions. These pits thus show a maximum mass defect. There are two classes of pit solutions, the first encloses a finite string and the second a semi-infinite one. So these pits are really stringpits, which can be seen as Wheeler's bags of gold albeit totally squashed bags. There is also another class, which is a stringy compact star at the $\frac{2m}{r_0}=1$ limit with $m$ finite. It is a typical quasiblack hole and it also shows maximum mass defect. A generic analysis is presented that shows that pit solutions with $\frac{2m}{r_0}=1$ and $m=0$ can exist displaying a maximum mass defect. The Zel'dovich-Letelier-Schwarzschild star at the $r_0=2m$ limit is actually an instance of the generic case.
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling

A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP). It uses smart initialization approaches to enrich the first generated population, and proposes various crossover operators to create a better diversity of offspring. Especially, the MIP-EGO configurator, which can tune algorithm parameters, is adopted to automatically tune operator probabilities. Furthermore, different local search strategies are employed to explore the neighborhood for better solutions. In general, the algorithm enhancement strategy can be integrated with any standard EMO algorithm. In this paper, it has been combined with NSGA-III to solve benchmark multi-objective FJSPs, whereas an off-the-shelf implementation of NSGA-III is not capable of solving the FJSP. The experimental results show excellent performance with less computing budget.
Power-Constrained Bandits

Contextual bandits often provide simple and effective personalization in decision making problems, making them popular tools to deliver personalized interventions in mobile health as well as other health applications. However, when bandits are deployed in the context of a scientific study-e.g. a clinical trial to test if a mobile health intervention is effective-the aim is not only to personalize for an individual, but also to determine, with sufficient statistical power, whether or not the system's intervention is effective. It is essential to assess the effectiveness of the intervention before broader deployment for better resource allocation. The two objectives are often deployed under different model assumptions, making it hard to determine how achieving the personalization and statistical power affect each other. In this work, we develop general meta-algorithms to modify existing algorithms such that sufficient power is guaranteed while still improving each user's well-being. We also demonstrate that our meta-algorithms are robust to various model mis-specifications possibly appearing in statistical studies, thus providing a valuable tool to study designers.
Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding

Barcodes are ubiquitous and have been used in most critical daily activities for decades. However, most traditional decoders require well-founded barcode under a relatively standard condition. While wilder conditioned barcodes such as underexposed, occluded, blurry, wrinkled and rotated are commonly captured in reality, those traditional decoders show weakness of recognizing. Several works attempted to solve those challenging barcodes, but many limitations still exist. This work aims to solve the decoding problem using deep convolutional neural network with the possibility of running on portable devices. Firstly, we proposed a special modification of inference based on the feature of having checksum and test-time augmentation, named Smart Inference (SI), in the prediction phase of a trained model. SI considerably boosts accuracy and reduces the false prediction for trained models. Secondly, we have created a large practical evaluation dataset of real captured 1D barcode under various challenging conditions to test our methods vigorously, publicly available for other researchers. The experiments' results demonstrated the SI effectiveness with the highest accuracy of 95.85% which outperformed many existing decoders on the evaluation set. Finally, we successfully minimized the best model by knowledge distillation to a shallow model which is shown to have high accuracy (90.85%) with a good inference speed of 34.2 ms per image on a real edge device.
Computing Bayes: Bayesian Computation from 1763 to the 21st Century

The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characterize Bayesian inference, with the rules of probability used to transform prior probability distributions for all unknowns - models, parameters, latent variables - into posterior distributions, subsequent to the observation of data. Conducting Bayesian inference requires the evaluation of integrals in which these probability distributions appear. Bayesian computation is all about evaluating such integrals in the typical case where no analytical solution exists. This paper takes the reader on a chronological tour of Bayesian computation over the past two and a half centuries. Beginning with the one-dimensional integral first confronted by Bayes in 1763, through to recent problems in which the unknowns number in the millions, we place all computational problems into a common framework, and describe all computational methods using a common notation. The aim is to help new researchers in particular - and more generally those interested in adopting a Bayesian approach to empirical work - make sense of the plethora of computational techniques that are now on offer; understand when and why different methods are useful; and see the links that do exist, between them all.
Coresets for clustering in Euclidean spaces: importance sampling is nearly optimal

Given a collection of n points in ℝ d , the goal of the (k,z)-clustering problem is to find a subset of k “centers” that minimizes the sum of the z-th powers of the Euclidean distance of each point to the closest center. Special cases of the (k,z)-clustering problem include the k-median and k-means problems. Our main result is a unified two-stage importance sampling framework that constructs an ε-coreset for the (k,z)-clustering problem. Compared to the results for (k,z)-clustering in [Feldman and Langberg, STOC 2011], our framework saves a ε2 d factor in the coreset size. Compared to the results for (k,z)-clustering in [Sohler and Woodruff, FOCS 2018], our framework saves a poly(k) factor in the coreset size and avoids the exp(k/ε) term in the construction time. Specifically, our coreset for k-median (z=1) has size Õ(ε−4 k) which, when compared to the result in [Sohler and Woodruff, STOC 2018], saves a k factor in the coreset size. Our algorithmic results rely on a new dimensionality reduction technique that connects two well-known shape fitting problems: subspace approximation and clustering, and may be of independent interest. We also provide a size lower bound of Ω(k· min{2 z/20,d }) for a 0.01-coreset for (k,z)-clustering, which has a linear dependence of size on k and an exponential dependence on z that matches our algorithmic results.
Faster Stochastic Quasi-Newton Methods

Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems, such as training neural networks, due to low per-iteration computational complexity. In fact, the Newton or quasi-newton (QN) methods leveraging the second-order information are able to achieve a better solution than the first-order methods. Thus, stochastic QN (SQN) methods have been developed to achieve a better solution efficiently than the stochastic first-order methods by utilizing approximate second-order information. However, the existing SQN methods still do not reach the best known stochastic first-order oracle (SFO) complexity. To fill this gap, we propose a novel faster stochastic QN method (SpiderSQN) based on the variance reduced technique of SIPDER. We prove that our SpiderSQN method reaches the best known SFO complexity of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(n+n^{1/2}\epsilon ^{-2})$ </tex-math></inline-formula> in the finite-sum setting to obtain an <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-first-order stationary point. To further improve its practical performance, we incorporate SpiderSQN with different momentum schemes. Moreover, the proposed algorithms are generalized to the online setting, and the corresponding SFO complexity of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(\epsilon ^{-3})$ </tex-math></inline-formula> is developed, which also matches the existing best result. Extensive experiments on benchmark data sets demonstrate that our new algorithms outperform state-of-the-art approaches for nonconvex optimization.
Thermodynamics of small systems with emergent structures

We derive the entropy for a closed system of particles that can form structures, molecules in the simplest case. The entropy differs from the Boltzmann-Gibbs entropy by a term that captures the molecule states. For large systems the approach is equivalent to the grand canonical ensemble. For small systems large molecules start to play a dominant role. The number of molecules becomes a key quantity and appears explicitly in the second law of thermodynamics and in fluctuation theorems. We show that the fully connected Ising model with emergent structures exhibits a first-order phase transition.
Simple Multi-Resolution Representation Learning for Human Pose Estimation

Human pose estimation - the process of recognizing human keypoints in a given image - is one of the most important tasks in computer vision and has a wide range of applications including movement diagnostics, surveillance, or self-driving vehicle. The accuracy of human keypoint prediction is increasingly improved thanks to the burgeoning development of deep learning. Most existing methods solved human pose estimation by generating heatmaps in which the $i$th heatmap indicates the location confidence of the $i$th keypoint. In this paper, we introduce novel network structures referred to as multi-resolution representation learning for human keypoint prediction. At different resolutions in the learning process, our networks branch off and use extra layers to learn heatmap generation. We firstly consider the architectures for generating the multi-resolution heatmaps after obtaining the lowest-resolution feature maps. Our second approach allows learning during the process of feature extraction in which the heatmaps are generated at each resolution of the feature extractor. The first and second approaches are referred to as multi-resolution heatmap learning and multi-resolution feature map learning respectively. Our architectures are simple yet effective, achieving good performance. We conducted experiments on two common benchmarks for human pose estimation: MS-COCO and MPII dataset. The code is made publicly available at https://github.com/tqtrunghnvn/SimMRPose.
Enumerating Chemical Graphs with Mono-block 2-Augmented Tree Structure from Given Upper and Lower Bounds on Path Frequencies

We consider a problem of enumerating chemical graphs from given constraints concerning their structures, which has an important application to a novel method for the inverse QSAR/QSPR recently proposed. In this paper, the structure of a chemical graph is specified by a feature vector each of whose entries represents the frequency of a prescribed path. We call a graph a 2-augmented tree if it is obtained from a tree (an acyclic graph) by adding edges between two pairs of nonadjacent vertices. Given a set of feature vectors as the interval between upper and lower bounds of feature vectors, we design an efficient algorithm for enumerating chemical 2-augmented trees that satisfy the path frequency specified by some feature vector in the set. We implemented the proposed algorithm and conducted some computational experiments.