Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty in lung cancer stage for survival estimation via set‐valued classification

The difficulty in identifying cancer stage in health care claims data has limited oncology quality of care and health outcomes research. We fit prediction algorithms for classifying lung cancer stage into three classes (stages I/II, stage III, and stage IV) using claims data, and then demonstrate a method for incorporating the classification uncertainty in survival estimation. Leveraging set‐valued classification and split conformal inference, we show how a fixed algorithm developed in one cohort of data may be deployed in another, while rigorously accounting for uncertainty from the initial classification step. We demonstrate this process using SEER cancer registry data linked with Medicare claims data.
Towards safe human-to-robot handovers of unknown containers

Safe human-to-robot handovers of unknown objects require accurate estimation of hand poses and object properties, such as shape, trajectory, and weight. Accurately estimating these properties requires the use of scanned 3D object models or expensive equipment, such as motion capture systems and markers, or both. However, testing handover algorithms with robots may be dangerous for the human and, when the object is an open container with liquids, for the robot. In this paper, we propose a real-to-simulation framework to develop safe human-to-robot handovers with estimations of the physical properties of unknown cups or drinking glasses and estimations of the human hands from videos of a human manipulating the container. We complete the handover in simulation, and we estimate a region that is not occluded by the hand of the human holding the container. We also quantify the safeness of the human and object in simulation. We validate the framework using public recordings of containers manipulated before a handover and show the safeness of the handover when using noisy estimates from a range of perceptual algorithms.
Robust Online Convex Optimization in the Presence of Outliers

We consider online convex optimization when a number k of data points are outliers that may be corrupted. We model this by introducing the notion of robust regret, which measures the regret only on rounds that are not outliers. The aim for the learner is to achieve small robust regret, without knowing where the outliers are. If the outliers are chosen adversarially, we show that a simple filtering strategy on extreme gradients incurs O(k) additive overhead compared to the usual regret bounds, and that this is unimprovable, which means that k needs to be sublinear in the number of rounds. We further ask which additional assumptions would allow for a linear number of outliers. It turns out that the usual benign cases of independently, identically distributed (i.i.d.) observations or strongly convex losses are not sufficient. However, combining i.i. d. observations with the assumption that outliers are those observations that are in an extreme quantile of the distribution, does lead to sublinear robust regret, even though the expected number of outliers is linear.
Persian-WSD-Corpus: A Sense Annotated Corpus for Persian All-words Word Sense Disambiguation

Word Sense Disambiguation (WSD) is a long-standing task in Natural Language Processing(NLP) that aims to automatically identify the most relevant meaning of the words in a given context. Developing standard WSD test collections can be mentioned as an important prerequisite for developing and evaluating different WSD systems in the language of interest. Although many WSD test collections have been developed for a variety of languages, no standard All-words WSD benchmark is available for Persian. In this paper, we address this shortage for the Persian language by introducing SBU-WSD-Corpus, as the first standard test set for the Persian All-words WSD task. SBU-WSD-Corpus is manually annotated with senses from the Persian WordNet (FarsNet) sense inventory. To this end, three annotators used SAMP (a tool for sense annotation based on FarsNet lexical graph) to perform the annotation task. SBU-WSD-Corpus consists of 19 Persian documents in different domains such as Sports, Science, Arts, etc. It includes 5892 content words of Persian running text and 3371 manually sense annotated words (2073 nouns, 566 verbs, 610 adjectives, and 122 adverbs). Providing baselines for future studies on the Persian All-words WSD task, we evaluate several WSD models on SBU-WSD-Corpus. The corpus is publicly available at this https URL.
Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints

The effectiveness of fingerprint-based authentication systems on good quality fingerprints is established long back. However, the performance of standard fingerprint matching systems on noisy and poor quality fingerprints is far from satisfactory. Towards this, we propose a data uncertainty-based framework which enables the state-of-the-art fingerprint pre-processing models to quantify noise present in the input image and identify fingerprint regions with background noise and poor ridge clarity. Quantification of noise helps the model two folds: firstly, it makes the objective function adaptive to the noise in a particular input fingerprint and consequently, helps to achieve robust performance on noisy and distorted fingerprint regions. Secondly, it provides a noise variance map which indicates noisy pixels in the input fingerprint image. The predicted noise variance map enables the end-users to understand erroneous predictions due to noise present in the input image. Extensive experimental evaluation on 13 publicly available fingerprint databases, across different architectural choices and two fingerprint processing tasks demonstrate effectiveness of the proposed framework.
Integrating Expert Knowledge With Domain Adaptation for Unsupervised Fault Diagnosis

Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy observations and only few samples of faulty conditions. The lack of labels and fault samples imposes a significant challenge for existing data-driven fault diagnosis methods. In this article, we aim to overcome this limitation by integrating expert knowledge with domain adaptation (DA) in a synthetic-to-real framework for unsupervised fault diagnosis. Motivated by the fact that domain experts often have a relatively good understanding on how different fault types affect healthy signals, in the first step of the proposed framework, a synthetic fault dataset is generated by augmenting real vibration samples of healthy bearings. This synthetic dataset integrates expert knowledge and encodes class information about the faults types. However, models trained solely based on the synthetic data often do not perform well because of the distinct distribution difference between the synthetically generated and real faults. To overcome this domain gap between the synthetic and real data, in the second step of the proposed framework, an imbalance-robust DA approach is proposed to adapt the model from synthetic faults (source) to the unlabeled real faults (target) which suffer from severe class imbalance. The framework is evaluated on two unsupervised fault diagnosis cases for bearings, the CWRU laboratory dataset and a real-world wind-turbine dataset. Experimental results demonstrate that the generated faults are effective for encoding fault type information and the DA is robust against the different levels of class imbalance between faults.
Convergence Analysis of A Proximal Linearized ADMM Algorithm for Nonconvex Nonsmooth Optimization

In this paper, we consider a proximal linearized alternating direction method of multipliers (PL-ADMM) for solving linearly constrained nonconvex and possibly nonsmooth optimization problems. The algorithm is generalized by using variable metric proximal terms in the primal updates and an over-relaxation stepsize in the multiplier update. We prove that the sequence generated by this method is bounded and its limit points are critical points. Under the powerful KurdykaLojasiewicz properties we prove that the sequence has a finite length thus converges, and we drive its convergence rates.
Study of conduction, block and reflection at the excitable tissues boundary in terms of the interval model of action potential

Some mechanisms of cardiac arrhythmias can be presented as a composition of elementary acts of block and reflection on the contacts of homogeneous areas of the conducting tissue. For study this phenomena we use an axiomatic one-dimensional model of interaction of cells of excitable tissue. The model has four functional parameters that determine the functional states durations of the cell. We show that the cells of a homogeneous excitable tissue, depending on the ratio of the durations of the functional intervals, can operate in the mode of solitary wave’s conduction or in one of three modes of self-generation. It is proved that the propagation of a solitary wave through the boundary of homogeneous conducting tissues can be accompanied by a block or multiplex reflection. Block and reflection are unidirectional phenomena, and there are not compatible on the same boundary. Systematized rules of transmitting, block and reflection waves at the boundary of homogeneous conducting tissues open up new possibilities for design mechanisms of generation and analyzing complex heart rate patterns.
Multivariate functional group sparse regression: Functional predictor selection

In this paper, we propose methods for functional predictor selection and the estimation of smooth functional coefficients simultaneously in a scalar-on-function regression problem under a high-dimensional multivariate functional data setting. In particular, we develop two methods for functional group-sparse regression under a generic Hilbert space of infinite dimension. We show the convergence of algorithms and the consistency of the estimation and the selection (oracle property) under infinite-dimensional Hilbert spaces. Simulation studies show the effectiveness of the methods in both the selection and the estimation of functional coefficients. The applications to functional magnetic resonance imaging (fMRI) reveal the regions of the human brain related to ADHD and IQ.
Online identification of symmetric pure states

We consider online strategies for discriminating between symmetric pure states with zero error when n copies of the states are provided. Optimized online strategies involve local, possibly adaptive measurements on each copy and are optimal at each step, which makes them robust in front of particle losses or an abrupt termination of the discrimination process. We first review previous results on binary minimum and zero error discrimination with local measurements that achieve the maximum success probability set by optimizing over global measurements, highlighting their online features. We then extend these results to the case of zero error identification of three symmetric states with constant overlap. We provide optimal online schemes that attain global performance for any n if the state overlaps are positive, and for odd n if overlaps have a negative value. For arbitrary complex overlaps, we show compelling evidence that online schemes fail to reach optimal global performance. The online schemes that we describe only require to store the last outcome obtained in a classical memory, and adaptiveness of the measurements reduce to at most two changes, regardless of the value of n.
Hot carrier dynamics and electron-optical phonon coupling in photoexcited graphene via time-resolved ultrabroadband terahertz spectroscopy

Electron-electron (e-e) interaction is known as a source of logarithmic renormalizations for Dirac fermions in quantum field theory. The renormalization of electron–optical phonon coupling (EPC) by e-e interaction, which plays a pivotal role in hot carrier and phonon dynamics, has been discussed after the discovery of graphene. We investigate the hot carrier dynamics and the EPC strength using time-resolved ultrabroadband terahertz (THz) spectroscopy combined with numerical simulation based on the Boltzmann transport equation and comprehensive temperature model. The numerical simulation demonstrates that the extrinsic carrier scatterings by the Coulomb potential of the charged impurity and surface polar phonons are significantly suppressed by the carrier screening effect and have negligible contributions to the THz photoconductivity in heavily doped graphene on polyethylene terephthalate (PET) substrate. The large negative photoconductivity and the nonDrude behavior of THz conductivity spectra appear under high pump fluence and can be attributed to the temporal variation of the hot carrier distribution and scattering rate. The transient reflectivity well reflects the EPC strength and temporal evolution of the hot carrier and optical phonon dynamics. We successfully estimate the EPC matrix element of the A1 optical phonon mode near the K point as 〈 D K 〉
Estimating Counts Through an Average Rounded to the Nearest Non-negative Integer and its Theoretical&Practical Effects

In practice, the use of rounding is ubiquitous. Although researchers have looked at the implications of rounding continuous random variables, rounding may be applied to functions of discrete random variables as well. For example, to infer on suicide excess deaths after a national emergency, authorities may provide a rounded average of deaths before and after the emergency started. Suicide rates tend to be relatively low around the world and such rounding may seriously affect inference on the change of suicide rate. In this paper, we study the scenario when a rounded to nearest integer average is used as a proxy for a non-negative discrete random variable. Specifically, our interest is in drawing inference on a parameter from the pmf of Y , when we get U = n[Y /n] as a proxy for Y . The probability generating function of U , E(U ), and Var(U ) capture the effect of the coarsening of the support of Y . Also, moments and estimators of distribution parameters are explored for some special cases. We show that under certain conditions, there is little impact from rounding. However, we also find scenarios where rounding can significantly affect statistical inference as demonstrated in three examples. The simple methods we propose are able to partially counter rounding error effects. While for some probability distributions it may be difficult to derive maximum likelihood estimators as a function of U , we provide a framework to obtain an estimator numerically.
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics

Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learning algorithms are not sample-efficient and requires long training times and quantities of data. In this work, we proposed a framework for sample-efficient Reinforcement Learning that take advantage of state and action representations to transform a high-dimensional problem into a low-dimensional one. Moreover, we seek to find the optimal policy mapping latent states to latent actions. Because now the policy is learned on abstract representations, we enforce, using auxiliary loss functions, the lifting of such policy to the original problem domain. Results show that the novel framework can efficiently learn low-dimensional and interpretable state and action representations and the optimal latent policy.
A topological solution to object segmentation and tracking

Significance We address a question at the foundation of natural and artificial vision: how can a visual system segment and track objects? In the real world, objects can undergo drastic changes in appearance due to deformation, perspective change, or dynamic occlusion. For example, an animal moving behind a fence will split into multiple pieces. How can a visual system apply a common label to all the pieces of the same object across space and time? Here, we prove that this can be solved using purely geometric mechanisms and furthermore demonstrate the approach on cluttered synthetic video. By enabling the automatic transformation of visual information from a sensory to symbolic format, the mechanism described here provides a springboard from sensation to intelligent symbolic reasoning.
Alternating direction method of multipliers applied to medical image restoration

We investigate the effects of the regularization parameter for the norm () and penalty parameter () in the alternating direction method of multipliers (ADMM) on the quality of restored medical images. Simulation studies are performed using images degraded by a point spread function (PSF) and Gaussian noise. The j-th column of the system matrix () is calculated by convolving the image with unity at pixel j and zero at all other pixels and the PSF. The simulation studies show that the mean structural similarity index is maximal when is approximately 10 to 20, where , with and being the transpose of A and the observed data, respectively. The restored image became blurred with a decrease in . This study will be useful for identifying optimal parameter values in the ADMM when applied to medical image restoration.
Implicit Greedy Rank Learning in Autoencoders via Overparameterized Linear Networks

Deep linear networks trained with gradient descent yield low rank solutions, as is typically studied in matrix factorization. In this paper, we take a step further and analyze implicit rank regularization in autoencoders. We show greedy learning of low-rank latent codes induced by a linear sub-network at the autoencoder bottleneck. We further propose orthogonal initialization and principled learning rate adjustment to mitigate sensitivity of training dynamics to spectral prior and linear depth. With linear autoencoders on synthetic data, our method converges stably to ground-truth latent code rank. With nonlinear autoencoders, our method converges to latent ranks optimal for downstream classification and image sampling.
Popcorn: Paillier Meets Compression For Efficient Oblivious Neural Network Inference

Oblivious inference enables the cloud to provide neural network inference-as-a-service (NN-IaaS), whilst neither disclosing the client data nor revealing the server's model. However, the privacy guarantee under oblivious inference usually comes with a heavy cost of efficiency and accuracy. We propose Popcorn, a concise oblivious inference framework entirely built on the Paillier homomorphic encryption scheme. We design a suite of novel protocols to compute non-linear activation and max-pooling layers. We leverage neural network compression techniques (i.e., neural weights pruning and quantization) to accelerate the inference computation. To implement the Popcorn framework, we only need to replace algebraic operations of existing networks with their corresponding Paillier homomorphic operations, which is extremely friendly for engineering development. We first conduct the performance evaluation and comparison based on the MNIST and CIFAR-10 classification tasks. Compared with existing solutions, Popcorn brings a significant communication overhead deduction, with a moderate runtime increase. Then, we benchmark the performance of oblivious inference on ImageNet. To our best knowledge, this is the first report based on a commercial-level dataset, taking a step towards the deployment to production.
DPPIN: A Biological Dataset of Dynamic Protein-Protein Interaction Networks

Nowadays, many network representation learning algorithms and downstream network mining tasks have already paid attention to dynamic networks or temporal networks, which are more suitable for real-world complex scenarios by modeling evolving patterns and temporal dependencies between node interactions. Moreover, representing and mining temporal networks have a wide range of applications, such as fraud detection, social network analysis, and drug discovery. To contribute to the network representation learning and network mining research community, in this paper, we generate a new biological dataset of dynamic protein-protein interaction networks (i.e., DPPIN), which consists of twelve dynamic protein-level interaction networks of yeast cells at different scales. We first introduce the generation process of DPPIN. To demonstrate the value of our published dataset DPPIN, we then list the potential applications that would be benefited. Furthermore, we design dynamic local clustering, dynamic spectral clustering, dynamic subgraph matching, dynamic node classification, and dynamic graph classification experiments, where DPPIN indicates future research opportunities for some tasks by presenting challenges on state-of-the-art baseline algorithms. Finally, we identify future directions for improving this dataset utility and welcome inputs from the community. All resources of this work are deployed and publicly available at https://github.com/DongqiFu/DPPIN.
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation

Vanilla models for object detection and instance segmentation suffer from the heavy bias toward detecting frequent objects in the long-tailed setting. Existing methods address this issue mostly during training, e.g., by re-sampling or re-weighting. In this paper, we investigate a largely overlooked approach -- post-processing calibration of confidence scores. We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size. We show that separately handling the background class and normalizing the scores over classes for each proposal are keys to achieving superior performance. On the LVIS dataset, NorCal can effectively improve nearly all the baseline models not only on rare classes but also on common and frequent classes. Finally, we conduct extensive analysis and ablation studies to offer insights into various modeling choices and mechanisms of our approach. Our code is publicly available at https://github.com/tydpan/NorCal/.
A comparative study of eight human auditory models of monaural processing

A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models.