Miracle-dz/UniFAR-FTUs huggingface dataset:
Late Toxicity After 3-Dimensional External Beam Radiotherapy Among Children With Cancer: A Systematic Review

Radiotherapy has evolved from 2-dimensional conventional radiotherapy (2D-RT) to 3-dimensional planned radiotherapy (3D-RT). Because 3D-RT improves conformity, an altered late health outcomes risk profile is anticipated. Here, we systematically reviewed the current literature on late toxicity after 3D-RT in children treated for cancer. PubMed was searched for studies describing late toxicity after 3D-RT for childhood cancer (below 21 y). Late toxicity was defined as somatic health outcomes occurring ≥90 days after treatment. We identified 13 eligible studies, describing most frequently head/neck area tumors. Included studies reported on crude frequencies of late toxicities including subsequent tumors and conditions of organ systems. Three studies offered a global assessment of the full spectrum of late toxicity; one study compared toxicities after 2D-RT and 3D-RT. Incidence rates were typically not provided. Heterogeneity in study characteristics, small study sizes and short follow-up times precluded multivariable modeling and pooling of data. In conclusion, among the first pediatric cohorts treated with 3D-RT, a broad variety of late toxicity is reported; precise estimates of incidence, and contributions of risk factors are unclear. Continued systematic evaluation of well-defined health outcomes in survivors treated with 3D-RT, including proton therapy, is needed to optimize evidence-based care for children with cancer and survivors.
Toxicity, preparation methods and applications of silver nanoparticles: an update

Abstract Nanoparticles (range under 100 nm) prepared by different technology modes including physical, chemical, biological have many applications. Like in the same way silver nanoparticles are used for different beneficial actions like antimicrobial- antibacterial, antifungal and antiviral, anti-inflammatory, anticancer, water treatment, cosmetics, and in the textiles industry. As silver nanoparticles have shown wide application by different mechanisms against various pathophyisiological conditions. To maintain safety under their use, the study of the toxicity of silver nanoparticles has become more important. Health agencies like WHO, NIOSH, EPA, EFSA & EU have issued guidelines for unrisky exposure limit of silver nanopartricles in drinking water, food and breathing. The main purpose of this article is to summarize genotoxicity, cytotoxicity, neurotoxicity, reproductive toxicity of silver nanoparticles in both in vitro and in vivo studies focused on mechanism and methods of detection. The main mechanism of silver nanoparticles toxicity involves disruption of the mitochondrial respiratory chain, which results in the generation of ROS and the stoppage of ATP synthesis which further leads to a cascade of toxic events. ROS production measured by the technique like flow cytometry using DCFHDA dye and other method includes a confocal microscope, lipid peroxidation, etc. Different assay techniques used for evaluation of different kind of toxicities such as the comet assay, MTT assay, and histological assay, are also discussed.
Expressiveness and Approximation Properties of Graph Neural Networks

Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are, however, usually geared at specific GNN architectures, and tools for understanding arbitrary GNN architectures are generally lacking. We provide an elegant way to easily obtain bounds on the separation power of GNNs in terms of the Weisfeiler-Leman (WL) tests, which have become the yardstick to measure the separation power of GNNs. The crux is to view GNNs as expressions in a procedural tensor language describing the computations in the layers of the GNNs. Then, by a simple analysis of the obtained expressions, in terms of the number of indexes and the nesting depth of summations, bounds on the separation power in terms of the WL-tests readily follow. We use tensor language to define Higher-Order Message-Passing Neural Networks (or k-MPNNs), a natural extension of MPNNs. Furthermore, the tensor language point of view allows for the derivation of universality results for classes of GNNs in a natural way. Our approach provides a toolbox with which GNN architecture designers can analyze the separation power of their GNNs, without needing to know the intricacies of the WL-tests. We also provide insights in what is needed to boost the separation power of GNNs.
NAN: Noise-Aware NeRFs for Burst-Denoising

Burst denoising is now more relevant than ever, as computational photography helps overcome sensitivity issues inherent in mobile phones and small cameras. A major challenge in burst-denoising is in coping with pixel misalignment, which was so far handled with rather simplistic assumptions of simple motion, or the ability to align in pre-processing. Such assumptions are not realistic in the presence of large motion and high levels of noise. We show that Neural Radiance Fields (NeRFs), originally suggested for physics-based novel-view rendering, can serve as a powerful framework for burst denoising. NeRFs have an inherent capability of handling noise as they integrate information from multiple images, but they are limited in doing so, mainly since they build on pixel-wise operations which are suitable to ideal imaging conditions. Our approach, termed NAN111Refer to the project website: noise-aware-nerf.github. io, leverages inter-view and spatial information in NeRFs to better deal with noise. It achieves state-of-the-art results in burst denoising and is especially successful in coping with large movement and occlusions, under very high levels of noise. With the rapid advances in accelerating NeRFs, it could provide a powerful platform for denoising in challenging environments.
An approach to improving sound-based vehicle speed estimation

We consider improving the performance of a recently proposed sound-based vehicle speed estimation method. In the original method, an intermediate feature, referred to as the modified attenuation (MA), has been proposed for both vehicle detection and speed estimation. The MA feature maximizes at the instant of the vehicle's closest point of approach, which represents a training label extracted from video recording of the vehicle's pass by. In this paper, we show that the original labeling approach is suboptimal and propose a method for label correction. The method is tested on the VS10 dataset, which contains 304 audio-video recordings of ten different vehicles. The results show that the proposed label correction method reduces average speed estimation error from 7.39 km/h to 6.92 km/h. If the speed is discretized into 10 km/h classes, the accuracy of correct class prediction is improved from 53.2% to 53.8%, whereas when tolerance of one class offset is allowed, accuracy is improved from 93.4% to 94.3%.
On the"old"and"new"relativistic wave equations for the particle having spin s=3/2

Relativistic wave equation of motion without redundant components for the particle having spin 3 / 2 has been considered. In order to show the newness a comparison with the known equations for the spin s =3/2 ﬁeld is given. Therefore, the brief review of the relativistic wave equations for the particle with spin s =3/2 is suggested. In our equation the wave function for the particle-antiparticle doublet contains only 8 components. The consideration is carried out both at the level of relativistic quantum mechanics and at the level of local ﬁeld theory. The extended Foldy– Wouthuysen transformation, which gives the operator link between these two levels is suggested.
Closing the Gap between Weighted and Unweighted Matching in the Sliding Window Model

We consider the Maximum-weight Matching ( MWM ) problem in the streaming sliding window model of computation. In this model, the input consists of a sequence of weighted edges on a given vertex set V of size n . The objective is to maintain an approximation of a maximum-weight matching in the graph spanned by the L most recent edges, for some integer L , using space ˜ O ( n ). Crouch et al. [ESA’13] gave a (3 + ε )-approximation for (unweighted) Maximum Matching ( MM ), and, very recently, Biabani et al. [ISAAC’21] gave a (3 . 5 + ε )-approximation for MWM . In this paper, we give a (3+ ε )-approximation for MWM , thereby closing the gap between MWM and MM . Biabani et al.’s work makes use of the smooth histogram technique introduced by Braverman and Ostrovsky [FOCS’07]. Rather than designing sliding window algorithms directly, this technique reduces the problem to designing so-called lookahead algorithms that have certain smoothness properties. Biabani et al. showed that the one-pass MWM streaming algorithm by Paz and Schwartzman [SODA’17] constitutes a lookahead algorithm with approximation factor 3 . 5 + ε , which yields their result. We ﬁrst give a hard instance, showing that Paz and Schwartzman’s algorithm is indeed no better than a 3 . 5-approximation lookahead algorithm, which implies that Biabani et al.’s analysis is tight. To obtain our improvement, we give an alternative and more complex deﬁnition of lookahead algorithms that still maintains the connection to the sliding window model. Our new deﬁnition, however, reﬂects further smoothness properties of Paz and Schwartzman’s algorithm, which we exploit in order to improve upon Biabani et al.’s analysis,
The PartialSpoof Database and Countermeasures for the Detection of Short Generated Audio Segments Embedded in a Speech Utterance

—Automatic speaker veriﬁcation is susceptible to var- ious manipulations and spooﬁng, such as text-to-speech (TTS) synthesis, voice conversion (VC), replay, tampering, adversarial attacks, and so on. In this paper, we consider a new spooﬁng scenario called “Partial Spoof” (PS) in which synthesized or transformed audio segments are embedded into a bona ﬁde speech utterance. While existing countermeasures (CMs) can detect fully spoofed utterances where entire audio signals are generated through the application of TTS and/or VC algorithms, there is a need for their adaptation or extension to the PS scenario to detect utterances in which only a part of the audio signal is generated using TTS or VC algorithms and hence only a fraction of an utterance is spoofed. For improved explainability, such new CMs should ideally also be able to detect such short spoofed segments. Our previous study introduced the ﬁrst version of a speech database suitable for training CMs for the PS scenario and showed that, although it is possible to train CMs to execute the two types of detection described above, there is much room for improvement.Inthispaperwe propose various improvements to construct a signiﬁcantly more accurate CM that can detect short generated spoofed audio segments at ﬁner temporal resolutions. First, we introduce newly proposed self-supervised pre-trained models as enhanced feature extractors. Second, we extend the PartialSpoof database by adding segment labels for various temporal resolu- tions. Since the short spoofed audio segments to be embedded by attackers are of variable length, six different temporal resolutions are considered, ranging from as short as 20 ms to as large as 640 ms. Third, we propose a new CM and training strategies that enable the simultaneous use of the segment-level labels at different temporal resolutions as well as utterance-level labels to execute the above two types of detection at the same time. We also show that the proposed CM is capable of detecting spooﬁng at the utterance level with low error rates, not only in the PS scenario but also in a related logical access (LA) scenario. The equal error rates (EERs) of utterance-level detection on the PartialSpoof database and the ASVspoof 2019 LA database were 0.47% and 0.59%, respectively.
Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization

In recent years, the use of WiFi fingerprints for indoor positioning has grown in popularity, largely due to the widespread availability of WiFi and the proliferation of mobile communication devices. However, many existing methods for constructing fingerprint data sets rely on labor-intensive and time-consuming processes of collecting large amounts of data. Additionally, these methods often focus on ideal laboratory environments, rather than considering the practical challenges of large multifloor buildings. To address these issues, we present a novel WiFi domain adversarial graph convolutional network model that can be trained using a small number of labeled site survey data and large amounts of unlabeled crowdsensed WiFi fingerprints. By constructing heterogeneous graphs based on received signal strength indicators (RSSIs) between waypoints and WiFi access points (APs), our model is able to effectively capture the topological structure of the data. We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies. To deal with the challenges of large amounts of unlabeled data and multiple data domains, we employ a semisupervised domain adversarial training scheme to effectively utilize unlabeled data and align the data distributions across domains. Our system is evaluated using a public indoor localization data set that includes multiple buildings, and the results show that it performs competitively in terms of localization accuracy in large buildings.
Learning-Based Bounded Synthesis for Semi-MDPs With LTL Specifications

This letter proposes a learning-based bounded synthesis for a semi-Markov decision process (SMDP) with a linear temporal logic (LTL) specification. In the product of the SMDP and the deterministic <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-co-Büchi automaton (<inline-formula> <tex-math notation="LaTeX">$\text{d}K$ </tex-math></inline-formula>cBA) converted from the LTL specification, we learn both the winning region of satisfying the LTL specification and the dynamics therein based on reinforcement learning and Bayesian inference. Then, we synthesize an optimal policy satisfying the following two conditions. (1) It maximizes the probability of reaching the wining region. (2) It minimizes a long-term risk for the dwell time within the winning region. The minimization of the long-term risk is done based on the estimated dynamics and a value iteration. We show that, if the discount factor is sufficiently close to one, the synthesized policy converges to the optimal policy as the number of the data obtained by the exploration goes to the infinity.
Supplementary for Paper: Learning Pixel-Level Distinctions for Video Highlight Detection

We take an encoder-decoder structure for implementation. The encoder net can be constituted by any 3D Convolution Network (such as C3D [6], S3D [8] and etc.), while the component of decoder net differs from different encoder net in order to obtain the target highlight frame as same size as the input frame. We choose the input clip consisting by continuous frames at random to enjoy a global distribution. In our experiments, we implement the encoder-decoder structure with backbone TASED-Net [3]. The size of input and target frame is 384 × 224. At training stage, it takes TASED-Net pretrained on DHF1K [7] as the saliency mask generator to be the auxiliary spatial module, the encoder net and decoder net are initialized with parameters pretrained on Kinetics [1]. While in inference stage, it only need the encoder and decoder net. We resize the input frame by 384 × 224 and set the number of input frames as 32 to compose an input video clip. Finally, it takes approximately six hours for the TASED-Net implementation. For the model we takes in ablation study Ours(C3D) in YouTube Highlight dataset, we take the C3D [6] as the encoder net, while the decoder net contains four ConvTransposed Layers aiming to generate the same size 112 × 112 as the input target frame. For Ours(C3D), the number of frames as input clip is 16 and we resize the frames as 112 × 112, and it takes three hours for training for Ours(C3D). For the hyper parameters in our model (i.e. we set β to be the threshold of the highlight map to generate the pseudo label). In YouTube Highlight dataset [5], TvSum [4] and CoSum [2], we simply set the value of β as 0.0005, especially for certain domain(dog, gymnastics, skiing) in YouTube Highlight dataset as 0.001.
The Importance of Future Information in Credit Card Fraud Detection

Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospec-tively and avoid further frauds. Since human verification is often necessary and the payment processing time is limited, the second task manages the largest volume of transactions. In the literature, fraud detection challenges and algorithms performance are widely studied but the very formulation of the problem is never disrupted: it aims at predicting if a transaction is fraudulent based on its characteristics and the past transactions of the cardholder. Yet, in posterior detection, verification often takes days, so new payments on the card become available before a decision is taken. This is our motivation to propose a new paradigm: posterior fraud detection with “future” information. We start by providing evidence of the on-time availability of subsequent transactions, usable as extra context to improve detection. We then design a Bidirectional LSTM to make use of these transactions. On a real-world dataset with over 30 million transactions, it achieves higher performance than a regular LSTM, which is the state-of-the-art classifier for fraud detection that only uses the past context. We also introduce new metrics to show that the proposal catches more frauds, more compromised cards, and based on their earliest frauds. We believe that fu-Proceedings ture works on this new paradigm will have a significant impact on the detection of compromised cards.
The relation between the radio emission of the core and host galaxy properties in Fanaroff–Riley type II radio galaxies

Abstract We study the radio power of the core and its relation to the optical properties of the host galaxy in samples of high-excitation (HERG) and low-excitation (LERG) Fanaroff–Riley type II (FRII) radio galaxies. The radio galaxy sample is divided into two groups of core/non-core FRII, based on the existence of strong, weak or lack of single radio core component. We show that FRII LERGs with radio emission of the core have significantly higher [O III] line luminosities compared to the non-core LERG FRIIs. There is no significant difference between the hosts of the core and non-core FRIIs of LERG type in galaxy sizes, concentration indices, star formation rates, 4000-Å break strengths, colours, black hole masses, and black hole to stellar masses. We show that the results are not biased by the stellar masses, redshifts, and angular sizes of the radio galaxies. We argue that the detection of higher [O III] luminosities in the core FRIIs may indicate the presence of higher amounts of gas, very close to the active galactic nuclei (AGN) nucleus in the core FRIIs compared to the non-core FRIIs or may result from the interaction of the radio jets with this gas. The core and non-core FRIIs of the HERG type show no significant differences perhaps due to our small sample size. The effect of relativistic beaming on the radio luminosities and the contribution of restating AGN activity have also been considered.
Defect Identification, Categorization, and Repair: Better Together

Just-In-Time defect prediction (JIT-DP) models can identify defect-inducing commits at check-in time. Even though previous studies have achieved a great progress, these studies still have the following limitations: 1) useful information (e.g., semantic information and structure information) are not fully used; 2) existing work can only predict a commit as buggy one or clean one without more information about what type of defect it is; 3) a commit may involve changes in many files, which cause difficulty in locating the defect; 4) prior studies treat defect identification and defect repair as separate tasks, none aims to handle both tasks simultaneously. In this paper, to handle aforementioned limitations, we propose a comprehensive defect prediction and repair framework named CompDefect, which can identify whether a changed function (a more fine-grained level) is defect-prone, categorize the type of defect, and repair such a defect automatically if it falls into several scenarios, e.g., defects with single statement fixes, or those that match a small set of defect templates. Generally, the first two tasks in CompDefect are treated as a multiclass classification task, while the last one is treated as a sequence generation task. The whole input of CompDefect consists of three parts (exampled with positive functions): the clean version of a function (i.e., the version before defect introduced), the buggy version of a function and the fixed version of a function. In multiclass classification task, CompDefect categorizes the type of defect via multiclass classification with the information in both the clean version and the buggy version. In code sequence generation task, CompDefect repairs the defect once identified or keeps it unchanged.
E^2TAD: An Energy-Efficient Tracking-based Action Detector

Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the"who-when-where-what"level. This paper presents a tracking-based solution to accurately and efficiently localize predefined key actions spatially (by predicting the associated target IDs and locations) and temporally (by predicting the time in exact frame indices). This solution won first place in the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC).
A reflection equivalence for Gorenstein-projective quiver representations

Abstract: For Λ a selfinjective algebra, and Q a finite quiver without oriented cycles, the algebra ΛQ is a Gorenstein algebra and the category G-projΛQ of Gorenstein-projective ΛQ-modules is a Frobenius category. For a sink v of Q, we define a functor F (v) : G-projΛQ → G-projΛQ(v) between the stable categories modulo projectives, where Q(v) is obtained from Q by changing the direction of each arrow ending in v. The functor is given by an explicit construction on the level of objects and homomorphisms. Our main result states that F (v) is an equivalence of categories. In the case where the underlying graph of Q is a tree, we deduce that the stable category G-projΛQ does not depend on the orientation of Q. Moreover, if Q is a quiver of type A3 and Λ = k[T ]/(T n) the bounded polynomial algebra, we use the symmetry of the octahedron in the octahedral axiom to verify that the composition of twelve reflections yields the identity on objects.