Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapting to Unseen Environments through Explicit Representation of Context

In order to deploy autonomous agents to domains such as autonomous driving, infrastructure management, health care, and finance, they must be able to adapt safely to unseen situations. The current approach in constructing such agents is to try to include as much variation into training as possible, and then generalize within the possible variations. This paper proposes a principled approach where a context module is coevolved with a skill module. The context module recognizes the variation and modulates the skill module so that the entire system performs well in unseen situations. The approach is evaluated in a challenging version of the Flappy Bird game where the effects of the actions vary over time. The Context+Skill approach leads to significantly more robust behavior in environments with previously unseen effects. Such a principled generalization ability is essential in deploying autonomous agents in real world tasks, and can serve as a foundation for continual learning as well.
Near-Optimal Hardware Design for Convolutional Neural Networks

Recently, the demand of low-power deep-learning hardware for industrial applications has been increasing. Most existing artificial intelligence (AI) chips have evolved to rely on new chip technologies rather than on radically new hardware architectures, to maintain their generality. This study proposes a novel, special-purpose, and high-efficiency hardware architecture for convolutional neural networks. The proposed architecture maximizes the utilization of multipliers by designing the computational circuit with the same structure as that of the computational flow of the model, rather than mapping computations to fixed hardware. In addition, a specially designed filter circuit simultaneously provides all the data of the receptive field, using only one memory read operation during each clock cycle; this allows the computation circuit to operate seamlessly without idle cycles. Our reference system based on the proposed architecture uses 97% of the peak-multiplication capability in actual computations required by the computation model throughout the computation period. In addition, overhead components are minimized so that the proportion of the resources constituting the non-multiplier components is smaller than that constituting the multiplier components, which are indispensable for the computational model. The efficiency of the proposed architecture is close to an ideally efficient system that cannot be improved further in terms of the performance-to-resource ratio. An implementation based on the proposed hardware architecture has been applied in commercial AI products.
Likelihood Based Learning Rule for Temporal Coding In Recurrent Spiking Neural Networks

Recurrent spiking neural networks (RSNN) in the brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of time and energy consumption. Moreover, learning can happen rapidly after very few examples. This is due to the optimality of coding and learning schemes, which have yet to be clearly understood. This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones. Several spiking network models have been proposed but it remains a challenge to design RSNNs that use biologically plausible mechanisms capable of solving complex temporal tasks. We use a general probabilistic framework that relies on the principle of maximizing the likelihood for the network to reproduce some temporal dynamics. This principle permits to analytically work out an explicit and biologically plausible plasticity rule. Here we propose a novel target-based learning scheme in which such a rule can be used to efficiently train a RSNN to solve several temporal tasks such as learning multidimensional trajectory and an implementation of the temporal XOR. We finally show that an online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output. Our model is general and it can be applied to a wide variety of network architectures and types of biological neurons. The derived plasticity learning rule is specific to each neuron model and can produce a theoretical prediction to be experimentally verified.
Triggering mixing and deceleration in FRI jets: a solution

Since Fanaroff & Riley (1974) reported the morphological and brightness dichotomy of radiogalaxies, and it became clear that the symmetric emission from jets and counter-jets in the centre-brightened, less powerful, FRI sources could be caused by jet deceleration, many works have addressed different mechanisms that could cause this difference. Recent observational results seem to indicate that the deceleration must be caused by the development of small-scale instabilities that force mixing at the jet boundary. According to these results, the mixing layer expands and propagates down to the jet axis along several kiloparsecs, until it covers the whole jet cross-section. Several candidate mechanisms have been proposed as the initial trigger for the generation of such mixing layer. However, the instabilities proposed so far do not fully manage to explain the observations of FRI jets and/or require a triggering mechanism. Therefore, there is not still a satisfactory explanation for the original cause of jet deceleration. In this letter, I show that the penetration (and exit) of stars from jets could give the adequate explanation by means of creating a jet-interstellar medium mixing layer that expands across the jet.
Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization

Epoch gradient descent method (a.k.a. Epoch-GD) proposed by (Hazan and Kale, 2011) was deemed a breakthrough for stochastic strongly convex minimization, which achieves the optimal convergence rate of O(1/T) with T iterative updates for the objective gap. However, its extension to solving stochastic min-max problems with strong convexity and strong concavity still remains open, and it is still unclear whether a fast rate of O(1/T) for the duality gap is achievable for stochastic min-max optimization under strong convexity and strong concavity. Although some recent studies have proposed stochastic algorithms with fast convergence rates for min-max problems, they require additional assumptions about the problem, e.g., smoothness, bi-linear structure, etc. In this paper, we bridge this gap by providing a sharp analysis of epoch-wise stochastic gradient descent ascent method (referred to as Epoch-GDA) for solving strongly convex strongly concave (SCSC) min-max problems, without imposing any additional assumptions about smoothness or its structure. To the best of our knowledge, our result is the first one that shows Epoch-GDA can achieve the fast rate of O(1/T) for the duality gap of general SCSC min-max problems. We emphasize that such generalization of Epoch-GD for strongly convex minimization problems to Epoch-GDA for SCSC min-max problems is non-trivial and requires novel technical analysis. Moreover, we notice that the key lemma can be also used for proving the convergence of Epoch-GDA for weakly-convex strongly-concave min-max problems, leading to the best complexity as well without smoothness or other structural conditions.
Machines Learn Appearance Bias in Face Recognition

We seek to determine whether state-of-the-art, black box face recognition techniques can learn first-impression appearance bias from human annotations. With FaceNet, a popular face recognition architecture, we train a transfer learning model on human subjects' first impressions of personality traits in other faces. We measure the extent to which this appearance bias is embedded and benchmark learning performance for six different perceived traits. In particular, we find that our model is better at judging a person's dominance based on their face than other traits like trustworthiness or likeability, even for emotionally neutral faces. We also find that our model tends to predict emotions for deliberately manipulated faces with higher accuracy than for randomly generated faces, just like a human subject. Our results lend insight into the manner in which appearance biases may be propagated by standard face recognition models.
Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry

Network congestion games are a well-understood model of multi-agent strategic interactions. Despite their ubiquitous applications, it is not clear whether it is possible to design information structures to ameliorate the overall experience of the network users. We focus on Bayesian games with atomic players, where network vagaries are modeled via a (random) state of nature which determines the costs incurred by the players. A third-party entity—the sender—can observe the realized state of the network and exploit this additional information to send a signal to each player. A natural question is the following: is it possible for an informed sender to reduce the overall social cost via the strategic provision of information to players who update their beliefs rationally? The paper focuses on the problem of computing optimal ex ante persuasive signaling schemes, showing that symmetry is a crucial property for its solution. Indeed, we show that an optimal ex ante persuasive signaling scheme can be computed in polynomial time when players are symmetric and have affine cost functions. Moreover, the problem becomes NP-hard when players are asymmetric, even in non-Bayesian settings.
Stochastic Approximate Gradient Descent via the Langevin Algorithm

We introduce a novel and efficient algorithm called the stochastic approximate gradient descent (SAGD), as an alternative to the stochastic gradient descent for cases where unbiased stochastic gradients cannot be trivially obtained. Traditional methods for such problems rely on general-purpose sampling techniques such as Markov chain Monte Carlo, which typically requires manual intervention for tuning parameters and does not work efficiently in practice. Instead, SAGD makes use of the Langevin algorithm to construct stochastic gradients that are biased in finite steps but accurate asymptotically, enabling us to theoretically establish the convergence guarantee for SAGD. Inspired by our theoretical analysis, we also provide useful guidelines for its practical implementation. Finally, we show that SAGD performs well experimentally in popular statistical and machine learning problems such as the expectation-maximization algorithm and the variational autoencoders.
Fine-tuning the H-scan for visualizing types of tissue scatterers.

The H-scan approach is a matched filter methodology that aims to add information to the traditional ultrasound B-scan. The theory is based on the differences in the echoes produced by different classes of reflectors or scatterers. Matched filters can be created for different types of scatterers, whereby the maximum output indicates a match and color schemes can be used to indicate the class of scatterer responsible for echoes, providing a visual interpretation of the results. However, within the theory of weak scattering from a variety of shapes, small changes in the size of the inhomogeneous objects will create shifts in the scattering transfer function. In this paper, we determine the shift in center frequency, along with time and frequency domain characteristics of echoes and argue for a general power law transfer function to capture the major characteristics of scattering types. With this general approach, the H-scan matched filters can be set to elicit more fine grain shifts in scattering types, commensurate with more subtle changes in tissue morphology. Compensation for frequency dependent attenuation is helpful for avoiding beam softening effects with increasing depths. Examples from phantoms and normal and pathological tissues are provided to demonstrate that the H-scan analysis and displays are sensitive to scatterer size and morphology, and can be adapted to conventional imaging systems.
Geometric spin–orbit coupling and chirality-induced spin selectivity

We report a new type of spin–orbit coupling (SOC) called geometric SOC. Starting from the relativistic theory in curved space, we derive an effective nonrelativistic Hamiltonian in a generic curve embedded into flat three dimensions. The geometric SOC is O(m −1), in which m is the electron mass, and hence much larger than the conventional SOC of O(m −2). The energy scale is estimated to be a hundred meV for a nanoscale helix. We calculate the current-induced spin polarization in a coupled-helix model as a representative of the chirality-induced spin selectivity. We find that it depends on the chirality of the helix and is of the order of 0. 01ℏ per nm when a charge current of 1 μA is applied.
Simple and Accurate Uncertainty Quantification from Bias-Variance Decomposition

Accurate uncertainty quantification is crucial for many applications where decisions are in play. Examples include medical diagnosis and self-driving vehicles. We propose a new method that is based directly on the bias-variance decomposition, where the parameter uncertainty is given by the variance of an ensemble divided by the number of members in the ensemble, and the aleatoric uncertainty plus the squared bias is estimated by training a separate model that is regressed directly on the errors of the predictor. We demonstrate that this simple sequential procedure provides much more accurate uncertainty estimates than the current state-of-the-art on two MRI reconstruction tasks.
Coherent gravitational waveforms and memory from cosmic string loops

We construct, for the first time, the time-domain gravitational wave strain waveform from the collapse of a strongly gravitating Abelian Higgs cosmic string loop in full general relativity. We show that the strain exhibits a large memory effect during merger, ending with a burst and the characteristic ringdown as a black hole is formed. Furthermore, we investigate the waveform and energy emitted as a function of string width, loop radius and string tension Gμ. We find that the mass normalized gravitational wave energy displays a strong dependence on the inverse of the string tension E GW/M 0 ∝ 1/Gμ, with EGW/M0∼O(1)% at the percent level, for the regime where Gμ ≳ 10−3. Conversely, we show that the efficiency is only weakly dependent on the initial string width and initial loop radii. Using these results, we argue that gravitational wave production is dominated by kinematical instead of geometrical considerations.
Resolving Spurious Correlations in Causal Models of Environments via Interventions

Causal models could increase interpretability, robustness to distributional shift and sample efficiency of RL agents. In this vein, we address the question of learning a causal model of an RL environment. This problem is known to be difficult due to spurious correlations. We overcome this difficulty by rewarding an RL agent for designing and executing interventions to discover the true model. We compare rewarding the agent for disproving uncertain edges in the causal graph, rewarding the agent for activating a certain node, or rewarding the agent for increasing the causal graph loss. We show that our methods result in a better causal graph than one generated by following the random policy, or a policy trained on the environment's reward. We find that rewarding for the causal graph loss works the best.
Deep-Reinforcement-Learning-Based Mode Selection and Resource Allocation for Cellular V2X Communications

Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle (V2V) links, and high signaling overhead of centralized resource allocation approaches become bottlenecks. In this article, we investigate a joint optimization problem of transmission mode selection and resource allocation for cellular V2X communications. In particular, the problem is formulated as a Markov decision process, and a deep reinforcement learning (DRL)-based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs. Moreover, considering training limitation of local DRL models, a two-timescale federated DRL algorithm is developed to help obtain robust models. Wherein, the graph theory-based vehicle clustering algorithm is executed on a large timescale and in turn, the federated learning algorithm is conducted on a small timescale. The simulation results show that the proposed DRL-based algorithm outperforms other decentralized baselines, and validate the superiority of the two-timescale federated DRL algorithm for newly activated V2V pairs.
Effect of pH on the complex coacervation and on the formation of layers of sodium alginate and PDADMAC.

In this study, we investigated the thermodynamic features of a system based on oppositely charged polyelectrolytes, sodium alginate, and poly(diallyldimethylammonium chloride) (PDADMAC) at different pH values. Additionally, a comparison of the effects of the thermodynamic parameters on the growth of the layers based on the same polymers is presented. For this investigation, different techniques were combined to compare results from the association in solution and co-assembled layers at the silicon surface. Dynamic light scattering (DLS) and isothermal titration calorimetry (ITC) were used for studies in solution, and the layer-by-layer technique was employed for the preparation of the polymer layers. Ellipsometry and atomic force microscopy (AFM) were used to characterize the layer thickness growth as a function of the solution pH, and interferometric confocal microscopy was employed to analyze the topography and roughness of the films. The titration of both polyelectrolytes in two different sequences of additions confirmed the mechanism; it involved a two-step process that was monitored by varying the enthalpy, as determined by ITC experiments, and the structural evolution of the aggregates into coacervates, according to DLS. The primary process is aggregation to form polyelectrolyte complexes having a smaller hydrodynamic diameter, which abruptly transit toward a secondary process because of the formation of coacervate particles that have a larger hydrodynamic diameter. Independent of pH and the sequence of addition, for the first process, both directions are entropically driven. However, the binding enthalpy (Hb) decreased with a decrease in the pH of the solution. The layers grown for the PDADMAC/sodium alginate system demonstrated pH sensitivity with either linear or exponential behavior, depending on the pH values of the polyelectrolyte solutions. The more endothermic process at pH 10 afforded layers with a smaller thickness and with linear growth according to the increase in the number of layers from 5 to 20. Decreases in the pH of the solution resulted in the layers growing exponentially; additionally, a decrease in the Hb of the association in the solution was observed. The layer thicknesses measured using ellipsometry and AFM data were in good agreement. Additionally, a pH influence on the roughness and topography of the films was observed. Films from basic dipping solutions resulted in surfaces that were more homogeneous with less roughness; in contrast, films with more layers and those formed in a low-pH dipping solution were rougher and less homogeneous.
The regular stochastic block model on several-community networks

This paper studies the regular stochastic block model comprising several communities: each of the $k$ non-overlapping communities, for $k \geqslant 3$, possesses $n$ vertices, each of which has total degree $d$. The values of the intra-cluster degrees (i.e. the number of neighbours of a vertex inside the cluster it belongs to) and the inter-cluster degrees (i.e. the number of neighbours of a vertex inside a cluster different from its own) are allowed to vary across clusters. We discuss three main results. The first of these compares the probability measure induced by our model with the uniform measure on the space of $d$-regular graphs on $kn$ vertices; the second establishes that the clusters, under rather weak assumptions, are unique asymptotically almost surely as $n \rightarrow \infty$; the third shows that efficient weak recovery of the clusters is possible under suitable assumptions on the eigenvalues of the $k \times k$ matrix of intra-cluster and inter-cluster degrees.
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

Domain generalization is the problem of machine learning when the training data and the test data come from different data domains. We present a simple theoretical model of learning to generalize across domains in which there is a meta-distribution over data distributions, and those data distributions may even have different supports. In our model, the training data given to a learning algorithm consists of multiple datasets each from a single domain drawn in turn from the meta-distribution. We study this model in three different problem settings---a multi-domain Massart noise setting, a decision tree multi-dataset setting, and a feature selection setting, and find that computationally efficient, polynomial-sample domain generalization is possible in each. Experiments demonstrate that our feature selection algorithm indeed ignores spurious correlations and improves generalization.
Deep Learning-Based End-to-End Diagnosis System for Avascular Necrosis of Femoral Head

As the first diagnostic imaging modality of avascular necrosis of the femoral head (AVNFH), accurately staging AVNFH from a plain radiograph is critical yet challenging for orthopedists. Thus, we propose a deep learning-based AVNFH diagnosis system (AVN-net). The proposed AVN-net reads plain radiographs of the pelvis, conducts diagnosis, and visualizes results automatically. Deep convolutional neural networks are trained to provide an end-to-end diagnosis solution, covering tasks of femoral head detection, exam-view identification, side classification, AVNFH diagnosis, and key clinical notes generation. AVN-net is able to obtain state-of-the-art testing AUC of 0.97 (<inline-formula><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula> CI: <inline-formula><tex-math notation="LaTeX">$0.97-0.98$</tex-math></inline-formula>) in AVNFH detection and significantly greater F1 scores than less-to-moderately experienced orthopedists in all diagnostic tests (<inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> < 0.01). Furthermore, two real-world pilot studies were conducted for diagnosis support and education assistance, respectively, to assess the utility of AVN-net. The experimental results are promising. With the AVN-net diagnosis as a reference, the diagnostic accuracy and consistency of all orthopedists considerably improved while requiring only 1/4 of the time. Students self-studying the AVNFH diagnosis using AVN-net can learn better and faster than the control group. To the best of our knowledge, this study is the first research on the prospective use of a deep learning-based diagnosis system for AVNFH by conducting two pilot studies representing real-world application scenarios. We have demonstrated that the proposed AVN-net achieves expert-level AVNFH diagnosis performance, provides efficient support in clinical decision-making, and effectively passes clinical experience to students.