Miracle-dz/UniFAR-FTUs huggingface dataset:
Appearance Fusion of Multiple Cues for Video Co-localization

This work addresses the joint object discovery problem in videos while utilizing multiple object-related cues. In contrast to the usual spatial fusion approach, a novel appearance fusion approach is presented here. Specifically, this paper proposes an effective fusion process of different GMMs derived from multiple cues into one GMM. Much the same as any fusion strategy, this approach also needs some guidance. The proposed method relies on reliability and consensus phenomenon for guidance. As a case study, we pursue the "video co-localization" object discovery problem to propose our methodology. Our experiments on YouTube Objects and YouTube Co-localization datasets demonstrate that the proposed method of appearance fusion undoubtedly has an advantage over both the spatial fusion strategy and the current state-of-the-art video co-localization methods.
Weighting NTBEA for Game AI Optimisation

The N-Tuple Bandit Evolutionary Algorithm (NTBEA) has proven very effective in optimising algorithm parameters in Game AI. A potential weakness is the use of a simple average of all component Tuples in the model. This study investigates a refinement to the N-Tuple model used in NTBEA by weighting these component Tuples by their level of information and specificity of match. We introduce weighting functions to the model to obtain Weighted- NTBEA and test this on four benchmark functions and two game environments. These tests show that vanilla NTBEA is the most reliable and performant of the algorithms tested. Furthermore we show that given an iteration budget it is better to execute several independent NTBEA runs, and use part of the budget to find the best recommendation from these runs.
A table of n-component handlebody links of genus n+1 up to six crossings

Abstract A handlebody link is a union of handlebodies of positive genus embedded in 3-space, which generalises the notion of links in classical knot theory. In this paper, we consider handlebody links with a genus two handlebody and $n-1$ solid tori, $n>1$ . Our main result is the classification of such handlebody links with six crossings or less, up to ambient isotopy.
Exact dynamics and thermalization of open quantum systems coupled to reservoirs through particle exchanges

In this paper, we study the exact dynamics of general open systems interacting with their environments through particle exchanges. The paper includes two main results. First, by taking advantage of the propagating function in the coherent state representation, we obtain the exact solution of the reduced density matrix, which is expressed in terms of the nonequilibrium Green functions. Second, in the dynamical perspective, we provide a rigorous thermalization process of open quantum systems.
Monocular Depth Prediction through Continuous 3D Loss

This paper reports a new continuous 3D loss function for learning depth from monocular images. The dense depth prediction from a monocular image is supervised using sparse LIDAR points, which enables us to leverage available open source datasets with camera-LIDAR sensor suites during training. Currently, accurate and affordable range sensor is not readily available. Stereo cameras and LIDARs measure depth either inaccurately or sparsely/costly. In contrast to the current point-to-point loss evaluation approach, the proposed 3D loss treats point clouds as continuous objects; therefore, it compensates for the lack of dense ground truth depth due to LIDAR’s sparsity measurements. We applied the proposed loss in three state-of-the-art monocular depth prediction approaches DORN, BTS, and Monodepth2. Experimental evaluation shows that the proposed loss improves the depth prediction accuracy and produces point-clouds with more consistent 3D geometric structures compared with all tested baselines, implying the benefit of the proposed loss on general depth prediction networks. A video demo of this work is available at https://youtu.be/5HL8BjSAY4Y.
Temporal Conjunctive Query Answering in the Extended DL-Lite Family

Ontology-based query answering (OBQA) augments classical query answering in databases by domain knowledge encoded in an ontology. Systems for OBQA use the ontological knowledge to infer new information that is not explicitly given in the data. Moreover, they usually employ the open-world assumption, which means that knowledge that is not stated explicitly in the data and that is not inferred is not assumed to be true or false. Classical OBQA however considers only a snapshot of the data, which means that information about the temporal evolution of the data is not used for reasoning and hence lost. We investigate temporal conjunctive queries (TCQs) that allow to access temporal data through classical ontologies. In particular, we study combined and data complexity of TCQ entailment for ontologies written in description logics from the extended DL-Lite family. Many of these logics allow for efficient reasoning in the atemporal setting and are successfully applied in practice. We show comprehensive complexity results for temporal reasoning with these logics.
Understanding the robustness of deep neural network classifiers for breast cancer screening

Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. There exists extensive literature on this subject in the context of natural images that can potentially be built upon. However, it cannot be assumed that conclusions about robustness will transfer from natural images to mammogram images, due to significant differences between the two image modalities. In order to determine whether conclusions will transfer, we measure the sensitivity of a radiologist-level screening mammogram image classifier to four commonly studied input perturbations that natural image classifiers are sensitive to. We find that mammogram image classifiers are also sensitive to these perturbations, which suggests that we can build on the existing literature. We also perform a detailed analysis on the effects of low-pass filtering, and find that it degrades the visibility of clinically meaningful features called microcalcifications. Since low-pass filtering removes semantically meaningful information that is predictive of breast cancer, we argue that it is undesirable for mammogram image classifiers to be invariant to it. This is in contrast to natural images, where we do not want DNNs to be sensitive to low-pass filtering due to its tendency to remove information that is human-incomprehensible.
Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach

With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-12 students who are at risk of dropping out of the online courses. Prior researchers have focused on predicting dropout in Massive Open Online Courses (MOOCs), which often deliver higher education, i.e., graduate level courses at top institutions. However, few studies have focused on developing a machine learning approach for students in K-12 online courses. In this paper, we develop a machine learning framework to conduct accurate at-risk student identification specialized in K-12 multimodal online environments. Our approach considers both online and offline factors around K-12 students and aims at solving the challenges of (1) multiple modalities, i.e., K-12 online environments involve interactions from different modalities such as video, voice, etc; (2) length variability, i.e., students with different lengths of learning history; (3) time sensitivity, i.e., the dropout likelihood is changing with time; and (4) data imbalance, i.e., only less than 20\% of K-12 students will choose to drop out the class. We conduct a wide range of offline and online experiments to demonstrate the effectiveness of our approach. In our offline experiments, we show that our method improves the dropout prediction performance when compared to state-of-the-art baselines on a real-world educational dataset. In our online experiments, we test our approach on a third-party K-12 online tutoring platform for two months and the results show that more than 70\% of dropout students are detected by the system.
Data Mapping and Finite Difference Learning.

Restricted Boltzmann machine (RBM) is a two-layer neural network constructed as a probabilistic model and its training is to maximize a product of probabilities by the contrastive divergence (CD) scheme. In this paper a data mapping is proposed to describe the relationship between the visible and hidden layers and the training is to minimize a squared error on the visible layer by a finite difference learning. This paper presents three new properties in using the RBM: 1) nodes on the visible and hidden layers can take real-valued matrix data without a probabilistic interpretation; 2) the famous CD1 is a finite difference approximation of the gradient descent; 3) the activation can take non-sigmoid functions such as identity, relu and softsign. The data mapping provides a unified framework on the dimensionality reduction, the feature extraction and the data representation pioneered and developed by Hinton and his colleagues. As an approximation of the gradient descent, the finite difference learning is applicable to both directed and undirected graphs. Numerical experiments are performed to verify these new properties on the very low dimensionality reduction, the collinearity of timer series data and the use of flexible activations.
Management of pharynx fistula after upper digestive tract instrumentation

Objectives: Pharynx fistula is a pathological state that can pose serious problems for both physician and patient and can lead up to the death of the patient in complicated cases. Methods: The authors describe a series of guidelines for the management of the pharynx fistula regarding the complications of the instrumentation of the upper digestive tract. Results: Most of the cases that are addressed to our clinic can be treated with a conservative approach and a nutrition therapy plan tailored to each case. In selected cases surgery is the method of choice for therapy. RESEARCH ROJSP 2017, Vol. 2 (issue 1): E 35-40.
Intensity mapping as a probe of axion dark matter

Intensity mapping (IM) of spectral lines has the potential to revolutionize cosmology by increasing the total number of observed modes by several orders of magnitude compared to the cosmic microwave background (CMB) anisotropies. In this paper, we consider IM of neutral hydrogen (HI) in the redshift range $0 \lesssim z \lesssim 3$ employing a halo model approach where HI is assumed to follow the distribution of dark matter (DM) halos. If a portion of the DM is composed of ultralight axions then the abundance of halos is changed compared to cold dark matter below the axion Jeans mass. With fixed total HI density, $\Omega_{\rm HI}$, assumed to reside entirely in halos, this effect introduces a scale-independent increase in the HI power spectrum on scales above the axion Jeans scale, which our model predicts consistent with N-body simulations. Lighter axions introduce a scale-dependent feature even on linear scales due to its suppression of the matter power spectrum near the Jeans scale. We use the Fisher matrix formalism to forecast the ability of future HI surveys to constrain the axion fraction of DM and marginalize over astrophysical and model uncertainties. We find that a HIRAX-like survey is a very reliable IM survey configuration, being affected minimally by uncertainties due to non-linear scales, while the SKA1MID configuration is the most constraining as it is sensitive to non-linear scales. Including non-linear scales and combining a SKA1MID-like IM survey with the Simons Observatory CMB, the benchmark ''fuzzy DM'' model with $m_a = 10^{-22}\text{ eV}$ can be constrained at the 10% level. For lighter ULAs this limit improves below 1%, and allows the possibility to test the connection between axion models and the grand unification scale across a wide range of masses.
Fracton hydrodynamics

We introduce new classes of hydrodynamic theories inspired by the recently discovered fracton phases of quantum matter. Fracton phases are characterized by elementary excitations (fractons) with restricted mobility. The hydrodynamic theories we introduce describe thermalization in systems with fracton-like mobility constraints, including fluids where charge and dipole moment are both locally conserved, and fluids where charge is conserved along every line or plane of a lattice. Each of these fluids is subdiffusive, and constitutes a new universality class of hydrodynamic behavior. There are infinitely many such classes, each with distinct subdiffusive exponents, all of which are captured by our formalism. Our framework naturally explains recent results on dynamics with constrained quantum circuits, as well as recent experiments with ultracold atoms in tilted optical lattices. We identify crisp experimental signatures of these novel hydrodynamics, and explain how they may be realized in near term ultracold atom experiments.
Understand how radial shockwave therapy can help in rehabilitation of musculoskeletal disorders

his article discusses the role that the relatively new treatment modality of radial shockwave therapy (RSWT) can play in rehabilitation of musculoskeletal disorders. The study presents empirical evidence on the benefits of RSWT in the short term for treating lower limb injuries as measured by perceived reduction in pain and increased functional mobility. This article highlights how effectively RSWT can be used in the early treatment of musculoskeletal disorders and the importance and continued use of traditional rehab modalities following RSWT. You can test your knowledge of the subject using our elearning assessment and include the resulting certificate in your CPD portfolio, as well as print out our professionally produced patient information leaflet to give to your patients.
PathVQA: 30000+ Questions for Medical Visual Question Answering

Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology? To achieve this goal, the first step is to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer. Our work makes the first attempt to build such a dataset. Different from creating general-domain VQA datasets where the images are widely accessible and there are many crowdsourcing workers available and capable of generating question-answer pairs, developing a medical VQA dataset is much more challenging. First, due to privacy concerns, pathology images are usually not publicly available. Second, only well-trained pathologists can understand pathology images, but they barely have time to help create datasets for AI research. To address these challenges, we resort to pathology textbooks and online digital libraries. We develop a semi-automated pipeline to extract pathology images and captions from textbooks and generate question-answer pairs from captions using natural language processing. We collect 32,799 open-ended questions from 4,998 pathology images where each question is manually checked to ensure correctness. To our best knowledge, this is the first dataset for pathology VQA. Our dataset will be released publicly to promote research in medical VQA.
Fairway: SE Principles for Building Fairer Software

Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a biased manner that gives undue advantages to a specific group of people (where those groups are determined by sex, race, etc.). This"algorithmic discrimination"in the AI software systems has become a matter of serious concern in the machine learning and software engineering community. There have been works done to find"algorithmic bias"or"ethical bias"in the software system. Once the bias is detected in the AI software system, the mitigation of bias is extremely important. In this work, we a)explain how ground-truth bias in training data affects machine learning model fairness and how to find that bias in AI software,b)propose a methodFairwaywhich combines pre-processing and in-processing approach to remove ethical bias from training data and trained model. Our results show that we can find bias and mitigate bias in a learned model, without much damaging the predictive performance of that model. We propose that (1) test-ing for bias and (2) bias mitigation should be a routine part of the machine learning software development life cycle. Fairway offers much support for these two purposes.
Tracking an Auto-Regressive Process with Limited Communication per Unit Time †

Samples from a high-dimensional AR[1] process are quantized and sent over a time-slotted communication channel of finite capacity. The receiver seeks to form an estimate of the process in real-time. We consider the slow-sampling regime where multiple communication slots occur between two sampling instants. We propose a successive update scheme which uses communication between sampling instants to update the estimates of the latest sample. We show that there exist quantizers that render the fast but loose version of this scheme, which updates estimates in every slot, universally optimal asymptotically. However, we provide evidence that most practical quantizers will require a judiciously chosen update frequency.
The impact of peculiar velocities on supernova cosmology

We study correlated fluctuations of Type~Ia supernova observables due to peculiar velocities of both the observer and the supernova host galaxies, and their impact on cosmological parameter estimation. We demonstrate using the CosmicFlows-3 dataset that at low redshifts the corrections for peculiar velocities in the JLA catalogue have been systematically underestimated. By querying a horizon-size N-body simulation we find that compared to a randomly placed Copernican observer, an observer in an environment like our local universe will see 2--5 times stronger correlations between supernovae in the JLA catalogue. Hence the covariances usually employed which assume a Copernican observer underestimate the effects of coherent motion of the supernova host galaxies. Although previous studies have suggested that this should have $<2\%$ effect on cosmological parameter estimation, we find that when peculiar velocities are treated consistently the JLA data favours significantly smaller values of matter and dark energy density than in the standard $\Lambda$CDM model. A joint fit to simultaneously determine the cosmological parameters and the bulk flow finds a bulk flow faster than 200 km~s$^{-1}$ continuing beyond 200~Mpc. This demonstrates that the local bulk flow is an essential nuisance parameter which must be included in cosmological model fitting when analysing supernova data.
DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks

With the widespread development of the quantified-self movement, an increasing number of users rely on mobile applications to monitor their physical activity through their smartphones. However, granting applications a direct access to sensor data exposes users to privacy risks. In particular, motion sensor data are usually transmitted to analytics applications hosted in the cloud, which leverages on machine learning models to provide feedback on their activity status to users. In this setting, nothing prevents the service provider to infer private and sensitive information about a user such as health or demographic attributes. To address this issue, we propose DySan, a privacy-preserving framework to sanitize motion sensor data against unwanted sensitive inferences (i.e., improving privacy) while limiting the loss of accuracy on the physical activity monitoring (i.e., maintaining data utility). Our approach is inspired from the framework of Generative Adversarial Networks to sanitize the sensor data for the purpose of ensuring a good trade-off between utility and privacy. More precisely, by learning in a competitive manner several networks, DySan is able to build models that sanitize motion data against inferences on a specified sensitive attribute (e.g., gender) while maintaining an accurate activity recognition. DySan builds various sanitizing models, characterized by different sets of hyperparameters in the global loss function, to propose a transfer learning scheme over time by dynamically selecting the model which provides the best utility and privacy trade-off according to the incoming data. Experiments conducted on real datasets demonstrate that DySan can drastically limit the gender inference up to 41% (from 98% with raw data to 57% with sanitized data) while only reducing the accuracy of activity recognition by 3% (from 95% with raw data to 92% with sanitized data).
Mathematical analysis of electromagnetic scattering by dielectric nanoparticles with high refractive indices

In this work, we are concerned with the mathematical modeling of the electromagnetic (EM) scattering by arbitrarily shaped non-magnetic nanoparticles with high refractive indices. When illuminated by visible light, such particles can exhibit a very strong isotropic magnetic response, resulting from the coupling of the incident wave with the circular displacement currents of the EM fields. The main aim of this work is to mathematically illustrate this phenomenon. We shall first introduce the EM scattering resolvent and the concept of dielectric subwavelength resonances. Then we derive the a priori estimates for the subwavelength resonances and the associated resonant modes. We also show the existence of resonances and obtain their asymptotic expansions in terms of the small particle size and the high contrast parameter. After that, we investigate the enhancement of the scattering amplitude and the cross sections when the resonances occur. In doing so, we develop a novel multipole radiation framework that directly separates the electric and magnetic multipole moments and allows us to clearly see their orders of magnitude and blow-up rates. We prove that at the dielectric subwavelength resonant frequencies, the nanoparticles with high refractive indices behave like the sum of the electric dipole and the resonant magnetic dipole. Some explicit calculations and numerical experiments are also provided to validate our general results and formulas.
AgNOR histochemical expression in benign prostatic hyperplasia and prostatic adenocarcinoma

Benign prostatic hyperplasia and prostatic adenocarcinoma were common diseases and usually occurred after the 5th decade of life. The problem in diagnosing using Hematoxylin and Eosin staining was how to differentiate whether it is benign or malignant zone. Therefore, proliferating markers, such as AgNOR, could be helping to over this difficulty. A descriptive study using consecutive sampling as the method of sample recruiting was conducted to describe AgNOR histochemical expression in benign prostatic hyperplasia and prostatic adenocarcinoma. AgNOR staining was done in 13 benign prostatic hyperplasia samples and 7 prostatic adenocarcinoma samples, which have been confirmed using p63 immunohistochemical staining before. Benign prostatic hyperplasia usually showed lower AgNOR proliferating activity while all of theprostatic adenocarcinoma (100%) had high AgNOR proliferating activity.