Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimizing Low-Rank Models of High-Order Tensors: Hardness, Span, Tight Relaxation, and Applications

We consider the problem of finding the smallest or largest entry of a tensor of order <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> that is specified via its rank decomposition. Stated in a different way, we are given <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> sets of <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula>-dimensional vectors and we wish to select one vector from each set such that the sum of the Hadamard product of the selected vectors is minimized or maximized. We show that this fundamental tensor problem is NP-hard for any tensor rank higher than one, and polynomial-time solvable in the rank-one case. We also propose a continuous relaxation and prove that it is tight for any rank. For low-enough ranks, the proposed continuous reformulation is amenable to low-complexity gradient-based optimization, and we propose a suite of gradient-based optimization algorithms drawing from projected gradient descent, Frank-Wolfe, or explicit parametrization of the relaxed constraints. We also show that our core results remain valid no matter what kind of polyadic tensor model is used to represent the tensor of interest, including Tucker, HOSVD/MLSVD, tensor train, or tensor ring. Next, we consider the class of problems that can be posed as special instances of the problem of interest. We show that this class includes the partition problem (and thus all NP-complete problems via polynomial-time transformation), integer least squares, integer linear programming, integer quadratic programming, sign retrieval (a special kind of mixed integer programming / restricted version of phase retrieval), and maximum likelihood decoding of parity check codes. We demonstrate promising experimental results on a number of hard problems, including state-of-art performance in decoding low density parity check codes and general parity check codes.
Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions

Due to depth ambiguities and occlusions, lifting 2D poses to 3D is a highly ill-posed problem. Well-calibrated distributions of possible poses can make these ambiguities explicit and preserve the resulting uncertainty for downstream tasks. This study shows that previous attempts, which account for these ambiguities via multiple hypotheses generation, produce miscalibrated distributions. We identify that miscalibration can be attributed to the use of sample-based metrics such as minMPJPE. In a series of simulations, we show that minimizing minMPJPE, as commonly done, should converge to the correct mean prediction. However, it fails to correctly capture the uncertainty, thus resulting in a miscalibrated distribution. To mitigate this problem, we propose an accurate and well-calibrated model called Conditional Graph Normalizing Flow (cGNFs). Our model is structured such that a single cGNF can estimate both conditional and marginal densities within the same model - effectively solving a zero-shot density estimation problem. We evaluate cGNF on the Human~3.6M dataset and show that cGNF provides a well-calibrated distribution estimate while being close to state-of-the-art in terms of overall minMPJPE. Furthermore, cGNF outperforms previous methods on occluded joints while it remains well-calibrated.
Transformer-based Action recognition in hand-object interacting scenarios

This report describes the 2nd place solution to the ECCV 2022 Human Body, Hands, and Activities (HBHA) from Egocentric and Multi-view Cameras Challenge: Action Recognition. This challenge aims to recognize hand-object interaction in an egocentric view. We propose a framework that estimates keypoints of two hands and an object with a Transformer-based keypoint estimator and recognizes actions based on the estimated keypoints. We achieved a top-1 accuracy of 87.19% on the testset.
Multi-View Guided Multi-View Stereo

This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames, leveraging a sparse set of depth measurements gathered jointly with image acquisition. Given a deep multi-view stereo network, our framework uses sparse depth hints to guide the neural network by modulating the plane-sweep cost volume built during the forward step, enabling us to infer constantly much more accurate depth maps. Moreover, since multiple viewpoints can provide additional depth measurements, we propose a multi-view guidance strategy that increases the density of the sparse points used to guide the network, thus leading to even more accurate results. We evaluate our Multi-View Guided framework within a variety of state-of-the-art deep multi-view stereo networks, demonstrating its effectiveness at improving the results achieved by each of them on BlendedMVG and DTU datasets.
Time-Delay Cosmography: Measuring the Hubble Constant and other cosmological parameters with strong gravitational lensing

Multiply lensed sources experience a relative time delay in the arrival of photons. This effect can be used to measure absolute distances and the Hubble constant ($H_0$) and is known as time-delay cosmography. The methodology is independent of the local distance ladder and early-universe physics and provides a precise and competitive measurement of $H_0$. With upcoming observatories, time-delay cosmography can provide a 1% precision measurement of $H_0$ and can decisively shed light on the current reported 'Hubble tension'. This paper presents the theoretical background and the current techniques applied for time-delay cosmographic studies and the measurement of the Hubble constant. The paper describes the challenges and systematics in the different components of the analysis and strategies to mitigate them. The current measurements are discussed in context and the opportunities with the anticipated data sets in the future are laid out.
Improving Segmentation of Breast Ultrasound Images: Semi Automatic Two Pointers Histogram Splitting Technique

—Automatically segmenting lesion area in breast ul- trasound (BUS) images is a challenging one due to its noise, speckle and artifacts. Edge-map of BUS images also does not help because in most cases the edge-map gives no information whatsoever. Almost all segmentation technique takes the edge- map of the image as its ﬁrst step, though there are a few algorithms that try to avoid edge-maps as well. Improving the edge-map of breast ultrasound images theoretically improves the chances of automatic segmentation to be more precise. In this paper, we propose a semi-automatic technique of histogram splitting using two pointers. Here the user only has to select two initially guessed points denoting a circle on the region of interest (ROI). The method will automatically study the internal histogram and split it using two pointers. The output BUS image has improved edge-map and ultimately the segmentation on it is better compared to regular segmentation using same algorithm and same initialization. Also, we further processed the edge-map to have less edge-pixels to area ratio, improving the homogeneity and the chances of easy segmentation in the future.
Competitive Online Truthful Time-Sensitive-Valued Data Auction

—Digital product, such as original data and data-copyright, as an irreplaceable form of data products, can be transferred by the owner to potential users to maximize its potential usage while protecting the traded data from being resold. Moreover, the value of the digital product and data typically changes over time in real data application scenarios such as monitoring and recommendation systems, i.e. , its value is time-sensitive. In this work, we investigate online mechanisms for trading time-sensitive valued digital product and data-copyright. We adopt a continuous function d ( t ) to represent the data value ﬂuctuation over time t . Our objective is to design an online trading mechanism achieving truthfulness and revenue-competitiveness with respect to the ofﬂine widely-celebrated Vickrey auction. However, designing such single-round online mechanisms ( i.e. , the copyright can only be sold to one unique buyer) is much more challenging than that in a multi-round scenario due to the short of opportunities to learn the trading information, or than that in a non-time-sensitive scenario as each user’s valuation changes over time. We ﬁrst prove several lower bounds on the revenue competitive ratios of individual-rational mechanisms under various assumptions, such as a lower bound of Ω( n ) when there is an arbitrarily unknown function d ( t ) , and a lower bound of Ω( log n loglog n ) when the known function d ( t ) is non-increasing. We then propose several online truthful auction mechanisms for various adversarial models, such as a randomized observe-then-select mechanism M R and prove that it is truthful and Θ(log 2 n ) -competitive when d ( t ) is known and non-increasing and the number of users n c in each discount-class is of same order. With same assumption, we show that a simpler mechanism M 1 is truthful and Θ(log n ) -competitive. Furthermore, without any restrictions on the sizes of the discount-classes, we prove that the mechanism M 1 could have a competitive ratio as ad as Ω( n 2 ) . Then we present an effective truthful weighted-selection mechanism M (cid:48) W by relaxing the assumptions on the sizes of the discount-classes. We prove that it achieves a competitive ratio Θ( n log n ) for any known non-decreasing discount function d ( t ) , and n c ≥ 2 . When the optimum expected revenue can be estimated within a constant factor, we propose a truthful online posted-price mechanism that achieves a constant competitive ratio. We conduct extensive numerical evaluations to evaluate the practical performances of proposed mechanisms and our results demonstrate that our mechanisms perform very well in most cases.
A Continuum of Generation Tasks for Investigating Length Bias and Degenerate Repetition

Language models suffer from various degenerate behaviors. These differ between tasks: machine translation (MT) exhibits length bias, while tasks like story generation exhibit excessive repetition. Recent work has attributed the difference to task constrainedness, but evidence for this claim has always involved many confounding variables. To study this question directly, we introduce a new experimental framework that allows us to smoothly vary task constrainedness, from MT at one end to fully open-ended generation at the other, while keeping all other aspects fixed. We find that: (1) repetition decreases smoothly with constrainedness, explaining the difference in repetition across tasks; (2) length bias surprisingly also decreases with constrainedness, suggesting some other cause for the difference in length bias; (3) across the board, these problems affect the mode, not the whole distribution; (4) the differences cannot be attributed to a change in the entropy of the distribution, since another method of changing the entropy, label smoothing, does not produce the same effect.
Using Integer Programming Techniques in Real-Time Scheduling Analysis

—Real-time scheduling theory assists developers of embedded systems in verifying that the timing constraints required by critical software tasks can be feasibly met on a given hardware platform. Fundamental problems in the theory are often formulated as search problems for ﬁxed points of functions and are solved by ﬁxed-point iterations. These ﬁxed-point methods are used widely because they are simple to understand, simple to implement, and seem to work well in practice. These fundamental problems can also be formulated as integer programs and solved with algorithms that are based on theories of linear programming and cutting planes amongst others. However, such algorithms are harder to understand and implement than ﬁxed-point iterations. In this research, we show that ideas like linear programming duality and cutting planes can be used to develop algorithms that are as easy to implement as existing ﬁxed-point iteration schemes but have better convergence properties. We evaluate the algorithms on synthetically generated problem instances to demonstrate that the new algorithms are faster than the existing algorithms.
Stable ion-tunable antiambipolarity in mixed ion-electron conducting polymers enables biorealistic artificial neurons

Bio-integrated neuromorphic systems promise for new protocols to record and regulate the signaling of biological systems. Making such artificial neural circuits successful requires minimal circuit complexity and ion-based operating mechanisms similar to that of biology. However, simple leaky integrate-and-fire model neurons, commonly realized in either silicon 1 or organic 2,3 semiconductor neuromorphic systems, can emulate only a few neural features. More functional neuron models 1,4,5 , based on traditional complex Si-based complementary-metal-oxide-semiconductor (CMOS) 6–8 or negative differential resistance (NDR) 9–11 device circuits, are complicated to fabricate, not biocompatible, and lack ion- and chemical-based modulation features. Here we report a biorealistic conductance-based organic electrochemical neuron (c-OECN) using a mixed ion-electron conducting ladder-type polymer with reliable ion-tunable antiambipolarity. The latter is used to emulate the activation/inactivation of Na channels and delayed activation of K channels of biological neurons. These c-OECNs can then spike at bioplausible frequencies nearing 100 Hz, emulate most critical biological neural features, demonstrate stochastic spiking, and enable neurotransmitter and Ca 2+ -based spiking modulation. These combined features are impossible to achieve using previous technologies.
Testing the accuracy of likelihoods for cluster abundance cosmology

The abundance of galaxy clusters is a sensitive probe to the amplitude of matter density fluctuations, the total amount of matter in the Universe as well as its expansion history. Inferring correct values and accurate uncertainties of cosmological parameters requires accurate knowledge of cluster abundance statistics, encoded in the likelihood function. In this paper, we test the accuracy of cluster abundance likelihoods used in the literature, namely the Poisson and Gaussian likelihoods as well as the more complete description of the Gauss–Poisson Compound likelihood. This is repeated for a variety of binning choices and analysis setups. In order to evaluate the accuracy of a given likelihood, this work compares individual posterior covariances to the covariance of estimators over the 1000 simulated dark matter halo catalogues obtained from PINOCCHIO algorithm. We find that for Rubin/LSST and Euclid-like surveys the Gaussian likelihood gives robust constraints over a large range of binning choices. The Poisson likelihood, that does not account for sample covariance, always underestimates the errors on the parameters, even when the sample volume is reduced or only high-mass clusters are considered. We find no benefit in using the more complex Gauss–Poisson Compound likelihood as it gives essentially the same results as the Gaussian likelihood, but at a greater computational cost. Finally, in this ideal setup, we note only a small gain on the parameter error bars when using a large number of bins in the mass–redshift plane.
Multitasking network with fast noise

We consider the multitasking associative network in the low-storage limit and we study its phase diagram with respect to the noise level $T$ and the degree $d$ of dilution in pattern entries. We find that the system is characterized by a rich variety of stable states, among which pure states, parallel retrieval states, hierarchically organized states and symmetric mixtures (remarkably, both even and odd), whose complexity increases as the number of patterns $P$ grows. The analysis is performed both analytically and numerically: Exploiting techniques based on partial differential equations, allows us to get the self-consistencies for the order parameters. Such self-consistence equations are then solved and the solutions are further checked through stability theory to catalog their organizations into the phase diagram, which is completely outlined at the end. This is a further step toward the understanding of spontaneous parallel processing in associative networks.
Generalization Properties of Decision Trees on Real-valued and Categorical Features

We revisit binary decision trees from the perspective of partitions of the data. We introduce the notion of partitioning function, and we relate it to the growth function and to the VC dimension. We consider three types of features: real-valued, categorical ordinal and categorical nominal, with different split rules for each. For each feature type, we upper bound the partitioning function of the class of decision stumps before extending the bounds to the class of general decision tree (of any fixed structure) using a recursive approach. Using these new results, we are able to find the exact VC dimension of decision stumps on examples of l real-valued features, which is given by the largest integer d such that 2l ≥ ( d ⌊ d 2 ⌋ ) . Furthermore, we show that the VC dimension of a binary tree structure with LT leaves on examples of l real-valued features is in O(LT log(LT l)). Finally, we elaborate a pruning algorithm based on these results that performs better than the cost-complexity and reduced-error pruning algorithms on a number of data sets, with the advantage that no cross-validation is required.
Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation

Image-to-text tasks such as open-ended image captioning and controllable image description have received extensive attention for decades. Here we advance this line of work further, presenting Visual Spatial Description (VSD), a new perspective for image-to-text toward spatial semantics. Given an image and two objects inside it, VSD aims to produce one description focusing on the spatial perspective between the two objects. Accordingly, we annotate a dataset manually to facilitate the investigation of the newly-introduced task, and then build several benchmark encoder-decoder models by using VL-BART and VL-T5 as backbones. In addition, we investigate visual spatial relationship classification (VSRC) information into our model by pipeline and end-to-end architectures. Finally, we conduct experiments on our benchmark dataset to evaluate all our models. Results show that our models are awe-inspiring, offering accurate and human-like spatial-oriented text descriptions. Besides, VSRC has great potential for VSD, and the joint end-to-end architecture is the better choice for their integration. We will make the dataset and codes publicly available for research purposes.
Electromagnetic Assessment and AC Losses of Triaxial Cables with Multiple 2G-HTS Layers Per Phase

For an accurate estimation of the AC losses of superconducting triaxial cables, in this paper we present a two-dimensional model capable to provide a global assessment of multi-layer triaxial cables, validated against the reported AC-losses measurements on single-phase cables provided by the VNIIKP Cable Institute. Four models are presented, the ﬁrst being a single-phase cable of 50 tapes and the others being three triaxial cables made of up to 135 coated conductors distributed in up to 9 layers. A systematic study is devised, where the number of layers per phase increases from 1 to 3, with at least 14 tapes distributed across each layer of the ﬁrst (innermost) phase, 15 in the secondary (middle) phase, and 16 in the third (outermost) phase, respectively. Remarkably, our results reveal that the simple strategy of considering an unbalanced distribution for the amplitudes of the applied current, can generally balance the magnetic ﬁeld between the three phases even for the bilayer and trilayer cables, resulting in negligible magnetic leaks in all situations. Besides, our high-resolution simulations allow to see for the ﬁrst time how the transport and magnetization currents distribute across the thickness of all the superconducting tapes, from which we have found that the AC-losses of the 2nd phase is generally higher than at the other phases at low to moderate transport currents I tr < 0 . 8 I c . Nevertheless, depending on whether the I c of the SC tapes at the 3rd phase layers is lower than the one at the 2nd phase, the layers at the third phase can exhibit a considerable increment on the AC losses, this is as a result of the considered magneto angular anisotropy of the superconducting tapes, which lead to intriguing electromagnetic features that suggest a practical threshold for the applied transport current, being it 0 . 8 I c . Likewise, the relative change in the AC-losses per adding layers, per phase, and as a function of the entire range of applied transport current is disclosed.
Deep reinforcement learning oriented for real world dynamic scenarios

Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots. Recent deep reinforcement learning approaches show promising results to solve the problem, but it is not solved yet, as they typically assume no robot kinodynamic restrictions, holonomic movement or perfect environment knowledge. Moreover, most algorithms fail in the real world due to the inability to generate real-world training data for the huge variability of possible scenarios. In this work, we present a novel planner, DQN-DOVS, that uses deep reinforcement learning on a descriptive robocentric velocity space model to navigate in highly dynamic environments. It is trained using a smart curriculum learning approach on a simulator that faithfully reproduces the real world, reducing the gap between the reality and simulation. We test the resulting algorithm in scenarios with different number of obstacles and compare it with many state-of-the-art approaches, obtaining a better performance. Finally, we try the algorithm in a ground robot, using the same setup as in the simulation experiments.
Hypernetworks in Meta-Reinforcement Learning

Training a reinforcement learning (RL) agent on a real-world robotics task remains generally impractical due to sample inefficiency. Multi-task RL and meta-RL aim to improve sample efficiency by generalizing over a distribution of related tasks. However, doing so is difficult in practice: In multi-task RL, state of the art methods often fail to outperform a degenerate solution that simply learns each task separately. Hypernetworks are a promising path forward since they replicate the separate policies of the degenerate solution while also allowing for generalization across tasks, and are applicable to meta-RL. However, evidence from supervised learning suggests hypernetwork performance is highly sensitive to the initialization. In this paper, we 1) show that hypernetwork initialization is also a critical factor in meta-RL, and that naive initializations yield poor performance; 2) propose a novel hypernetwork initialization scheme that matches or exceeds the performance of a state-of-the-art approach proposed for supervised settings, as well as being simpler and more general; and 3) use this method to show that hypernetworks can improve performance in meta-RL by evaluating on multiple simulated robotics benchmarks.