Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation

The irregular geometry and high inter-slice variability in computerized tomography (CT) scans of the human pancreas make an accurate segmentation of this crucial organ a challenging task for existing data-driven deep learning methods. To address this problem, we present a novel model-driven stack-based fully convolutional network with a sliding window fusion algorithm for pancreas segmentation, termed MDS-Net. The MDS-Net’s cost function includes a data approximation term and a prior knowledge regularization term combined with a stack scheme for capturing and fusing the two-dimensional (2D) and local three-dimensional (3D) context information. Specifically, 3D CT scans are divided into multiple stacks to capture the local spatial context feature. To highlight the importance of single slices, the inter-slice relationships in the stack data are also incorporated in the MDS-Net framework. For implementing this proposed model-driven method, we create a stack-based U-Net architecture and successfully derive its back-propagation procedure for end-to-end training. Furthermore, a sliding window fusion algorithm is utilized to improve the consistency of adjacent CT slices and intra-stack. Finally, extensive quantitative assessments on the NIH Pancreas-CT dataset demonstrated higher pancreatic segmentation accuracy and reliability of MDS-Net compared to other state-of-the-art methods.
The widest Hα survey of accreting protoplanets around nearby transition disks

Context. The mechanisms of planet formation are still under debate. We know little about how planets form, even if more than 4000 exoplanets have been detected to date. Recent investigations target the cot of newly born planets: the protoplanetary disk. At the first stages of their life, exoplanets still accrete material from the gas-rich disk in which they are embedded. Transitional disks are indeed disks that show peculiarities, such as gaps, spiral arms, and rings, which can be connected to the presence of substellar companions. Aims. To investigate what is responsible for these features, we selected all the known transitional disks in the solar neighborhood (<200 pc) that are visible from the southern hemisphere. We conducted a survey of 11 transitional disks with the SPHERE instrument at the Very Large Telescope. This is the largest Hα survey that has been conducted so far to look for protoplanets. The observations were performed with the Hα filter of ZIMPOL in order to target protoplanets that are still in the accretion stage. All the selected targets are very young stars, less than 20 Myr, and show low extinction in the visible. Methods. We reduced the ZIMPOL pupil stabilized data by applying the method of the angular spectral differential imaging (ASDI), which combines both techniques. The datacubes are composed of the Cnt_Hα and the narrow band filter Hα, which are taken simultaneously to permit the suppression of the speckle pattern. The principal component analysis method was employed for the reduction of the data. For each dataset, we derived the 5σ contrast limit and converted it in upper limits on the accretion luminosity. Results. We do not detect any new accreting substellar companions around the targeted transition disks down to an average contrast of 12 magnitudes at 0. ′′2 from the central star. We have recovered the signal of the accreting M star companion around the star HD 142527. We have detected and resolved, for the first time in visible light, the quadruple system HD 98800. For every other system, we can exclude the presence of massive actively accreting companions, assuming that the accretion is not episodic and that the extinction is negligible. The mean accretion luminosity limit is 10−6 L⊙ at a separation of 0. ′′2 from the host.
LightFDG: An Integrated Approach to Flow Detection and Grooming in Optical Wireless DCNs

LightFDG is an integrated approach to flow detection (FD) and flow grooming (FG) in optical wireless data center networks (DCNs), which is interconnected via wavelength division multiplexing (WDM) based free-space optical (FSO) links. Since forwarding bandwidth-hungry elephant flows (EFs) and delay-sensitive mice flows (MFs) on the same path can cause severe performance degradation, the LightFDG optically grooms flows of each class into rack-to-rack (R2R) flows. Then, R2R-MF and R2R-EF flows are separately forwarded over lightpaths of separate MF and EF virtual topologies, respectively. Lightpaths are provisioned by jointly determining the capacity and route based on flows’ arrival rate, size, and completion time request. To prevent EFs from congesting the MF lightpaths, high speed and accurate flow-detection mechanisms are also necessary for classifying EFs as soon as possible. Therefore, a fast-lightweight-and-accurate flow detection framework is developed by leveraging the transmission control protocol (TCP) behaviors. The proposed FD scheme has the flexibility of being implemented as in-network or centralized to classify flows of modifiable and unmodifiable hosts, respectively. Since the centralized scheme incurs considerable overhead, the processing and communication overhead is also mitigated by proposed techniques. Numerical results show that LightFDG outperforms traditional load balancers by about <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> for EFs and <inline-formula> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> for MFs. Along with the developed overhead mitigation methods, the centralized scheme is shown to provide up to <inline-formula> <tex-math notation="LaTeX">$62\times $ </tex-math></inline-formula> lower overhead with 100% accuracy and with about <inline-formula> <tex-math notation="LaTeX">$224\times $ </tex-math></inline-formula> higher detection speeds than the existing centralized solutions.
The Aarhus red giants challenge

Contact. The large quantity of high-quality asteroseismic data that have been obtained from space-based photometric missions and the accuracy of the resulting frequencies motivate a careful consideration of the accuracy of computed oscillation frequencies of stellar models, when applied as diagnostics of the model properties. Aims. Based on models of red-giant stars that have been independently calculated using different stellar evolution codes, we investigate the extent to which the differences in the model calculation affect the model oscillation frequencies and other asteroseismic diagnostics. Methods. For each of the models, which cover four different masses and different evolution stages on the red-giant branch, we computed full sets of low-degree oscillation frequencies using a single pulsation code and, from these frequencies, typical asteroseismic diagnostics. In addition, we carried out preliminary analyses to relate differences in the oscillation properties to the corresponding model differences. Results. In general, the differences in asteroseismic properties between the different models greatly exceed the observational precision of these properties. This is particularly true for the nonradial modes whose mixed acoustic and gravity-wave character makes them sensitive to the structure of the deep stellar interior and, hence, to details of their evolution. In some cases, identifying these differences led to improvements in the final models presented here and in Paper I; here we illustrate particular examples of this. Conclusions. Further improvements in stellar modelling are required in order fully to utilise the observational accuracy to probe intrinsic limitations in the modelling and improve our understanding of stellar internal physics. However, our analysis of the frequency differences and their relation to stellar internal properties provides a striking illustration of the potential, in particular, of the mixed modes of red-giant stars for the diagnostics of stellar interiors.
Human Electromagnetic Field Exposure in Wearable Communications: A Review

The concern on human health is often overseen while wearable technologies attract exploding interests. Mainly due to the extreme proximity or a direct physical contact to the human skin, wearable communications devices are acknowledged to cause higher levels of specific absorption rate (SAR) at the skin surface. Unfortunately, so far, we have found no study encompassing all the aspects that the general public needs to understand about wearable technologies--i.e., the analytical and experimental backgrounds, and report of SAR levels generated from commercial wearable devices. In this context, this paper provides an extensive review on SAR from various commercial wearable devices that are currently sold in the market, as well as the analytical framework and the current measurement methodologies for standard compliance tests. Moreover, considering the present interest in millimeter wave (mmW), this paper sheds light on the SAR evaluated at 60 GHz and also compares the SAR to that measured at 2.4 GHz. We expect that this paper will be of value in informing the general public of the safety in using the currently sold wearable devices, and in igniting further study of the exact biological consequences from electromagnetic field (EMF) exposure due to wearable devices.
A Complete Classification of 3-dimensional Quadratic AS-regular Algebras of Type EC

Abstract Classification of AS-regular algebras is one of the main interests in noncommutative algebraic geometry. We say that a $3$-dimensional quadratic AS-regular algebra is of Type EC if its point scheme is an elliptic curve in $\mathbb {P}^{2}$. In this paper, we give a complete list of geometric pairs and a complete list of twisted superpotentials corresponding to such algebras. As an application, we show that there are only two exceptions up to isomorphism among all $3$-dimensional quadratic AS-regular algebras that cannot be written as a twist of a Calabi–Yau AS-regular algebra by a graded algebra automorphism.
Flow Rate Control in Smart District Heating Systems Using Deep Reinforcement Learning

At high latitudes, many cities adopt a centralized heating system to improve the energy generation efficiency and to reduce pollution. In multi-tier systems, so-called district heating, there are a few efficient approaches for the flow rate control during the heating process. In this paper, we describe the theoretical methods to solve this problem by deep reinforcement learning and propose a cloud-based heating control system for implementation. A real-world case study shows the effectiveness and practicability of the proposed system controlled by humans, and the simulated experiments for deep reinforcement learning show about 1985.01 gigajoules of heat quantity and 42276.45 tons of water are saved per hour compared with manual control.
Modeling Dust and Starlight in Galaxies Observed by Spitzer and Herschel: The KINGFISH Sample

Interstellar dust and starlight are modeled for the galaxies of the project “Key Insights on Nearby Galaxies: A Far-Infrared Survey with Herschel. ” The galaxies were observed by the Infrared Array Camera and the Multiband Imaging Photometer for Spitzer on Spitzer Space Telescope, and the Photodetector Array Camera and Spectrometer and the Spectral and Photometric Imaging Receiver on Herschel Space Observatory. With data from 3.6 to 500 μm, dust models are strongly constrained. Using a physical dust model, for each pixel in each galaxy we estimate (1) dust surface density, (2) dust mass fraction in polycyclic aromatic hydrocarbons (PAHs), (3) distribution of starlight intensities heating the dust, (4) total infrared (IR) luminosity emitted by the dust, and (5) IR luminosity originating in subregions with high starlight intensity. The dust models successfully reproduce the observed global and resolved spectral energy distributions. With the angular resolution of Herschel, we obtain well-resolved maps (available online) for the dust properties. As in previous studies, we find the PAH fraction to be an increasing function of metallicity, with a threshold oxygen abundance Z/Z⊙ ≈ 0.1, but we find the data to be fitted best with increasing linearly with above a threshold value of 0.15(O/H)⊙. We obtain total dust masses for each galaxy by summing the dust mass over the individual map pixels; these “resolved” dust masses are consistent with the masses inferred from a model fit to the global photometry. The global dust-to-gas ratios obtained from this study are found to correlate with galaxy metallicities. Systems with Z/Z⊙ ≳ 0.5 have most of their refractory elements locked up in dust, whereas in systems with Z/Z⊙ ≲ 0.3 most of these elements tend to remain in the gas phase. Within galaxies, we find that is suppressed in regions with unusually warm dust with . With knowledge of one long-wavelength flux density ratio (e.g., f160/f500), the minimum starlight intensity heating the dust ( ) can be estimated to within ∼50%, despite a variation in of more than two orders of magnitude. For the adopted dust model, dust masses can be estimated to within ∼0.2 dex accuracy using the f160/f500 flux ratio and the integrated dust luminosity, and to ∼0.07 dex accuracy using the 500 μm luminosity alone. There are additional systematic errors arising from the choice of dust model, but these are hard to estimate. These calibrated prescriptions for estimating starlight heating intensity and dust mass may be useful for studies of high-redshift galaxies.
Detecting and Correcting Adversarial Images Using Image Processing Operations

Deep neural networks (DNNs) have achieved excellent performance on several tasks and have been widely applied in both academia and industry. However, DNNs are vulnerable to adversarial machine learning attacks, in which noise is added to the input to change the network output. We have devised two methods for detecting adversarial images; one based on statistical image processing and one based on convolutional neural network in which the final softmax layer is removed during training. In addition to detection, the image-processing-based method can be used to reduce adversarial noise in images and thereby restore the image labels, which is crucial to restoring the normal functionalities of DNN-based systems. Testing using an adversarial machine learning database we created for generating several types of attack using images from the ImageNet Large Scale Visual Recognition Challenge database demonstrated the efficiency of our proposed methods for both detection and correction even when training was done from scratch on a small database.
Sparse Joint Transmission for Cell-Free Massive MIMO: A Sparse PCA Approach

Cell-free massive multiple-input multiple-output (MIMO) is a promising cellular network. In this network, a large number of distributed and multi-antenna access points (APs) jointly serve many single antenna users using the same time-frequency resource. Consequently, it possibly provides a uniform service experience to users regardless of the users' locations by eliminating interference at cell boundaries via user-centric joint transmission. This joint transmission, however, requires extremely high signaling overheads for data sharing via backhaul links and causes a high network-wide power consumption. To resolve these problems, in this paper, we present a novel joint transmission method, which is referred to as sparse joint transmission (sparse-JT), for cell-free massive MIMO networks with finite backhaul capacity constraints. Sparse-JT jointly identifies the user-centric cooperative APs sets, precoding vectors for beamforming and compression, and power allocation that maximizes a lower bound of the sum-spectral efficiency under the constraint that a total number of active APs per the joint transmission is sparse. The proposed algorithm guarantees to identify a local-optimal solution for a relaxed sum-spectral maximization problem. By simulations, we show that sparse-JT achieves higher ergodic spectral efficiencies than those attained by multi-cell zero-forcing precoding with the user-centric AP clustering algorithm in all system configurations.
PuckNet: Estimating hockey puck location from broadcast video

Puck location in ice hockey is essential for hockey analysts for determining the location of play and analyzing game events. However, because of the difficulty involved in obtaining accurate annotations due to the extremely low visibility and commonly occurring occlusions of the puck, the problem is very challenging. The problem becomes even more challenging in broadcast videos with changing camera angles. We introduce a novel methodology for determining puck location from approximate puck location annotations in broadcast video. Our method uniquely leverages the existing puck location information that is publicly available in existing hockey event data and uses the corresponding one-second broadcast video clips as input to the network. The rationale behind using video as input instead of static images is that with video, the temporal information can be utilized to handle puck occlusions. The network outputs a heatmap representing the probability of the puck location using a 3D CNN based architecture. The network is able to regress the puck location from broadcast hockey video clips with varying camera angles. Experimental results demonstrate the capability of the method, achieving 47.07% AUC on the test dataset. The network is also able to estimate the puck location in defensive/offensive zones with an accuracy of greater than 80%.
Source Camera Attribution from Strongly Stabilized Videos

The in-camera image stabilization technology deployed by most cameras today poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires the inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations. Our method identifies transformations at a sub-frame level and incorporates a number of constraints to validate their correctness. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on a public dataset of stabilized videos show that the proposed method improves attribution rate over existing methods by 17-19\% without a significant impact on false attribution rate.
A Reference Architecture and Modelling Principles for Architectural Stability based on Self-Awareness: Case of Cloud Architectures

With the increased dependence on software, there is a pressing need for engineering long-lived software. As architectures have a profound effect on the life-span of the software and the provisioned quality of service, stable architectures are significant assets. Architectural stability tends to reflect the success of the system in supporting continuous changes without phasing-out. The \textit{behavioural} aspect of stability is essential for seamless operation, to continuously keep the provision of quality requirements stable and prevent architecture's drifting and phasing-out. In this paper, we introduce a reference architecture and model for stability. Specifically, we leverage on the self-awareness principles and runtime goals modelling to explicitly support architectural stability. To illustrate the applicability and evaluate the proposed approach, we consider the case of cloud architectures. The experimental results show that our approach increases the efficiency of the architecture in keeping the expected behaviour stable during runtime operation.
Interactive AI with a Theory of Mind

Understanding each other is the key to success in collaboration. For humans, attributing mental states to others, the theory of mind, provides the crucial advantage. We argue for formulating human--AI interaction as a multi-agent problem, endowing AI with a computational theory of mind to understand and anticipate the user. To differentiate the approach from previous work, we introduce a categorisation of user modelling approaches based on the level of agency learnt in the interaction. We describe our recent work in using nested multi-agent modelling to formulate user models for multi-armed bandit based interactive AI systems, including a proof-of-concept user study.
Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field‐to‐source inversion. Current state‐of‐the‐art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill‐posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning‐based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.
Multimodal Generative Models for Compositional Representation Learning

As deep neural networks become more adept at traditional tasks, many of the most exciting new challenges concern multimodality---observations that combine diverse types, such as image and text. In this paper, we introduce a family of multimodal deep generative models derived from variational bounds on the evidence (data marginal likelihood). As part of our derivation we find that many previous multimodal variational autoencoders used objectives that do not correctly bound the joint marginal likelihood across modalities. We further generalize our objective to work with several types of deep generative model (VAE, GAN, and flow-based), and allow use of different model types for different modalities. We benchmark our models across many image, label, and text datasets, and find that our multimodal VAEs excel with and without weak supervision. Additional improvements come from use of GAN image models with VAE language models. Finally, we investigate the effect of language on learned image representations through a variety of downstream tasks, such as compositionally, bounding box prediction, and visual relation prediction. We find evidence that these image representations are more abstract and compositional than equivalent representations learned from only visual data.