Miracle-dz/UniFAR-FTUs huggingface dataset:
Target Aware Poisson-Gaussian Noise Parameters Estimation from Noisy Images

Digital sensors can lead to noisy results under many circumstances. To be able to remove the undesired noise from images, proper noise modeling and an accurate noise parameter estimation is crucial. In this project, we use a Poisson-Gaussian noise model for the raw-images captured by the sensor, as it fits the physical characteristics of the sensor closely. Moreover, we limit ourselves to the case where observed (noisy), and ground-truth (noise-free) image pairs are available. Using such pairs is beneficial for the noise estimation and is not widely studied in literature. Based on this model, we derive the theoretical maximum likelihood solution, discuss its practical implementation and optimization. Further, we propose two algorithms based on variance and cumulant statistics. Finally, we compare the results of our methods with two different approaches, a CNN we trained ourselves, and another one taken from literature. The comparison between all these methods shows that our algorithms outperform the others in terms of MSE and have good additional properties.
Validation of Composite Systems by Discrepancy Propagation

Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests. Validating such systems by means of simulation offers a promising and less expensive alternative, but requires an assessment of the simulation accuracy and therefore end-to-end measurements. Additionally, covariate shifts between simulations and actual usage can cause difficulties for estimating the reliability of such systems. In this work, we present a validation method that propagates bounds on distributional discrepancy measures through a composite system, thereby allowing us to derive an upper bound on the failure probability of the real system from potentially inaccurate simulations. Each propagation step entails an optimization problem, where -- for measures such as maximum mean discrepancy (MMD) -- we develop tight convex relaxations based on semidefinite programs. We demonstrate that our propagation method yields valid and useful bounds for composite systems exhibiting a variety of realistic effects. In particular, we show that the proposed method can successfully account for data shifts within the experimental design as well as model inaccuracies within the simulation.
Movable but not removable band degeneracies in a symmorphic crystal

Crossings of energy bands in solids that are not pinned at symmetry points in the Brillouin zone and yet cannot be removed by perturbations are thought to be conditioned on the presence of a nonsymmorphic symmetry. In this Letter we show that such band crossings can actually appear also in a symmorphic crystal. A study of a class of tight-binding multiband one-dimensional lattice models of spinful electrons reveals that chiral, time-reversal and site-mirror symmetries are suffcient to produce such movable but not removable band degeneracies.
On Small Black Holes in String Theory

We discuss the worldsheet sigma-model whose target space is the $d+1$ dimensional Euclidean Schwarzschild black hole. We argue that in the limit where the Hawking temperature of the black hole, $T$, approaches the Hagedorn temperature, $T_H$, it can be described in terms of a generalized version of the Horowitz-Polchinski effective theory. For $d\geq6$, where the Horowitz-Polchinski EFT [1,2] does not have suitable solutions, the modified effective Lagrangian allows one to study the black hole CFT in an expansion in powers of $d-6$ and $T_H-T$. At $T=T_H$, the sigma model is non-trivial for all $d>6$. It exhibits an enhanced $SU(2)$ symmetry, and is described by a non-abelian Thirring model with a radially dependent coupling. The resulting picture connects naturally to the results of [3-5], that relate Schwarzschild black holes in flat spacetime at large $d$ to the two dimensional black hole. We also discuss an analogous open string system, in which the black hole is replaced by a system of two separated D-branes connected by a throat. In this system, the asymptotic separation of the branes plays the role of the inverse temperature. At the critical separation, the system is described by a Kondo-type model, which again exhibits an enhanced $SU(2)$ symmetry. At large $d$, the brane system gives rise to the hairpin brane [6].
Theoretical analysis of deep neural networks for temporally dependent observations

Deep neural networks are powerful tools to model observations over time with non-linear patterns. Despite the widespread use of neural networks in such settings, most theoretical developments of deep neural networks are under the assumption of independent observations, and theoretical results for temporally dependent observations are scarce. To bridge this gap, we study theoretical properties of deep neural networks on modeling non-linear time series data. Specifically, non-asymptotic bounds for prediction error of (sparse) feed-forward neural network with ReLU activation function is established under mixing-type assumptions. These assumptions are mild such that they include a wide range of time series models including auto-regressive models. Compared to independent observations, established convergence rates have additional logarithmic factors to compensate for additional complexity due to dependence among data points. The theoretical results are supported via various numerical simulation settings as well as an application to a macroeconomic data set.
Nonlinear Attitude Estimation Using Intermittent and Multi-Rate Vector Measurements

This paper considers the problem of nonlinear attitude estimation for a rigid body system using intermittent and multi-rate inertial vector measurements as well as continuous (high-rate) angular velocity measurements. Two types of hybrid attitude observers on Lie group $SO(3)$ are proposed. First, we propose a hybrid attitude observer where almost global asymptotic stability is guaranteed using the notion of almost global input-to-state stability on manifolds. Thereafter, this hybrid attitude observer is extended by introducing a switching mechanism to achieve global asymptotic stability. Both simulation and experimental results are presented to illustrate the performance of the proposed hybrid observers.
On an $n$-manifold in $\mathbf{C}^n$ near an elliptic complex tangent

In this paper, we will be concerned with the local biholomorphic properties of a real n-manifold M in C. At a generic point, such a manifold basically has the nature of the standard R in C. Near a complex tangent, however, the consideration can be much more complicated and the manifold may acquire a nontrivial local hull of holomorphy and many other biholomorphic invariants. The study of such a problem was first carried out in a celebrated paper of E. Bishop [BIS] where, for each sufficiently non-degenerate complex tangent, he attached a biholomorphic invariant λ, called the Bishop invariant. When the complex tangent is elliptic, i.e., when 0 ≤ λ < 12 (for a more precise definition, see §2), he showed the existence of families of complex analytic disks with boundary on M that shrink down to the locus of points in M with complex tangents. In particular, using the well-known continuity principle, one sees that the image M of such families is contained in the holomorphic hull of the manifold. At the time, he asked whether M gives precisely the local holomorphic hull of M , as well as certain uniqueness properties of the attached disks. He also proposed the problem of determining the fine structure of M near such complex tangents. Later, there appeared a sequence of papers concerning the smooth character of M in case M ⊂ C. Here we would like to mention, in particular, the famous theorem proved by Kenig-Webster in their deep work [KW1] which states that the local hull of holomorphy M near an elliptic complex tangent is a smooth Levi flat hypersurface with M ⊂ C as part of its smooth boundary. In another important paper of Moser-Webster [MW], a systematic normal form theory was employed for the understanding of the local biholomorphic invariants of M in case M is real analytic. When the Bishop invariant λ 6= 0, their method works in any dimension and even for some hyperbolic complex tangents; but it breaks down for complex tangents with λ = 0. Among other things, they showed that M can be biholomorphically mapped into the affine space
Improving Semi-Supervised End-To-End Automatic Speech Recognition Using Cyclegan and Inter-Domain Losses

We propose a novel method that combines CycleGAN and inter-domain losses for semi-supervised end-to-end automatic speech recognition. Inter-domain loss targets the extraction of an intermediate shared representation of speech and text inputs using a shared network. CycleGAN uses cycle-consistent loss and the identity mapping loss to preserve relevant characteristics of the input feature after converting from one domain to another. As such, both approaches are suitable to train end-to-end models on unpaired speech-text inputs. In this paper, we exploit the advantages from both inter-domain loss and CycleGAN to achieve better shared representation of unpaired speech and text inputs and thus improve the speech-to-text mapping. Our experimental results on the WSJ eval92 and Voxforge (non English) show $8\sim 8.5\%$ character error rate reduction over the baseline, and the results on LibriSpeech test_clean also show noticeable improvement.
Deep Learning for Diagonal Earlobe Crease Detection

An article published on Medical News Today in June 2022 presented a fundamental question in its title: Can an earlobe crease predict heart attacks? The author explained that end arteries supply the heart and ears. In other words, if they lose blood supply, no other arteries can take over, resulting in tissue damage. Consequently, some earlobes have a diagonal crease, line, or deep fold that resembles a wrinkle. In this paper, we take a step toward detecting this specific marker, commonly known as DELC or Frank's Sign. For this reason, we have made the first DELC dataset available to the public. In addition, we have investigated the performance of numerous cutting-edge backbones on annotated photos. Experimentally, we demonstrate that it is possible to solve this challenge by combining pre-trained encoders with a customized classifier to achieve 97.7% accuracy. Moreover, we have analyzed the backbone trade-off between performance and size, estimating MobileNet as the most promising encoder.
WristSketcher: Creating Dynamic Sketches in AR with a Sensing Wristband

Restricted by the limited interaction area of native AR glasses (e.g., touch bars), it is challenging to create sketches in AR glasses. Recent works have attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures to expand the interactive spaces and can work as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devices allow for accurate sketching but are often heavy to carry, while sketching with bare hands is zero-burden but can be inaccurate due to arm instability. In addition, mid-air bare-hand sketching can easily lead to social misunderstandings and its prolonged use can cause arm fatigue. As a new attempt, in this work, we present WristSketcher, a new AR system based on a flexible sensing wristband for creating 2D dynamic sketches, featuring an almost zero-burden authoring model for accurate and comfortable sketch creation in real-world scenarios. Specifically, we have streamlined the interaction space from the mid-air to the surface of a lightweight sensing wristband, and implemented AR sketching and associated interaction commands by developing a gesture recognition method based on the sensing pressure points on the wristband. The set of interactive gestures used by our WristSketcher is determined by a heuristic study on user preferences. Moreover, we endow our WristSketcher with the ability of animation creation, allowing it to create dynamic and expressive sketches. Experimental results demonstrate that our WristSketcher i) faithfully recognizes users' gesture interactions with a high accuracy of 96.0%; ii) achieves higher sketching accuracy than Freehand sketching; iii) achieves high user satisfaction in ease of use, usability and functionality; and iv) shows innovation potentials in art creation, memory aids, and entertainment applications.
Rethinking Learning Approaches for Long-Term Action Anticipation

Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level representation of the ongoing activity in the video, which is then used for future prediction. We introduce ANTICIPATR which performs long-term action anticipation leveraging segment-level representations learned using individual segments from different activities, in addition to a video-level representation. We propose a two-stage learning approach to train a novel transformer-based model that uses these two types of representations to directly predict a set of future action instances over any given anticipation duration. Results on Breakfast, 50Salads, Epic-Kitchens-55, and EGTEA Gaze+ datasets demonstrate the effectiveness of our approach.
Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis

In this study we focus on the diagnosis of Parkinson's Disease (PD) based on electroencephalogram (EEG) signals. We propose a new approach inspired by the functioning of the brain that uses the dynamics, frequency and temporal content of EEGs to extract new demarcating features of the disease. The method was evaluated on a publicly available dataset containing EEG signals recorded during a 3-oddball auditory task involving N = 50 subjects, of whom 25 suffer from PD. By extracting two features, and separating them with a straight line using a Linear Discriminant Analysis (LDA) classifier, we can separate the healthy from the unhealthy subjects with an accuracy of 90 % $(p<0.03)$ using a single channel. By aggregating the information from three channels and making them vote, we obtain an accuracy of 94 %, a sensitivity of 96 % and a specificity of 92 %. The evaluation was carried out using a nested Leave-One-Out cross-validation procedure, thus preventing data leakage problems and giving a less biased evaluation. Several tests were carried out to assess the validity and robustness of our approach, including the test where we use only half the available data for training. Under this constraint, the model achieves an accuracy of 83.8 %.
Communication breakdown: On the low mutual intelligibility between human and neural captioning

We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner. We conduct this comparison on the recently introduced ImageCoDe data-set (Krojer et al. 2022), which contains hard distractors nearly identical to the images to be retrieved. We find that the neural retriever has much higher performance when fed neural rather than human captions, despite the fact that the former, unlike the latter, were generated without awareness of the distractors that make the task hard. Even more remarkably, when the same neural captions are given to human subjects, their retrieval performance is almost at chance level. Our results thus add to the growing body of evidence that, even when the “language” of neural models resembles English, this superficial resemblance might be deeply misleading.
Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns

We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision. While prior works have considered motion for segmentation, a key insight is that, while motion can be used to identify objects, not all objects are necessarily in motion: the absence of motion does not imply the absence of objects. Hence, our model learns to predict image regions that are likely to contain motion patterns characteristic of objects moving rigidly. It does not predict specific motion, which cannot be done unambiguously from a still image, but a distribution of possible motions, which includes the possibility that an object does not move at all. We demonstrate the advantage of this approach over its deterministic counterpart and show state-of-the-art unsupervised object segmentation performance on simulated and real-world benchmarks, surpassing methods that use motion even at test time. As our approach is applicable to variety of network architectures that segment the scenes, we also apply it to existing image reconstruction-based models showing drastic improvement. Project page and code: https://www.robots. ox.ac. uk/~vgg/research/ppmp .
Electromagnetic counterparts of compact object mergers powered by the radioactive decay of r‐process nuclei

The most promising astrophysical sources of kHz gravitational waves (GWs) are the inspiral and merger of binary neutron star(NS)/black hole systems. Maximizing the scientific return of a GW detection will require identifying a coincident electromagnetic (EM) counterpart. One of the most likely sources of isotropic EM emission from compact object mergers is a supernova-like transient powered by the radioactive decay of heavy elements synthesized in ejecta from the merger. We present the first calculations of the optical transients from compact object mergers that self-consistently determine the radioactive heating by means of a nuclear reaction network; using this heating rate, we model the light curve with a one-dimensional Monte Carlo radiation transfer calculation. For an ejecta mass ~ 10 -2 M ⊙ (10 -3 M ⊙ ) the resulting light-curve peaks on a time-scale ~1 d at a V-band luminosity vL v ~ 3 x 10 41 (10 41 )erg s -1 [M V = -15(-14)]; this corresponds to an effective 'f' parameter ~3 × 10- 6 in the Li-Paczynski toy model. We argue that these results are relatively insensitive to uncertainties in the relevant nuclear physics and to the precise early-time dynamics and ejecta composition. Since NS merger transients peak at a luminosity that is a factor of ~10 3 higher than a typical nova, we propose naming these events 'kilo-novae'. Because of the rapid evolution and low luminosity of NS merger transients, EM counterpart searches triggered by GW detections will require close collaboration between the GW and astronomical communities. NS merger transients may also be detectable following a short-duration gamma-ray burst or 'blindly' with present or upcoming optical transient surveys. Because the emission produced by NS merger ejecta is powered by the formation of rare r-process elements, current optical transient surveys can directly constrain the unknown origin of the heaviest elements in the Universe.
Barrier Hamiltonian Monte Carlo

In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a version of HMC which aims at sampling from a Gibbs distribution π on a manifold M , endowed with a Hessian metric g derived from a self-concordant barrier. Like Riemannian Manifold HMC, our method relies on Hamiltonian dynamics which comprise g . It incorporates the constraints deﬁning M and is therefore able to exploit its underlying geometry. We ﬁrst introduce c-BHMC (continuous BHMC), for which we assume that the Hamiltonian dynamics can be integrated exactly, and show that it generates a Markov chain for which π is invariant. Secondly, we design n-BHMC (numerical BHMC), a Metropolis-Hastings algo-rithm which combines an acceptance ﬁlter including a “reverse integration check“ and numerical integrators of the Hamiltonian dynamics. Our main results establish that n-BHMC generates a reversible Markov chain with respect to π . This is in contrast to existing algorithms which extend the HMC method to Riemannian manifolds, as they do not deal with asymptotic bias. Our conclusions are supported by numerical experiments where we consider target distributions deﬁned on polytopes.
A Survey of Data Optimization for Problems in Computer Vision Datasets

Recent years have witnessed remarkable progress in artificial intelligence (AI) thanks to refined deep network structures, powerful computing devices, and large-scale labeled datasets. However, researchers have mainly invested in the optimization of models and computational devices, leading to the fact that good models and powerful computing devices are currently readily available, while datasets are still stuck at the initial stage of large-scale but low quality. Data becomes a major obstacle to AI development. Taking note of this, we dig deeper and find that there has been some but unstructured work on data optimization. They focus on various problems in datasets and attempt to improve dataset quality by optimizing its structure to facilitate AI development. In this paper, we present the first review of recent advances in this area. First, we summarize and analyze various problems that exist in large-scale computer vision datasets. We then define data optimization and classify data optimization algorithms into three directions according to the optimization form: data sampling, data subset selection, and active learning. Next, we organize these data optimization works according to data problems addressed, and provide a systematic and comparative description. Finally, we summarize the existing literature and propose some potential future research topics.
WHY WALK AND RUN: ENERGETIC COSTS AND ENERGETIC OPTIMALITY IN SIMPLE MECHANICS-BASED MODELS OF A BIPEDAL

(a cubic ﬁtdoes not do any better). The accuracy of the extrapolations seemto be consistent with these mean square errors. 663.2 Convergence of the smoothed optimal control problem inN. OptimalvaluesforasequenceofNforV=0.5, D=0.5areplot-tedintermsoftheirdiﬀerencesfromtheaccuratecollisionalwalkingcost. Each of these numbers were obtained by solving a sequence ofǫ-smoothed optimal control problems (with the smoothing functionf
Separability-entanglement classifier via machine learning

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach.
Recovery of general relativity in massive gravity via the Vainshtein mechanism

We study in detail static spherically symmetric solutions of nonlinear Pauli-Fierz theory. We obtain a numerical solution with a constant density source. This solution shows a recovery of the corresponding solution of general relativity via the Vainshtein mechanism. This result has already been presented by us in a recent paper, and we give here more detailed information on it as well as on the procedure used to obtain it. We give new analytic insights into this problem, in particular, for what concerns the question of the number of solutions at infinity. We also present a weak-field limit which allows one to capture all the salient features of the numerical solution, including the Vainshtein crossover and the Yukawa decay.