Miracle-dz/UniFAR-FTUs huggingface dataset:
IRF7 impacts on prostate cancer cell survival in response to radiation

Understanding the impact of radiotherapy on the evolution of treatment resistant prostate cancer is critical for selecting effective treatment combinations. Whilst activation of Type 1 interferon signalling is a hallmark of how cells respond to viral infection, in cancer cells, multiple stresses are known to activate this same response. In this study we have evaluated for the first time the changes in the interferon response induced by culturing prostate cancer cells under sphere- forming conditions and following irradiation. We report a conserved upregulated transcript profile for both conditions that is strongly associated with therapeutic resistance and cell survival in vitro and in vivo. The profile includes and is regulated by the Type 1 interferon master regulator IRF7 which, when depleted, delays tumour re-growth following irradiation. We immuno-stained two independent prostate cohorts for IRF7 and found that increased expression, particularly in cases with low PTEN expression, correlated with poor prognosis. To more comprehensively characterise the impact of IRF7 and radiation on cells, RNA-Seq was performed on IRF7 knockdown cells at different radiation doses. We identified a number of biological processes that were IRF7-dependent, including the formation of stem-like cell populations and also therapeutic vulnerabilities. For example, irradiation sensitised surviving cells to either a combination of an IKKε/TBK1 and a MEK inhibitor or treatment with an inhibitor of IDO1, an IRF7- dependent gene. Translationally our work suggests that IRF7 expression can be used to stratify patients who may not benefit from receiving radiotherapy alone but rather may benefit from treatment combinations. In two cohorts treated with radical intent, strong IRF7 staining was associated with disease-specific death implicating this pathway as a convergence point for therapeutic resistance in prostate and potentially other cancer types.
Digital and Gradient Refractive Index Planar Optics by Nanoimprinting Mesoporous Silicon

We report the realization of digital and gradient index flat‐optics and planar waveguides using the ‘nanoimprinting refractive index’ (NIRI) technique applied to mesoporous silicon. This technique combines the distinct optical and mechanical metamaterial qualities of mesoporous silicon, including its widely tunable effective refractive index and ability to undergo plastic deformation with a near zero Poisson ratio. Nanoimprinting with premastered and reusable stamps containing analog or digital features enables the continuous or discontinuous patterning of refractive index with high contrast Δn ≥ 0.8 and subwavelength resolution. Using NIRI we experimentally demonstrate a wavefront shaping flat microlens array operating in the visible (405–635 nm) and mesoporous silicon and silica waveguides operating near 1310 nm. This study demonstrates the viability of patterning arbitrary refractive index distributions, n(x,y), on the surface of a chip while circumventing the challenges and limitations of top‐down lithographic techniques – thus opening a low‐cost and scalable approach for the realization of advanced planar optical technologies.
Maternal control over caste allocation in the ant Cardiocondyla obscurior

Ant queens and workers commonly engage in conflict over reproductive allocation because they have different fitness optima. How such conflicts are resolved depends on the power that each party of interest holds. Here, we show that queens have control over caste allocation in the ant Cardiocondyla obscurior. With the discovery of crystalline deposits that allow the identification of queens and workers across the entire course of development, we demonstrate that caste fate is irreversibly determined in the egg stage. Comparison of queen and worker-destined eggs and larvae revealed size and weight differences in late development, but no discernible differences in traits that may be used in social interactions, including hair morphology and cuticular odors. In line with this lack of caste-specific traits, adult workers treated developing queens and workers indiscriminately, even in the presence of a fertile queen. Together with previous studies showing queen control over sex allocation, these results provide evidence that conflict over reproductive allocation is absent in this ant.
Self-Sovereign Identity in a World of Authentication: Architecture and Domain Usecases

Self-Sovereign Identity (SSI) is projected to become part of every person's life in some form. The ability to verify and authenticate that an individual is the actual person they are purported to be along with securing the personal attributes could have wide spread implications when engaging with third party organizations. Utilizing blockchains and other decentralized technologies, SSI is a growing area of research. The aspect of securing personal information within a decentralized structure has possible benefits to the public and private sectors. In this paper, we describe the SSI framework architecture as well as possible use cases across domains like healthcare, finance, retail, and government. The paper also contrasts SSI and its decentralized architecture with the current widely adopted model of Public Key Infrastructure (PKI).
Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering

The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high‐performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)‐based methods are faster and more accurate than DFT‐based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.
Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach. We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results. STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting-based neural representation method. STINR tracks the lung target to an average center-of-mass error of 1–2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%. Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.
Simplified approach to implementing controlled-unitary operations in a two-qubit system

We introduce a scheme for realizing arbitrary controlled-unitary operations in a two-qubit system. If the 2x2 unitary matrix is special unitary (has unit determinant), the controlled-unitary gate operation can be realized in a single pulse operation. The pulse in our scheme will constitute varying one of the parameters of the system between an arbitrarily maximum and a 'calculated' minimum value. This parameter will constitute the variable parameter of the system while the other parameters, which include the coupling between the two qubits, will be treated as fixed parameters. The values of the parameters are what we solve for using our approach in order to realize an arbitrary controlled-unitary operation. We further show that the computational complexity of the operation is no greater than that required for a controlled-NOT (CNOT) gate. Since conventional schemes realize a controlled-unitary operation by breaking it into a sequence of single-qubit and CNOT gate operations, our method is an improvement because we not only require less time duration, but also fewer control lines to implement the same operation. To demonstrate improvement over other schemes, we show, as examples, how two controlled-unitary operations, one being the controlled-Hadamard gate, can be realized in a single pulse operation usingmore » our scheme. Furthermore, our method can be applied to a wide range of coupling schemes and can be used to realize gate operations between two qubits coupled via Ising, Heisenberg, and anisotropic interactions. « less
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers

For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients. In this paper, we developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method based on combined pre-treatment positron emission tomography/computed tomography (PET/CT) scans of HNC patients. We extracted radiomics features from the segmented tumor volume and constructed a multi-modality tumor recurrence-free survival (RFS) prediction model, which fused the prediction results from separate CT radiomics, PET radiomics, and clinical models. We performed 5-fold cross-validation to train and evaluate our methods on the MICCAI 2022 HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR) dataset. The ensemble prediction results on the testing cohort achieved Dice scores of 0.77 and 0.73 for GTVp and GTVn segmentation, respectively, and a C-index value of 0.67 for RFS prediction. The code is publicly available (https://github.com/wangkaiwan/HECKTOR-2022-AIRT). Our team's name is AIRT.
CoRoT 101186644: A transiting low-mass dense M-dwarf on an eccentric 20.7-day period orbit around a late F-star - Discovered in the CoRoT lightcurves

We present the study of the CoRoT transiting planet candidate 101186644, also named LRc01_E1\₄780. Analysis of the CoRoT lightcurve and the HARPS spectroscopic follow-up observations of this faint (m(V) = 16) candidate revealed an eclipsing binary composed of a late F-type primary (T-eff = 6090 +/- 200 K) and a low-mass, dense late M-dwarf secondary on an eccentric (e = 0.4) orbit with a period of similar to 20.7 days. The M-dwarf has a mass of 0.096 +/- 0.011 M-circle dot, and a radius of 0.104(-0.006)(+0.026) R-circle dot, which possibly makes it the smallest and densest late M-dwarf reported so far. Unlike the claim that theoretical models predict radii that are 5-15% smaller than measured for low-mass stars, this one seems to have a radius that is consistent and might even be below the radius predicted by theoretical models.
Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras

In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with reduced costs and improved comfort. This is typically achieved by detecting people using compact embedded devices that are installed on the room's ceiling, and that integrate low-resolution infrared camera, which conceals each person's identity. However, for accurate detection, state-of-the-art deep learning models still require supervised training using a large annotated dataset of images. In this paper, we investigate cost-effective methods that are suitable for person detection based on low-resolution infrared images. Results indicate that for such images, we can reduce the amount of supervision and computation, while still achieving a high level of detection accuracy. Going from single-shot detectors that require bounding box annotations of each person in an image, to auto-encoders that only rely on unlabelled images that do not contain people, allows for considerable savings in terms of annotation costs, and for models with lower computational costs. We validate these experimental findings on two challenging top-view datasets with low-resolution infrared images.
T2FPV: Constructing High-Fidelity First-Person View Datasets From Real-World Pedestrian Trajectories

—Predicting pedestrian motion is essential for developing socially-aware robots that interact in a crowded environment. While the natural visual perspective for a social interaction setting is an egocentric view, the majority of existing work in trajectory prediction has been investigated purely in the top-down trajectory space. To support ﬁrst-person view trajectory prediction research, we present T2FPV, a method for constructing high-ﬁdelity ﬁrst-person view datasets given a real-world, top-down trajectory dataset; we showcase our approach on the ETH/UCY pedestrian dataset to generate the egocentric visual data of all interacting pedestrians. We report that the bird’s-eye view assumption used in the original ETH/UCY dataset, i.e., an agent can observe everyone in the scene with perfect information, does not hold in the ﬁrst-person views; only a fraction of agents are fully visible during each 20-timestep scene used commonly in existing work. We evaluate existing trajectory prediction approaches under varying levels of realistic perception—displacement errors suffer a 356% increase compared to the top-down, perfect information setting. To promote research in ﬁrst-person view trajectory prediction, we release our T2FPV-ETH dataset and software tools § .
Image Classification using Sequence of Pixels

This study compares sequential image classification methods based on recurrent neural networks. We describe methods based on recurrent neural networks such as Long-ShortTerm memory(LSTM), bidirectional Long-Short-Term memory(BiLSTM) architectures, etc. We also review the state-ofthe-art sequential image classification architectures. We mainly focus on LSTM, BiLSTM, temporal convolution network, and independent recurrent neural network architecture in the study. It is known that RNN lacks in learning long-term dependencies in the input sequence. We use a simple feature construction method using orthogonal Ramanujan periodic transform on the input sequence. Experiments demonstrate that if these features are given to LSTM or BiLSTM networks, the performance increases drastically. Our focus in this study is to increase the training accuracy simultaneously reducing the training time for the LSTM and BiLSTM architecture, but not on pushing the state-of-the-art results, so we use simple LSTM/BiLSTM architecture. We compare sequential input with the constructed feature as input to single layer LSTM and BiLSTM network for MNIST and CIFAR datasets. We observe that sequential input to the LSTM network with 128 hidden unit training for five epochs results in training accuracy of 33% whereas constructed features as input to the same LSTM network results in training accuracy of 90% with 1 3 lesser time.
Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN

Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane. We address this issue by presenting Dual-Cycle, a new framework for joint deconvolution and fusion of dual-view fluorescence images. Inspired by the recent Neuroclear method, Dual-Cycle is designed as a cycle-consistent generative network trained in a self-supervised fashion by combining a dual-view generator and prior-guided degradation model. We validate Dual-Cycle on both synthetic and real data showing its state-of-the-art performance without any external training data.
Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph

Heterogeneous graph learning has drawn significant attentions in recent years, due to the success of graph neural networks (GNNs) and the broad applications of heterogeneous information networks. Various heterogeneous graph neural networks have been proposed to generalize GNNs for processing the heterogeneous graphs. Unfortunately, these approaches model the heterogeneity via various complicated modules. This paper aims to propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections. To optimize these relation embeddings and the other parameters simultaneously, a gradient scaling factor is proposed to constrain the embeddings to converge to suitable values. Besides, we theoretically demonstrate that our RE-GNNs have more expressive power than the meta-path based heterogeneous GNNs. Extensive experiments on the node classification tasks validate the effectiveness of our proposed method.
Deranged Matchings: Proofs and Conjectures

We introduce, and partially resolve, a conjecture that brings a three-centuries-old derangements phenomenon and its much younger two-decades-old analogue under the same umbrella. Through a graph-theoretic lens, a derangement is a perfect matching in the complete bipartite graph $K_{n,n}$ with a disjoint perfect matching $M$ removed. Likewise, a deranged matching is a perfect matching in the complete graph $K_{2n}$ minus a perfect matching $M'$. With $\mathrm{pm}(\cdot)$ counting perfect matchings, the elder phenomenon takes the form $\mathrm{pm}(K_{n,n}-M)/\mathrm{pm}(K_{n,n})\to 1/e$ as $n\to\infty$ while its youthful analogue is $\mathrm{pm}(K_{2n}-M')/\mathrm{pm}(K_{2n})\to 1/\sqrt{e}$. These starting graphs are both $2n$-vertex `balanced complete $r$-partite' graphs $K_{r \times {2n}/{r}}$, respectively with $r=2$ and $r=2n$. We conjecture that $\mathrm{pm}(K_{r\times{2n}/r}-M)/\mathrm{pm}(K_{r\times{2n}/r})\sim e^{-r/(2r-2)}$ as $n\to\infty$ and establish several substantive special cases thereof. For just two examples, $r=3$ yields the limit $e^{-3/4}$ while $r=n$ results again in $e^{-1/2}$. Our tools blend combinatorics and analysis in a medley incorporating Inclusion-Exclusion and Tannery's Theorem.
Central limit theorem on CAT(0) spaces with contracting isometries

Let $G$ be a group with a non-elementary action on a proper CAT(0) space $X$, and let $\mu$ be a measure on $G$ such that the random walk $(Z_n)_n$ generated by $\mu$ has finite second moment on $X$. Let $o$ be a basepoint in $X$, and assume that there exists a rank one isometry in $G$. We prove that in this context, $(Z_n o )_n$ satisfies a Central Limit Theorem, namely that the random variables $\frac{1}{\sqrt{n}}(d(Z_n o, o) - n \lambda) $ converge in law to a non-degenerate Gaussian distribution $N_\mu$, for $\lambda$ the (positive) drift of the random walk. The strategy relies on the use of hyperbolic models introduced by H. Petyt, A. Zalloum and D. Spriano, which are analogues of curve graphs and cubical hyperplanes for the class of CAT(0) spaces. As a side result, we prove that the probability that the nth-step $Z_n$ acts on $X$ as a contracting isometry goes to 1 as $n$ goes to infinity.
X-Ray Emission from the Exoplanet Hosting LTT 1445 Triple Star System

JWST will be able to observe the atmospheres of rocky planets transiting nearby M dwarfs. The M-dwarf triple star system LTT 1445, at a distance of 6.86 pc, hosts some of the nearest rocky terrestrial planets. These planets most likely orbit the M 3.5V star LTT 1445A. During a 28.6 ks Chandra ACIS-S3 observation we have: (i) spatially resolved and detected all three stars in the LTT 1445 system; (ii) measured the X-ray luminosity of the individual stars, including LTT 1445A, for the first time; (iii) studied the flux variability of the X-ray sources and found strong variability from the A and C components; and (iv) investigated how the coronal luminosities, temperatures, and volume emission measures vary at different activity levels. Combining these X-ray data with upcoming HST ultraviolet observations will allow a differential emission measure estimation of the star’s extreme-ultraviolet spectrum, thereby facilitating modeling of the rocky planets’ atmospheres.
Incorporation of Human Knowledge into Data Embeddings to Improve Pattern Significance and Interpretability

Embedding is a common technique for analyzing multi-dimensional data. However, the embedding projection cannot always form significant and interpretable visual structures that foreshadow underlying data patterns. We propose an approach that incorporates human knowledge into data embeddings to improve pattern significance and interpretability. The core idea is (1) externalizing tacit human knowledge as explicit sample labels and (2) adding a classification loss in the embedding network to encode samples' classes. The approach pulls samples of the same class with similar data features closer in the projection, leading to more compact (significant) and class-consistent (interpretable) visual structures. We give an embedding network with a customized classification loss to implement the idea and integrate the network into a visualization system to form a workflow that supports flexible class creation and pattern exploration. Patterns found on open datasets in case studies, subjects' performance in a user study, and quantitative experiment results illustrate the general usability and effectiveness of the approach.