Miracle-dz/UniFAR-FTUs huggingface dataset:
All you need is a good representation: A multi-level and classifier-centric representation for few-shot learning

The main problems of few-shot learning are how to learn a generalized representation and how to construct discriminant classifiers with few-shot samples. We tackle both issues by learning a multi-level representation with a classifier-centric constraint. We first build the multi-level representation by combining three different levels of information: local, global, and higher-level. The resulting representation can characterize new concepts with different aspects and present more universality. To overcome the difficulty of generating classifiers by several shot features, we also propose a classifier-centric loss for learning the representation of each level, which forces samples to be centered on their respective classifier weights in the feature space. Therefore, the multi-level representation learned with classifier-centric constraint not only can enhance the generalization ability, but also can be used to construct the discriminant classifier through a small number of samples. Experiments show that our proposed method, without training or fine-tuning on novel examples, can outperform the current state-of-the-art methods on two low-shot learning datasets. We further show that our approach achieves a significant improvement over baseline method in cross-task validation, and demonstrate its superiority in alleviating the domain shift problem.
Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning

In this work we explore the use of latent representations obtained from multiple input sensory modalities (such as images or sounds) in allowing an agent to learn and exploit policies over different subsets of input modalities. We propose a three-stage architecture that allows a reinforcement learning agent trained over a given sensory modality, to execute its task on a different sensory modality-for example, learning a visual policy over image inputs, and then execute such policy when only sound inputs are available. We show that the generalized policies achieve better out-of-the-box performance when compared to different baselines. Moreover, we show this holds in different OpenAI gym and video game environments, even when using different multimodal generative models and reinforcement learning algorithms.
Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music

Instrument classification is one of the fields in Music Information Retrieval (MIR) that has attracted a lot of research interest. However, the majority of that is dealing with monophonic music, while efforts on polyphonic material mainly focus on predominant instrument recognition. In this paper, we propose an approach for instrument classification in polyphonic music from predominantly monophonic data that involves performing data augmentation by mixing different audio segments. A variety of data augmentation techniques focusing on different sonic aspects, such as overlaying audio segments of the same genre, as well as pitch and tempo-based synchronization, are explored. We utilize Convolutional Neural Networks for the classification task, comparing shallow to deep network architectures. We further investigate the usage of a combination of the above classifiers, each trained on a single augmented dataset. An ensemble of VGG-like classifiers, trained on non-augmented, pitch-synchronized, tempo-synchronized and genre-similar excerpts, respectively, yields the best results, achieving slightly above 80% in terms of label ranking average precision (LRAP) in the IRMAS test set.
A lack of evolution in the very bright end of the galaxy luminosity function from z ≃ 8 to 10

We utilize deep near-infrared survey data from the UltraVISTA fourth data release (DR4) and the VIDEO survey, in combination with overlapping optical and Spitzer data, to search for bright star-forming galaxies at z ≳ 7.5. Using a full photometric redshift fitting analysis applied to the ∼6 $\, {\rm deg}^2$ of imaging searched, we find 27 Lyman break galaxies (LBGs), including 20 new sources, with best-fitting photometric redshifts in the range 7.4 < z < 9.1. From this sample, we derive the rest-frame UV luminosity function at z = 8 and z = 9 out to extremely bright UV magnitudes (MUV ≃ −23) for the first time. We find an excess in the number density of bright galaxies in comparison to the typically assumed Schechter functional form derived from fainter samples. Combined with previous studies at lower redshift, our results show that there is little evolution in the number density of very bright (MUV ∼ −23) LBGs between z ≃ 5 and z ≃ 9. The tentative detection of an LBG with best-fitting photometric redshift of z = 10.9 ± 1.0 in our data is consistent with the derived evolution. We show that a double power-law fit with a brightening characteristic magnitude (ΔM*/Δz ≃ −0.5) and a steadily steepening bright-end slope (Δβ/Δz ≃ −0.5) provides a good description of the z > 5 data over a wide range in absolute UV magnitude (−23 < MUV < −17). We postulate that the observed evolution can be explained by a lack of mass quenching at very high redshifts in combination with increasing dust obscuration within the first ${\sim}1 \, {\rm Gyr}$ of galaxy evolution.
RETRO: Relation Retrofitting For In-Database Machine Learning on Textual Data

There are massive amounts of textual data residing in databases, valuable for many machine learning (ML) tasks. Since ML techniques depend on numerical input representations, word embeddings are increasingly utilized to convert symbolic representations such as text into meaningful numbers. However, a naive one-to-one mapping of each word in a database to a word embedding vector is not sufficient and would lead to poor accuracies in ML tasks. Thus, we argue to additionally incorporate the information given by the database schema into the embedding, e.g. which words appear in the same column or are related to each other. In this paper, we propose RETRO (RElational reTROfitting), a novel approach to learn numerical representations of text values in databases, capturing the best of both worlds, the rich information encoded by word embeddings and the relational information encoded by database tables. We formulate relation retrofitting as a learning problem and present an efficient algorithm solving it. We investigate the impact of various hyperparameters on the learning problem and derive good settings for all of them. Our evaluation shows that the proposed embeddings are ready-to-use for many ML tasks such as classification and regression and even outperform state-of-the-art techniques in integration tasks such as null value imputation and link prediction.
Simulation-Based Reinforcement Learning for Real-World Autonomous Driving

We use reinforcement learning in simulation to obtain a driving system controlling a full-size real-world vehicle. The driving policy takes RGB images from a single camera and their semantic segmentation as input. We use mostly synthetic data, with labelled real-world data appearing only in the training of the segmentation network.Using reinforcement learning in simulation and synthetic data is motivated by lowering costs and engineering effort.In real-world experiments we confirm that we achieved successful sim-to-real policy transfer. Based on the extensive evaluation, we analyze how design decisions about perception, control, and training impact the real-world performance.
Stability of the Decoupled Extended Kalman Filter Learning Algorithm in LSTM-Based Online Learning

We investigate the convergence and stability properties of the decoupled extended Kalman filter learning algorithm (DEKF) within the long-short term memory network (LSTM) based online learning framework. For this purpose, we model DEKF as a perturbed extended Kalman filter and derive sufficient conditions for its stability during LSTM training. We show that if the perturbations -- introduced due to decoupling -- stay bounded, DEKF learns LSTM parameters with similar convergence and stability properties of the global extended Kalman filter learning algorithm. We verify our results with several numerical simulations and compare DEKF with other LSTM training methods. In our simulations, we also observe that the well-known hyper-parameter selection approaches used for DEKF in the literature satisfy our conditions.
A Contribution to Theory of Factor Income Distribution, Cambridge Capital Controversy and Equity Premium Puzzle

Under very general conditions, we construct a micro-macro model for closed economy with a large number of heterogeneous agents. By introducing both financial capital (i.e. valued capital---- equities of firms) and physical capital (i.e. capital goods), our framework gives a logically consistent, complete factor income distribution theory with micro-foundation. The model shows factor incomes obey different distribution rules at the micro and macro levels, while marginal distribution theory and no-arbitrage princi-ple are unified into a common framework. Our efforts solve the main problems of Cambridge capital controversy, and reasonably explain the equity premium puzzle. Strong empirical evidences support our results.
Structural analysis of massive galaxies using HST deep imaging at z < 0.5

Context. The most massive galaxies (Mstellar ≥ 1011 M⊙) in the local Universe are characterized by a bulge-dominated morphology and old stellar populations, in addition to being confined to a tight mass-size relation. Identifying their main components can provide insights into their formation mechanisms and subsequent mass assembly. Aims. Taking advantage of Hubble Space Telescope (HST) CANDELS data, we analyze the lowest redshift (z < 0.5) massive galaxies in the H and I band in order to disentangle their structural constituents and study possible faint non-axisymmetric features. Methods. Our final sample consists of 17 massive galaxies. Due to the excellent HST spatial resolution for intermediate redshift objects, they are hard to model by purely automatic parametric fitting algorithms. We performed careful single and double (bulge-disk decompositions) Sérsic fits to their galaxy surface brightness profiles. We compare the model color profiles with the observed ones and also derive multi-component global effective radii attempting to obtain a better interpretation of the mass-size relation. Additionally, we test the robustness of our measured structural parameters via simulations. Results. We find that the Sérsic index does not offer a good proxy for the visual morphological type for our sample of massive galaxies. Our derived multi-component effective radii give a better description of the size of our sample galaxies than those inferred from single Sérsic models with GALFIT. Our galaxy population lies on the scatter of the local mass-size relation, indicating that these massive galaxies have not experienced a significant growth in size since z ∼ 0.5. Interestingly, the few outliers are late-type galaxies, indicating that spheroids must reach the local mass-size relation earlier. For most of our sample galaxies, both single- and multi-component Sérsic models with GALFIT show substantial systematic deviations from the observed surface brightness profiles in the outskirts. These residuals may be partly due to several factors, namely a nonoptimal data reduction for low surface brightness features or the existence of prominent stellar haloes for massive galaxies, or they could also arise from conceptual shortcomings of parametric 2D image decomposition tools. They consequently propagate into galaxy color profiles. This is a significant obstacle to the exploration of the structural evolution of galaxies, which calls for a critical assessment and refinement of existing surface photometry techniques.
D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problems

Decentralized optimization algorithms have attracted intensive interests recently, as it has a balanced communication pattern, especially when solving large-scale machine learning problems. Stochastic Path Integrated Differential Estimator Stochastic First-Order method (SPIDER-SFO) nearly achieves the algorithmic lower bound in certain regimes for nonconvex problems. However, whether we can find a decentralized algorithm which achieves a similar convergence rate to SPIDER-SFO is still unclear. To tackle this problem, we propose a decentralized variant of SPIDER-SFO, called decentralized SPIDER-SFO (D-SPIDER-SFO). We show that D-SPIDER-SFO achieves a similar gradient computation cost—that is, O(ε−3) for finding an ϵ-approximate first-order stationary point—to its centralized counterpart. To the best of our knowledge, D-SPIDER-SFO achieves the state-of-the-art performance for solving nonconvex optimization problems on decentralized networks in terms of the computational cost. Experiments on different network configurations demonstrate the efficiency of the proposed method.
Rototaxis: Localization of active motion under rotation

The ability to navigate in complex, inhomogeneous environments is fundamental to survival at all length scales, giving rise to the rapid development of various subfields in bio-locomotion such as the well established concept of chemotaxis. In this work, we extend this existing notion of taxis to rotating environments and introduce the idea of "roto-taxis" to bio-locomotion. In particular, we explore both overdamped and inertial dynamics of a model synthetic self-propelled particle in the presence of constant global rotation, focusing on the particle's ability to localize near a rotation center as a survival strategy. We find that in the overdamped regime, the swimmer is in general able to generate a self restoring active torque that enables it to remain on stable epicyclical-like trajectories. On the other hand, for underdamped motion with inertial effects, the intricate competition between self-propulsion, centrifugal and Coriolis forces, in conjunction with the rototactic torque leads to complex dynamical behavior which we reveal by numerical simulations. Our results are relevant for a wide range of setups, from vibrated granular matter on turntables to microorganisms or animals swimming near swirls or vortices.
What’s Hidden in a Randomly Weighted Neural Network?

Training a neural network is synonymous with learning the values of the weights. By contrast, we demonstrate that randomly weighted neural networks contain subnetworks which achieve impressive performance without ever training the weight values. Hidden in a randomly weighted Wide ResNet-50 is a subnetwork (with random weights) that is smaller than, but matches the performance of a ResNet-34 trained on ImageNet. Not only do these ``untrained subnetworks" exist, but we provide an algorithm to effectively find them. We empirically show that as randomly weighted neural networks with fixed weights grow wider and deeper, an ``untrained subnetwork" approaches a network with learned weights in accuracy.
DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing

We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function. Due to the nature of the implicit function, the rendering process requires tremendous function queries, which is particularly problematic when the function is represented as a neural network. We optimize both the forward and backward pass of our rendering layer to make it run efficiently with affordable memory consumption on a commodity graphics card. Our rendering method is fully differentiable such that losses can be directly computed on the rendered 2D observations, and the gradients can be propagated backward to optimize the 3D geometry. We show that our rendering method can effectively reconstruct accurate 3D shapes from various inputs, such as sparse depth and multi-view images, through inverse optimization. With the geometry based reasoning, our 3D shape prediction methods show excellent generalization capability and robustness against various noises.
Predicting Performance of Software Configurations: There is no Silver Bullet

Many software systems offer configuration options to tailor their functionality and non-functional properties (e.g., performance). Often, users are interested in the (performance-)optimal configuration, but struggle to find it, due to missing information on influences of individual configuration options and their interactions. In the past, various supervised machine-learning techniques have been used to predict the performance of all configurations and to identify the optimal one. In the literature, there is a large number of machine-learning techniques and sampling strategies to select from. It is unclear, though, to what extent they affect prediction accuracy. We have conducted a comparative study regarding the mean prediction accuracy when predicting the performance of all configurations considering 6 machine-learning techniques, 18 sampling strategies, and 6 subject software systems. We found that both the learning technique and the sampling strategy have a strong influence on prediction accuracy. We further observed that some learning techniques (e.g., random forests) outperform other learning techniques (e.g., k-nearest neighbor) in most cases. Moreover, as the prediction accuracy strongly depends on the subject system, there is no combination of a learning technique and sampling strategy that is optimal in all cases, considering the tradeoff between accuracy and measurement overhead, which is in line with the famous no-free-lunch theorem.
Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation

The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is not guaranteed to do so and Weighted Expected Improvement does so only for a restricted range of weights. We introduce two novel -greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory, and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that -greedy algorithms are generally at least as effective as conventional acquisition functions (e.g., EI and UCB), particularly with a limited budget. In higher dimensions, -greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real-world computational fluid dynamics optimisation problem and a robotics active learning problem. Our analysis and experiments suggest that the most effective strategy, particularly in higher dimensions, is to be mostly greedy, occasionally selecting a random exploratory solution.
GraphZero: Breaking Symmetry for Efficient Graph Mining

Graph mining for structural patterns is a fundamental task in many applications. Compilation-based graph mining systems, represented by AutoMine, generate specialized algorithms for the provided patterns and substantially outperform other systems. However, the generated code causes substantial computation redundancy and the compilation process incurs too much overhead to be used online, both due to the inherent symmetry in the structural patterns. In this paper, we propose an optimizing compiler, GraphZero, to completely address these limitations through symmetry breaking based on group theory. GraphZero implements three novel techniques. First, its schedule explorer efficiently prunes the schedule space without missing any high-performance schedule. Second, it automatically generates and enforces a set of restrictions to eliminate computation redundancy. Third, it generalizes orientation, a surprisingly effective optimization that was mainly used for clique patterns, to apply to arbitrary patterns. Evaluated on multiple graph mining applications and complex patterns with 7 real-world graph datasets, GraphZero demonstrates up to 40X performance improvement and up to 197X reduction on schedule generation overhead over AutoMine.
Siam R-CNN: Visual Tracking by Re-Detection

We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with a novel tracklet-based dynamic programming algorithm, which takes advantage of re-detections of both the first-frame template and previous-frame predictions, to model the full history of both the object to be tracked and potential distractor objects. This enables our approach to make better tracking decisions, as well as to re-detect tracked objects after long occlusion. Finally, we propose a novel hard example mining strategy to improve Siam R-CNN's robustness to similar looking objects. Siam R-CNN achieves the current best performance on ten tracking benchmarks, with especially strong results for long-term tracking. We make our code and models available at www.vision. rwth-aachen.de/page/siamrcnn.
Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA

We propose a novel non-parametric method for cross-modal retrieval which is applied on top of precomputed image and text embeddings. By combining our method with standard approaches for building image and text encoders, trained independently with a self-supervised classification objective, we create a baseline model which outperforms most existing methods on a challenging image-to-recipe task. We also use our method for comparing image and text encoders trained using different modern approaches, thus addressing the issues hindering the developments of novel methods for cross-modal recipe retrieval. We demonstrate how to use the insights from model comparison and extend our baseline model with standard triplet loss that improves SoTA on the Recipe1M dataset by a large margin, while using only precomputed features and with much less complexity than existing methods.
Generalised Kähler structure on $ \mathbb{C}P^2 $ and elliptic functions

We construct a toric generalised Kähler structure on $ \mathbb{C}P^2 $ and show that the various structures such as the complex structure, metric etc are expressed in terms of certain elliptic functions. We also compute the generalised Kähler potential in terms of integrals of elliptic functions.