Miracle-dz/UniFAR-FTUs huggingface dataset:
Q-matrix Unaware Double JPEG Detection using DCT-Domain Deep BiLSTM Network

The double JPEG compression detection has received much attention in recent years due to its applicability as a forensic tool for the most widely used JPEG file format. Existing state-of-the-art CNN-based methods either use histograms of all the frequencies or rely on heuristics to select histograms of specific low frequencies to classify single and double compressed images. However, even amidst lower frequencies of double compressed images/patches, histograms of all the frequencies do not have distinguishable features to separate them from single compressed images. This paper directly extracts the quantized DCT coefficients from the JPEG images without decompressing them in the pixel domain, obtains all AC frequencies' histograms, uses a module based on $1\times 1$ depth-wise convolutions to learn the inherent relation between each histogram and corresponding q-factor, and utilizes a tailor-made BiLSTM network for selectively encoding these feature vector sequences. The proposed system outperforms several baseline methods on a relatively large and diverse publicly available dataset of single and double compressed patches. Another essential aspect of any single vs. double JPEG compression detection system is handling the scenario where test patches are compressed with entirely different quantization matrices (Q-matrices) than those used while training; different camera manufacturers and image processing software generally utilize their customized quantization matrices. A set of extensive experiments shows that the proposed system trained on a single dataset generalizes well on other datasets compressed with completely unseen quantization matrices and outperforms the state-of-the-art methods in both seen and unseen quantization matrices scenarios.
Correcting correlation functions for redshift-dependent interloper contamination

The construction of catalogues of a particular type of galaxy can be complicated by interlopers contaminating the sample. In spectroscopic galaxy surveys this can be due to the misclassification of an emission line; for example in the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) low-redshift [O ii] emitters may make up a few per cent of the observed Ly α emitter (LAE) sample. The presence of contaminants affects the measured correlation functions and power spectra. Previous attempts to deal with this using the cross-correlation function have assumed sources at a fixed redshift, or not modelled evolution within the adopted redshift bins. However, in spectroscopic surveys like HETDEX, where the contamination fraction is likely to be redshift dependent, the observed clustering of misclassified sources will appear to evolve strongly due to projection effects, even if their true clustering does not. We present a practical method for accounting for the presence of contaminants with redshift-dependent contamination fractions and projected clustering. We show using mock catalogues that our method, unlike existing approaches, yields unbiased clustering measurements from the upcoming HETDEX survey in scenarios with redshift-dependent contamination fractions within the redshift bins used. We show our method returns autocorrelation functions with systematic biases much smaller than the statistical noise for samples with at least as high as 7 per cent contamination. We also present and test a method for fitting for the redshift-dependent interloper fraction using the LAE–[O ii] galaxy cross-correlation function, which gives less biased results than assuming a single interloper fraction for the whole sample.
Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph

We present a SE(3)-equivariant graph neural network (GNN) approach that directly predicting the formation factor and effective permeability from micro-CT images. FFT solvers are established to compute both the formation factor and effective permeability, while the topology and geometry of the pore space are represented by a persistence-based Morse graph. Together, they constitute the database for training, validating, and testing the neural networks. While the graph and Euclidean convolutional approaches both employ neural networks to generate low-dimensional latent space to represent the features of the micro-structures for forward predictions, the SE(3) equivariant neural network is found to generate more accurate predictions, especially when the training data is limited. Numerical experiments have also shown that the new SE(3) approach leads to predictions that fulfill the material frame indifference whereas the predictions from classical convolutional neural networks (CNN) may suffer from spurious dependence on the coordinate system of the training data. Comparisons among predictions inferred from training the CNN and those from graph convolutional neural networks (GNN) with and without the equivariant constraint indicate that the equivariant graph neural network seems to perform better than the CNN and GNN without enforcing equivariant constraints.
Unified approach to electrical and thermal transport in high-
Tc
 superconductors

In this paper we present a consolidated equation for all low-field transport coefficients, based on a reservoir approach developed for non-interacting quasiparticles. This formalism allows us to treat the two distinct types of charged (fermionic and bosonic) quasiparticles that can be simultaneously present, as for example in superconductors. Indeed, in the underdoped cuprate superconductors these two types of carriers result in two onset temperatures with distinct features in transport: T ∗, where the fermions first experience an excitation (pseudo)gap, and Tc, where bosonic conduction processes are dominant and often divergent. This provides the central goal of this paper, which is to address the challenges in thermoelectric transport that stem from having two characteristic temperatures as well as two types of charge carriers whose contributions can in some instances enhance each other and in others compete. We show how essential features of the cuprates (their bad-metal character and the presence of Fermi arcs) provide an explanation for the classic pseudogap onset signatures at T ∗ in the longitudinal resistivity, ρxx. Based on the fits to the temperaturedependent ρxx, we present the implications for all of the other thermoelectric transport properties.
Sparse data inpainting for the recovery of Galactic-binary gravitational wave signals from gapped data

The forthcoming space-based gravitational wave observatory LISA will open a new window for the measurement of galactic binaries, which will deliver unprecedented information about these systems. However, the detection of galactic binary gravitational wave signals is challenged by the presence of gaps in the data. Whether being planned or not, gapped data reduce our ability to detect faint signals and increase the risk of misdetection. Inspired by advances in signal processing, we introduce a non-parametric inpainting algorithm based on the sparse representation of the galactic binary signal in the Fourier domain. In contrast to traditional inpainting approaches, noise statistics are known theoretically on ungapped measurements only. This calls for the joint recovery of both the ungapped noise and the galactic binary signal. We thoroughly show that sparse inpainting yields an accurate estimation of the gravitational imprint of the galactic binaries. Additionally, we highlight that the proposed algorithm produces a statistically consistent ungapped noise estimate. We further evaluate the performances of the proposed inpainting methods to recover the gravitational wave signal on a simple example involving verification galactic binaries recently proposed in LISA data challenges.
Designing Effective Interview Chatbots: Automatic Chatbot Profiling and Design Suggestion Generation for Chatbot Debugging

Recent studies show the effectiveness of interview chatbots for information elicitation. However, designing an effective interview chatbot is non-trivial. Few tools exist to help designers design, evaluate, and improve an interview chatbot iteratively. Based on a formative study and literature reviews, we propose a computational framework for quantifying the performance of interview chatbots. Incorporating the framework, we have developed iChatProfile, an assistive chatbot design tool that can automatically generate a profile of an interview chatbot with quantified performance metrics and offer design suggestions for improving the chatbot based on such metrics. To validate the effectiveness of iChatProfile, we designed and conducted a between-subject study that compared the performance of 10 interview chatbots designed with or without using iChatProfile. Based on the live chats between the 10 chatbots and 1349 users, our results show that iChatProfile helped the designers build significantly more effective interview chatbots, improving both interview quality and user experience.
Door Delivery of Packages using Drones

—In this work, we present a system that enables delivery drones to autonomously navigate and deliver packages at various locations around the house. The objective of this to reach a speciﬁc location in the house where the recipient wants the package to be delivered by the drone without the use of any external markers as currently used. This work is motivated by the recent advancements in semantic segmentation using deep learning that can potentially replace the specialized marker used by the current delivery drone for identifying the place where it needs to deliver the package. The proposed system is more natural in the sense that it takes an instruction input on where to deliver the package similar to the instructions provided to the human couriers. We propose a semantic segmentation-based lowering location estimator that enables the drone to ﬁnd a safe spot around the house to lower from higher altitudes. Following this, we propose a strategy for visually routing the drone from the location where it lowered to a speciﬁc location like the front door of the house where it needs to deliver the package. We extensively evaluate the proposed approach in a simulated environment that demonstrates that the delivery drone can deliver the package to the front door and also to other speciﬁed locations around the house.
The Second Law of Quantum Complexity and the Entanglement Wormhole

This work is originally a Cambridge Part III essay paper. Quantum complexity arises as an alternative measure to the Fubini metric between two quantum states. Given two states and a set of allowed gates, it is defined as the least complex unitary operator capable of transforming one state into the other. Starting with K qubits evolving through a k-local Hamiltonian, it is possible to draw an analogy between the quantum system and an auxiliary classical system. Using the definition of complexity to define a metric for the classical system, it is possible to relate its entropy with the quantum complexity of the K qubits, defining the Second Law of Quantum Complexity. The law states that, if it is not already saturated, the quantum complexity of a system will increase with an overwhelming probability towards its maximum value. In the context of AdS/CFT duality and the ER=EPR conjecture, the growth of the volume of the Einstein Rosen bridge interior is proportional to the quantum complexity of the instantaneous state of the conformal field theory. Therefore, the interior of the wormhole connecting two entangled CFT will grow as a natural consequence of the complexification of the boundary state.
Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration by obtaining multiple undersampled images simultaneously through parallel imaging has always been the subject of research. In this paper, we propose the Dual-Octave Convolution (Dual-OctConv), which is capable of learning multi-scale spatial-frequency features from both real and imaginary components, for fast parallel MR image reconstruction. By reformulating the complex operations using octave convolutions, our model shows a strong ability to capture richer representations of MR images, while at the same time greatly reducing the spatial redundancy. More specifically, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary), which are then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intra-group information updating and inter-group information exchange to aggregate the contextual information across different groups. Our framework provides two appealing benefits: (i) it encourages interactions between real and imaginary components at various spatial frequencies to achieve richer representational capacity, and (ii) it enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. We evaluate the performance of the proposed model on the acceleration of multi-coil MR image reconstruction. Extensive experiments are conducted on an {in vivo} knee dataset under different undersampling patterns and acceleration factors. The experimental results demonstrate the superiority of our model in accelerated parallel MR image reconstruction. Our code is available at: github.com/chunmeifeng/Dual-OctConv.
Topological superfluid transition in bubble-trapped condensates

Ultracold quantum gases are highly controllable and, thus, capable of simulating difficult quantum many-body problems ranging from condensed matter physics to astrophysics. Although experimental realizations have so far been restricted to flat geometries, recently also curved quantum systems, with the prospect of exploring tunable geometries, are produced in microgravity facilities as ground-based experiments are technically limited. Here we analyze bubble-trapped condensates, in which the atoms are confined on the surface of a thin spherically-symmetric shell by means of external magnetic fields. A thermally-induced proliferation of vorticity yields a vanishing of superfluidity. We describe the occurrence of this topological transition by conceptually extending the theory of Berezinskii, Kosterlitz and Thouless for infinite uniform systems to such finite-size systems. Unexpectedly, we find universal scaling relations for the mean critical temperature and the finite width of the superfluid transition. Furthermore, we elucidate how they could be experimentally observed in finite-temperature hydrodynamic excitations.
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation

This paper addresses the task of unsupervised video multi-object segmentation. Current approaches follow a two-stage paradigm: 1) detect object proposals using pre-trained Mask R-CNN, and 2) conduct generic feature matching for temporal association using re-identification techniques. However, the generic features, widely used in both stages, are not reliable for characterizing unseen objects, leading to poor generalization. To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation. In particular, to address instance discrimination, we propose to combine foreground region estimation and instance grouping together in one network, and additionally introduce temporal guidance for segmenting each frame, enabling more accurate object discovery. For temporal association, we complement current video object segmentation architectures with a discriminative appearance model, capable of capturing more fine-grained target-specific information. Given object proposals from the instance discrimination network, three essential strategies are adopted to achieve accurate segmentation: 1) target-specific tracking using a memory-augmented appearance model; 2) target-agnostic verification to trace possible tracklets for the proposal; 3) adaptive memory updating using the verified segments. We evaluate the proposed approach on DAVIS17 and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.
BurstLink: Techniques for Energy-Efficient Conventional and Virtual Reality Video Display

Conventional planar video streaming is the most popular application in mobile systems, and the rapid growth of 360 ◦ video content and virtual reality (VR) devices are accelerating the adoption of VR video streaming. Unfortunately, video streaming consumes signiﬁcant system energy due to high power consumption of the system components (e.g., DRAM, display interfaces, and display panel) involved in this process. We propose BurstLink, a novel system-level technique that improves the energy efﬁciency of planar and VR video streaming. BurstLink is based on two key ideas. First, BurstLink directly transfers a decoded video frame from the host sys-tem (i.e., the video decoder or GPU) to the display panel, bypassing the host DRAM. To this end, we extend the display panel with a double remote frame buffer (DRFB) , instead of the DRAM’s double frame buffer, so that the system can directly update the DRFB with a new frame while updating the panel’s pixels with the current frame stored in the DRFB. Second, BurstLink transfers a complete decoded frame to the display panel in a single burst , using the maximum bandwidth of modern display interfaces. Unlike conventional systems where the frame transfer rate is limited by the pixel-update throughput of the display panel, BurstLink can always take full advantage of the high bandwidth of modern display interfaces by decoupling the frame transfer from the pixel update as enabled by the DRFB. This direct and burst frame transfer of BurstLink signiﬁcantly reduces energy consumption in video display by 1) reducing accesses to the host DRAM, 2) increasing the system’s residency at idle power states, and 3) enabling temporal power gating of several system components after quickly transferring each frame into the DRFB
Speaking of Trust - Speech as a Measure of Trust

Since trust measures in human-robot interaction are often subjective or not possible to implement real-time, we propose to use speech cues (on what, when and how the user talks) as an objective real-time measure of trust. This could be implemented in the robot to calibrate towards appropriate trust. However, we would like to open the discussion on how to deal with the ethical implications surrounding this trust measure.
Kantian equilibria in classical and quantum symmetric games

The aim of the paper is to examine the notion of simple Kantian equilibrium in 2× 2 symmetric games and their quantum counterparts. We focus on finding the Kantian equilibrium strategies in the general form of the games. As a result, we derive a formula that determines the reasonable strategies for any payoffs in the bimatrix game. This allowed us to compare the payoff results for classical and quantum way of playing the game. We showed that a very large part of 2× 2 symmetric games have more beneficial Kantian equilibria when they are played with the use of quantum strategies.
Radar SLAM: A Robust SLAM System for All Weather Conditions

A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade their imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensing geometry and noise characteristics bring open challenges when being exploited for SLAM. % However, there are still open challenges since most existing visual and LiDAR SLAM systems do not operate in bad weathers. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion tracking algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. % while a scheme to reject ambiguous loop closure candidates is also designed specifically for radar. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions, e.g. heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.
Point Wise or Feature Wise? A Benchmark Comparison of Publicly Available Lidar Odometry Algorithms in Urban Canyons

Robust and precise localization is essential for an autonomous system with navigation requirements. Lidar odometry (LO) has been extensively studied in the past decades to realize this goal. Satisfactory accuracy can be achieved in scenarios with abundant environmental features using existing LO algorithms. Unfortunately, the performance of the LO is significantly degraded in urban canyons with numerous dynamic objects and complex environmental structures. Meanwhile, it is still not clear from the existing literature which LO algorithms perform well in such challenging environments. To fill this gap, this article evaluates an array of popular and extensively studied LO pipelines using the data sets collected in urban canyons of Hong Kong. We present the results in terms of their positioning accuracy and computational efficiency. The three major factors dominating the performance of LO in urban canyons are concluded, including the ego-vehicle dynamic, moving objects, and the degree of urbanization. According to our experiment results, point wise accomplishes better accuracy in urban canyons while feature-wise achieves cost-efficiency and satisfactory positioning accuracy.
The spectra of IceCube neutrino candidate sources – I. Optical spectroscopy of blazars

There is mounting evidence that ultra-energetic neutrinos of astrophysical origin may be associated with blazars. Here we investigate a unique sample of 47 blazars, ∼ 20 of which could be new neutrino sources. In particular, we focus on 17 objects of yet unknown redshift, for which we present optical spectroscopy secured at the Gran Telescopio Canarias and the ESO Very Large Telescope. We find all sources but one (a quasar) to be BL Lac objects. For nine targets we are able to determine the redshift (0.09 < z < 1.6), while for the others we set a lower limit on it, based on either the robust detection of intervening absorption systems or on an estimation derived from the absence of spectral signatures of the host galaxy. In some spectra we detect forbidden and semi-forbidden emission lines with luminosities in the range 1040 − 1041 erg s−1. We also report on the spectroscopy of seven blazars possibly associated with energetic neutrinos that partially meet the criteria of our sample and are discussed in the Appendix. These results represent the starting point of our investigation into the real nature of these objects and their likelihood of being neutrino emitters.