Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic target positioning and tracking for image-guided radiotherapy without implanted fiducials

Current image-guided prostate radiotherapy often relies on the use of implanted fiducials or transducers for target localization. Fiducial or transducer insertion requires an invasive procedure that adds cost and risks for bleeding, infection, and discomfort to some patients. We are developing a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kV X-ray images without the need for daily cone-beam computed tomography (CBCT). A deep learning model was first trained by using several thousand annotated projection X-ray images. The trained model is capable of identifying the location of the prostate target for a given input X-ray projection image. To assess the accuracy of the approach, three patients with prostate cancer received volumetric modulated arc therapy (VMAT) were retrospectively studied. The results obtained by using the deep learning model and the actual position of the prostate were compared quantitatively. The deviations between the target positions obtained by the deep learning model and the corresponding annotations ranged from 1.66 mm to 2.77 mm for anterior-posterior (AP) direction, and from 1.15 mm to 2.88 mm for lateral direction. Target position provided by deep learning model for the kV images acquired using OBI is found to be consistent that derived from the fiducials. This study demonstrates, for the first time, that highly accurate markerless prostate localization based on deep learning is achievable. The strategy provides a clinically valuable solution to daily patient positioning and real-time target tracking for image-guided radiotherapy (IGRT) and interventions.
Sequential Experimental Design for Transductive Linear Bandits

In this paper we introduce the transductive linear bandit problem: given a set of measurement vectors $\mathcal{X}\subset \mathbb{R}^d$, a set of items $\mathcal{Z}\subset \mathbb{R}^d$, a fixed confidence $\delta$, and an unknown vector $\theta^{\ast}\in \mathbb{R}^d$, the goal is to infer $\text{argmax}_{z\in \mathcal{Z}} z^\top\theta^\ast$ with probability $1-\delta$ by making as few sequentially chosen noisy measurements of the form $x^\top\theta^{\ast}$ as possible. When $\mathcal{X}=\mathcal{Z}$, this setting generalizes linear bandits, and when $\mathcal{X}$ is the standard basis vectors and $\mathcal{Z}\subset \{0,1\}^d$, combinatorial bandits. Such a transductive setting naturally arises when the set of measurement vectors is limited due to factors such as availability or cost. As an example, in drug discovery the compounds and dosages $\mathcal{X}$ a practitioner may be willing to evaluate in the lab in vitro due to cost or safety reasons may differ vastly from those compounds and dosages $\mathcal{Z}$ that can be safely administered to patients in vivo. Alternatively, in recommender systems for books, the set of books $\mathcal{X}$ a user is queried about may be restricted to well known best-sellers even though the goal might be to recommend more esoteric titles $\mathcal{Z}$. In this paper, we provide instance-dependent lower bounds for the transductive setting, an algorithm that matches these up to logarithmic factors, and an evaluation. In particular, we provide the first non-asymptotic algorithm for linear bandits that nearly achieves the information theoretic lower bound.
Citizens' Emotion on GST: A Spatio-Temporal Analysis over Twitter Data

People might not be close-at-hand but they still are - by virtue of the social network. The social network has transformed lives in many ways. People can express their views, opinions and life experiences on various platforms be it Twitter, Facebook or any other medium there is. Such events constitute of reviewing a product or service, conveying views on political banters, predicting share prices or giving feedback on the government policies like Demonetization or GST. These social platforms can be used to investigate the insights of the emotional curve that the general public is generating. This kind of analysis can help make a product better, predict the future prospects and also to implement the public policies in a better way. Such kind of research on sentiment analysis is increasing rapidly. In this research paper, we have performed temporal analysis and spatial analysis on 1,42,508 and 58,613 tweets respectively and these tweets were posted during the post-GST implementation period from July 04, 2017 to July 25, 2017. The tweets were collected using the Twitter streaming API. A well-known lexicon, National Research Council Canada (NRC) emotion Lexicon is used for opinion mining that exhibits a blend of eight basic emotions i.e. joy, trust, anticipation, surprise, fear sadness, anger, disgust and two sentiments i.e. positive and negative for 6,554 words.
Unleashing the Unused Potential of I-Vectors Enabled by GPU Acceleration

Speaker embeddings are continuous-value vector representations that allow easy comparison between voices of speakers with simple geometric operations. Among others, i-vector and x-vector have emerged as the mainstream methods for speaker embedding. In this paper, we illustrate the use of modern computation platform to harness the benefit of GPU acceleration for i-vector extraction. In particular, we achieve an acceleration of 3000 times in frame posterior computation compared to real time and 25 times in training the i-vector extractor compared to the CPU baseline from Kaldi toolkit. This significant speed-up allows the exploration of ideas that were hitherto impossible. In particular, we show that it is beneficial to update the universal background model (UBM) and re-compute frame alignments while training the i-vector extractor. Additionally, we are able to study different variations of i-vector extractors more rigorously than before. In this process, we reveal some undocumented details of Kaldi's i-vector extractor and show that it outperforms the standard formulation by a margin of 1 to 2% when tested with VoxCeleb speaker verification protocol. All of our findings are asserted by ensemble averaging the results from multiple runs with random start.
On application of a response propensity model to estimation from web samples

Increasing nonresponse rates and the cost of data collection are two pressing problems encountered in traditional randomized surveys. The proliferation of inexpensive data from web surveys stimulates interest in statistical techniques for valid inferences from web samples. We consider estimation of population and domain means in the two-sample setup, where the web sample contains variables of interest and covariates that are shared with an auxiliary random sample. First, we propose an estimator of population mean, based on the estimated propensity of response to a web survey, a.k. a. web response propensity. This makes inferences from web samples that are similar to well-established techniques used for observational studies and missing data problems. Second, we propose an "implicit logistic" regression for estimating parameters of the web response model in the two-sample setup. In addition to random sample design information, it utilizes random sample inclusion probabilities, nominally assigned to web sample units, and the size of the subpopulation of web responders. A simulation study confirms validity of the proposed estimator in comparison with alternative approximate estimators. We illustrate our method by estimating prevalence of chronic health conditions and related medication use for the U.S. population of adults, using web and random samples from experimental web survey and the National Health Interview Survey.
Individual Fairness in Sponsored Search Auctions

Fairness in advertising is a topic of particular interest in both the computer science and economics literatures, supported by theoretical and empirical observations. We initiate the study of tradeoffs between individual fairness and performance in online advertising, where advertisers place bids on ad slots for each user and the platform must determine which ads to display. Our main focus is to investigate the "cost of fairness": more specifically, whether a fair allocation mechanism can achieve utility close to that of a utility-optimal unfair mechanism. Motivated by practice, we consider both the case of many advertisers in a single category, e.g. sponsored results on a job search website, and ads spanning multiple categories, e.g. personalized display advertising on a social networking site, and show the tradeoffs are inherently different in these settings. We prove lower and upper bounds on the cost of fairness for each of these settings. For the single category setting, we show constraints on the "fairness" of advertisers' bids are necessary to achieve good utility. Moreover, with bid fairness constraints, we construct a mechanism that simultaneously achieves a high utility and a strengthening of typical fairness constraints that we call total variation fairness. For the multiple category setting, we show that fairness relaxations are necessary to achieve good utility. We consider a relaxed definition based on user-specified category preferences that we call user-directed fairness, and we show that with this fairness notion a high utility is achievable. Finally, we show that our mechanisms in the single and multiple category settings compose well, yielding a high utility combined mechanism that satisfies user-directed fairness across categories and conditional total variation fairness within categories.
PUBLIC SERVICE SYSTEMS ON NETWORKS AND THEIR GRAPH THEORETICAL PROPERTIES

Many problems related to public service systems can be represented on complex networks. Knowledges from graph theory allow us to make computation of such problems more efficient. In this paper we pay attention on graph centers and medians, which are connected with location problems of emergency systems. We define an ecc/deg coefficient and ecc/deg set of a graph. We suppose that research of this graph theoretical topic could bring some new advances in the area of public service systems.
HappyBot: Generating Empathetic Dialogue Responses by Improving User Experience Look-ahead

Recent neural conversation models that attempted to incorporate emotion and generate empathetic responses either focused on conditioning the output to a given emotion, or incorporating the current user emotional state. While these approaches have been successful to some extent in generating more diverse and seemingly engaging utterances, they do not factor in how the user would feel towards the generated dialogue response. Hence, in this paper, we advocate such look-ahead of user emotion as the key to modeling and generating empathetic dialogue responses. We thus train a Sentiment Predictor to estimate the user sentiment look-ahead towards the generated system responses, which is then used as the reward function for generating more empathetic responses. Human evaluation results show that our model outperforms other baselines in empathy, relevance, and fluency.
Long time behavior of Levy-driven Ornstein-Uhlenbeck process with regime-switchin

In this work we investigate the long time behavior of the Ornstein-Uhlenbeck process driven by Levy noise with regime-switching. We provide explicit criteria on the transience and recurrence of this process. Contrasted with the Ornstein-Uhlenbeck process driven simply by Brownian motion, whose stationary distribution must be light-tailed, both the jumps caused by the Levy noise and regime-switching described by Markov chain can derive the heavy-tailed property of the stationary distribution. In this work, the different role played by Levy measure and regime-switching process is clearly characterized.
An observable time series based SLAM algorithm for deforming environment

In this paper, we study the back-end of simultaneous localization and mapping (SLAM) problem in deforming environment, where robot localizes itself and tracks multiple non-rigid soft surface using its onboard sensor measurements. An elaborate analysis is conducted on conventional deformation modelling method, Embedded Deformation (ED) graph. We demonstrate and prove that the ED graph widely used in such scenarios is unobservable and leads to multiple solutions unless suitable priors are provided. Example as well as theoretical prove are provided to show the ambiguity of ED graph and camera pose. In modelling non-rigid scenario with ED graph, motion priors of the deforming environment is essential to separate robot pose and deforming environment. The conclusion can be extrapolated to any free form deformation formulation. In solving the observability, this research proposes a preliminary deformable SLAM approach to estimate robot pose in complex environments that exhibits regular motion. A strategy that approximates deformed shape using a linear combination of several previous shapes is proposed to avoid the ambiguity in robot movement and rigid and non-rigid motions of the environment. Fisher information matrix rank analysis with a base case is discussed to prove the effectiveness. Moreover, the proposed algorithm is validated extensively on Monte Carlo simulations and real experiments. It is demonstrated that the new algorithm significantly outperforms conventional rigid SLAM and ED based SLAM especially in scenarios where there is large deformation.
Computing robot configurations using a genetic algorithm for multimodal optimization

This paper describes a general approach for robot inverse kinematic transformation, which computes all possible solutions of this multimodal problem. A function which computes the error between two tool center points (TCPs) is minimized by an advanced genetic algorithm. This multi-population genetic algorithm uses exchange and migration operators for computing all solutions of multimodal optimization problems.
Teacher-Student Compression with Generative Adversarial Networks

More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices. Teacher-student compression (TSC), also known as distillation, alleviates this burden by training a less expensive student model to mimic the expensive teacher model while maintaining most of the original accuracy. However, when fresh data is unavailable for the compression task, the teacher's training data is typically reused, leading to suboptimal compression. In this work, we propose to augment the compression dataset with synthetic data from a generative adversarial network (GAN) designed to approximate the training data distribution. Our GAN-assisted TSC (GAN-TSC) significantly improves student accuracy for expensive models such as large random forests and deep neural networks on both tabular and image datasets. Building on these results, we propose a comprehensive metric---the TSC Score---to evaluate the quality of synthetic datasets based on their induced TSC performance. The TSC Score captures both data diversity and class affinity, and we illustrate its benefits over the popular Inception Score in the context of image classification.
First-order covariance inequalities via Stein’s method

We propose probabilistic representations for inverse Stein operators (i.e. solutions to Stein equations) under general conditions; in particular we deduce new simple expressions for the Stein kernel. These representations allow to deduce uniform and non-uniform Stein factors (i.e. bounds on solutions to Stein equations) and lead to new covariance identities expressing the covariance between arbitrary functionals of an arbitrary {univariate} target in terms of a weighted covariance of the derivatives of the functionals. Our weights are explicit, easily computable in most cases, and expressed in terms of objects familiar within the context of Stein's method. Applications of the Cauchy-Schwarz inequality to these weighted covariance identities lead to sharp upper and lower covariance bounds and, in particular, weighted Poincare inequalities. Many examples are given and, in particular, classical variance bounds due to Klaassen, Brascamp and Lieb or Otto and Menz are corollaries. Connections with more recent literature are also detailed.
Companion Surface of Danger Cylinder and its Role in Solution Variation of P3P Problem

Traditionally the danger cylinder is intimately related to the solution stability in P3P problem. In this work, we show that the danger cylinder is also closely related to the multiple-solution phenomenon. More specifically, we show when the optical center lies on the danger cylinder, of the 3 possible P3P solutions, i.e., one double solution, and two other solutions, the optical center of the double solution still lies on the danger cylinder, but the optical centers of the other two solutions no longer lie on the danger cylinder. And when the optical center moves on the danger cylinder, accordingly the optical centers of the two other solutions of the corresponding P3P problem form a new surface, characterized by a polynomial equation of degree 12 in the optical center coordinates, called the Companion Surface of Danger Cylinder (CSDC). That means the danger cylinder always has a companion surface. For the significance of CSDC, we show that when the optical center passes through the CSDC, the number of solutions of P3P problem must change by 2. That means CSDC acts as a delimitating surface of the P3P solution space. These new findings shed some new lights on the P3P multi-solution phenomenon, an important issue in PnP study.
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.
Modeling C-shock Chemistry in Isolated Molecular Outflows

Shocks are a crucial probe for understanding the ongoing chemistry within ices on interstellar dust grains, where many complex organic molecules (COMs) are believed to be formed. However, previous work has been limited to the initial liberation into the gas phase through nonthermal desorption processes such as sputtering. Here we present results from the adapted three-phase gas-grain chemical network code nautilus, with the inclusion of additional high-temperature reactions, nonthermal desorption, collisional dust heating, and shock physics parameters. This enhanced model is capable of reproducing many of the molecular distributions and abundance ratios seen in our prior observations of the prototypical shocked outflow L1157. In addition, we find that, among others, NH2CHO, HCOOCH3, and CH3CHO have significant post-shock chemistry formation routes that differ from those of many other COMs observed in shocks. Finally, a number of selected species and phenomena are studied here with respect to their usefulness as shock tracers in various astrophysical sources.
Onset of inflation amid backreaction from inhomogeneities

We analyze the onset of inflation for a simple single-field model when the system begins with significant inhomogeneities on length-scales shorter than the initial Hubble radius. We incorporate certain nonlinear interactions among the coupled degrees of freedom by using the nonperturbative Hartree approximation. Consistent with recent, more computationally intensive numerical-relativity studies, we find inflation to be robust for large-field models, even when the system begins with significant structure on sub-Hubble scales. We consider the space of initial conditions $(\varphi (t_0), \dot{\varphi} (t_0))$, where $\varphi$ is the vacuum expectation value of the quantized field that drives inflation. Although some regions of $(\varphi (t_0), \dot{\varphi} (t_0))$ that would have yielded sufficient inflation in the absence of inhomogeneities fail to do so when backreaction from inhomogeneities is incorporated, an equal volume of such regions succeeds in producing sufficient inflation which did not do so in the absence of inhomogeneities. For large-field models, in other words, the total volume of the space of initial conditions $(\varphi (t_0), \dot{\varphi} (t_0))$ that yields sufficient inflation is {\it conserved} when we incorporate nonlinear backreaction from inhomogeneities, compared to the case in which inhomogeneities are neglected.
X-ray extinction from interstellar dust

Aims. We present a study on the prospects of observing carbon, sulfur, and other lower abundance elements (namely Al, Ca, Ti, and Ni) present in the interstellar medium using future X-ray instruments. We focus in particular on the detection and characterization of interstellar dust along the lines of sight. Methods. We compared the simulated data with different sets of dust aggregates, either obtained from past literature or measured by us using the SOLEIL-LUCIA synchrotron beamline. Extinction by interstellar grains induces modulations of a given photolelectric edge, which can be in principle traced back to the chemistry of the absorbing grains. We simulated data of instruments with characteristics of resolution and sensitivity of the current Athena, XRISM, and Arcus concepts. Results. In the relatively near future, the depletion and abundances of the elements under study will be determined with confidence. In the case of carbon and sulfur, the characterization of the chemistry of the absorbing dust will be also determined, depending on the dominant compound. For aluminum and calcium, despite the large depletion in the interstellar medium and the prominent dust absorption, in many cases the edge feature may not be changing significantly with the change of chemistry in the Al- or Ca-bearing compounds. The exinction signature of large grains may be detected and modeled, allowing a test on different grain size distributions for these elements. The low cosmic abundance of Ti and Ni will not allow us a detailed study of the edge features.