Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks

Recent work suggests that feature constraints in the training datasets of deep neu- 1 ral networks (DNNs) drive robustness to adversarial noise (Ilyas et al., 2019). 2 The representations learned by such adversarially robust networks have also been 3 shown to be more human perceptually-aligned than non-robust networks via image 4 manipulations (Santurkar et al., 2019; Engstrom et al., 2019). Despite appearing 5 closer to human visual perception, it is unclear if the constraints in robust DNN 6 representations match biological constraints found in human vision. Human vision 7 seems to rely on texture-based/summary statistic representations in the periphery, 8 which have been shown to explain phenomena such as crowding (Balas et al., 2009) 9 and performance on visual search tasks (Rosenholtz et al., 2012). To understand 10 how adversarially robust optimizations/representations compare to human vision, 11 we performed a psychophysics experiment using a metamer task similar to Freeman 12 & Simoncelli (2011); Wallis et al. (2019); Deza et al. (2017) where we evaluated 13 how well human observers could distinguish between images synthesized to match 14 adversarially robust representations compared to non-robust representations and a 15 texture synthesis model of peripheral vision (Texforms (Long et al., 2018)). We 16 found that the discriminability of robust representation and texture model images 17 decreased to near chance performance as stimuli were presented farther in the 18 periphery. Moreover, performance on robust and texture-model images showed 19 similar trends within participants, while performance on non-robust representa- 20 tions changed minimally across the visual ﬁeld. These results together suggest 21 that (1) adversarially robust representations capture peripheral computation better 22 than non-robust representations and (2) robust representations capture peripheral 23 computation similar to current state-of-the-art texture peripheral vision models. 24 More broadly, our ﬁndings support the idea that localized texture summary statis- 25 tic representations may drive human invariance to adversarial
Gradient Based Clustering

We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluster center positions. The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range of functions, satisfying some mild assumptions. The main advantage of the proposed approach is a simple and computationally cheap update rule. Unlike previous methods that specialize to a specific formulation of the clustering problem, our approach is applicable to a wide range of costs, including non-Bregman clustering methods based on the Huber loss. We analyze the convergence of the proposed algorithm, and show that it converges to the set of appropriately defined fixed points, under arbitrary center initialization. In the special case of Bregman cost functions, the algorithm converges to the set of centroidal Voronoi partitions, which is consistent with prior works. Numerical experiments on real data demonstrate the effectiveness of the proposed method.
Rapid measurement of the deterioration of oil immersed paper

The thermal deterioration of oil immersed paper was investigated by mass spectrometer and gas chromatograph. The results obtained are as follows: 1. It was shown that a new assembly using a mercury diffusion pump and Topler pump is very effective for extracting small amounts of gas from oil. 2. The thermal deterioration of kraft paper in oil can be studied over a wide temperature range from high temperatures to normal working temperatures. The thermal deterioration characteristic of kraft paper shown by the logarithm of the generation rate of (CO + CO2) = (Δq) vs 1/T is generally made of two offset line segments of differing slopes. This result differs from those for other materials.3, 4 3. The slope of log (1/ Δq) vs 1/T curve above described co-incides with the slope of log (life) vs 1/T plot obtained in measurements of the mean degree of polymerization. 4. According to the above results, the thermal life of kraft paper in oil at normal working temperatures can be estimated using together two types of information: (a) the slope of log (1/ Δq) vs 1/T and (b) the results of actual life tests made at much higher than normal temperatures.
Quadratic-Gradient Metasurface-Dome for Wide-Angle Beam-Steering Phased Array With Reduced Gain Loss at Broadside

Recently, the use of deflecting covering meta-domes for increasing the scanning range of a phased array has been proposed. Although promising, this solution still presents some inherent limitations, such as the significant reduction of the broadside gain compared to the original array, as well as the complexity of the implementation due to the need for a continuous phase profile. This work aims at proposing some technical solutions for maximizing the meta-dome performance and reducing the gain-loss at broadside. By properly discretizing and engineering the phase profile along the dome and by taking into account the different angles of incidence onto the meta-cells, we show how it is possible designing realistic meta-domes with reduced insertion loss at broadside, improved steering capabilities, and a reduced profile.
Ultra-Wideband Teach and Repeat

Autonomously retracing a manually-taught path is desirable for many applications, and Teach and Repeat (T&R) algorithms present an approach that is suitable for long-range autonomy. In this paper, ultra-wideband (UWB) ranging-based T&R is proposed for vehicles with limited resources. By fixing single UWB transceivers at far-apart unknown locations in an indoor environment, a robot with 3 UWB transceivers builds a locally consistent map during the teach pass by fusing the range measurements under a custom ranging protocol with an on-board IMU and height measurements. The robot then uses information from the teach pass to retrace the same trajectory autonomously. The proposed ranging protocol and T&R algorithm are validated in simulation, where it is shown that the robot can successfully retrace the taught trajectory with sub-metre tracking error.
Learning to reason about and to act on physical cascading events

Reasoning and interacting with dynamic environments is a fundamental problem in AI, but it becomes extremely challenging when actions can trigger cascades of cross-dependant events. We introduce a new supervised learning setup called Cascade where an agent is shown a video of a physically simulated dynamic scene, and is asked to intervene and trigger a cascade of events, such that the system reaches a "counterfactual" goal. For instance, the agent may be asked to “Make the blue ball hit the red one, by pushing the green ball”. The agent intervention is drawn from a continuous space, and cascades of events makes the dynamics highly non-linear. We combine semantic tree search with an eventdriven forward model and devise an algorithm that learns to search in semantic trees in continuous spaces. We demonstrate that our approach learns to effectively follow instructions to intervene in previously unseen complex scenes. It can also reason about alternative outcomes, when provided an observed cascade of events.
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners

Post-hoc explanations for black box models have been studied extensively in classification and regression settings. However, explanations for models that output similarity between two inputs have received comparatively lesser attention. In this paper, we provide model agnostic local explanations for similarity learners applicable to tabular and text data. We first propose a method that provides feature attributions to explain the similarity between a pair of inputs as determined by a black box similarity learner. We then propose analogies as a new form of explanation in machine learning. Here the goal is to identify diverse analogous pairs of examples that share the same level of similarity as the input pair and provide insight into (latent) factors underlying the model’s prediction. The selection of analogies can optionally leverage feature attributions, thus connecting the two forms of explanation while still maintaining complementarity. We prove that our analogy objective function is submodular, making the search for good-quality analogies efficient. We apply the proposed approaches to explain similarities between sentences as predicted by a state-of-the-art sentence encoder, and between patients in a healthcare utilization application. Efficacy is measured through quantitative evaluations, a careful user study, and examples of explanations.
Seimei KOOLS-IFU Mapping of the Gas and Dust Distributions in Galactic Planetary Nebulae: the Case of IC2165

We investigated the physical and chemical properties of the gas and dust components in a carbon-rich planetary nebula (PN) IC2165 using two-dimensional (2-D) emission-line maps with superior resolution. The extinction map is generated in a self-consistent and assumption-free manner. The circumstellar gas-to-dust mass ratio (GDR) map ranges radially from 1210 in the central nebula filled with hot gas plasma to 120 near the ionisation front. The determined GDR is comparable to ~400, which is commonly adopted for carbon-rich asymptotic giant branch (AGB) stars, and ~100 for ISM. Except for the inner regions, the GDR in IC2165 is nearly the same as in such AGB stars, indicating that most dust grains withstand the harsh radiation field without being destroyed. The gas and dust mass distributions concentrated in the equatorial plane may be related to the nonisotropic mass loss during the AGB phase and nebula shaping. The spatial distributions of electron densities/temperatures and ionic/elemental abundances were investigated herein. We determined 13 elemental abundances using PSF-matched spatially integrated multiwavelength spectra extracted from the same aperture. Their values are consistent with values predicted by a theoretical model for stars of initially 1.75 Msun and Z = 0.003. Finally, we constructed the photoionisation model using our distance measurement to be consistent with all derived quantities, including the GDR and gas and dust masses and post-AGB evolution. Thus, we demonstrate the capability of Seimei/KOOLS-IFU and how the spatial variation of the gas and dust components in PNe derived from IFU observations can help understand the evolution of the circumstellar/interstellar medium.
Adaptive Experimentation with Delayed Binary Feedback

Conducting experiments with objectives that take significant delays to materialize (e.g. conversions, add-to-cart events, etc.) is challenging. Although the classical “split sample testing” is still valid for the delayed feedback, the experiment will take longer to complete, which also means spending more resources on worse-performing strategies due to their fixed allocation schedules. Alternatively, adaptive approaches such as “multi-armed bandits” are able to effectively reduce the cost of experimentation. But these methods generally cannot handle delayed objectives directly out of the box. This paper presents an adaptive experimentation solution tailored for delayed binary feedback objectives by estimating the real underlying objectives before they materialize and dynamically allocating variants based on the estimates. Experiments show that the proposed method is more efficient for delayed feedback compared to various other approaches and is robust in different settings. In addition, we describe an experimentation product powered by this algorithm. This product is currently deployed in the online experimentation platform of JD.com, a large e-commerce company and a publisher of digital ads.
An Adiabatic Capacitive Artificial Neuron With RRAM-Based Threshold Detection for Energy-Efficient Neuromorphic Computing

In the quest for low power, bio-inspired computation both memristive and memcapacitive-based Artificial Neural Networks (ANN) have been the subjects of increasing focus for hardware implementation of neuromorphic computing. One step further, regenerative capacitive neural networks, which call for the use of adiabatic computing, offer a tantalising route towards even lower energy consumption, especially when combined with ‘memimpedace’ elements. Here, we present an artificial neuron featuring adiabatic synapse capacitors to produce membrane potentials for the somas of neurons; the latter implemented via dynamic latched comparators augmented with Resistive Random-Access Memory (RRAM) devices. Our initial 4-bit adiabatic capacitive neuron proof-of-concept example shows 90% synaptic energy saving. At 4 synapses/soma we already witness an overall 35% energy reduction. Furthermore, the impact of process and temperature on the 4-bit adiabatic synapse shows a maximum energy variation of 30% at $100^{o}C$ across the corners without any functionality loss. Finally, the efficacy of our adiabatic approach to ANN is tested for 512 & 1024 synapse/neuron for worst and best case synapse loading conditions and variable equalising capacitance’s quantifying the expected trade-off between equalisation capacitance and range of optimal power-clock frequencies vs. loading (i.e. the percentage of active synapses).
Keyword Localisation in Untranscribed Speech Using Visually Grounded Speech Models

Keyword localisation is the task of finding where in a speech utterance a given query keyword occurs. We investigate to what extent keyword localisation is possible using a visually grounded speech (VGS) model. VGS models are trained on unlabelled images paired with spoken captions. These models are therefore self-supervised—trained without any explicit textual label or location information. To obtain training targets, we first tag training images with soft text labels using a pretrained visual classifier with a fixed vocabulary. This enables a VGS model to predict the presence of a written keyword in an utterance, but not its location. We consider four ways to equip VGS models with localisations capabilities. Two of these—a saliency approach and input masking—can be applied to an arbitrary prediction model after training, while the other two—attention and a score aggregation approach—are incorporated directly into the structure of the model. Masked-based localisation gives some of the best reported localisation scores from a VGS model, with an accuracy of 57% when the system knows that a keyword occurs in an utterance and need to predict its location. In a setting where localisation is performed after detection, an $F_{1}$ of 25% is achieved, and in a setting where a keyword spotting ranking pass is first performed, a localisation $P{@}10$ of 32% is obtained. While these scores are modest compared to the idealised setting with unordered bag-of-word-supervision (from transcriptions), these VGS models do not receive any textual or location supervision. Further analyses show that these models are limited by the first detection or ranking pass. Moreover, individual keyword localisation performance is correlated with the tagging performance from the visual classifier. We also show qualitatively how and where semantic mistakes occur, e.g. that the model locates surfer when queried with ocean.
Tailoring Gradient Methods for Differentially Private Distributed Optimization

Decentralized optimization is gaining increased traction due to its widespread applications in large-scale machine learning and multiagent systems. The same mechanism that enables its success, i.e., information sharing among participating agents, however, also leads to the disclosure of individual agents' private information, which is unacceptable when sensitive data are involved. As differential privacy is becoming a de facto standard for privacy preservation, recently results have emerged integrating differential privacy with distributed optimization. However, directly incorporating differential privacy design in existing distributed optimization approaches significantly compromises optimization accuracy. In this article, we propose to redesign and tailor gradient methods for differentially private distributed optimization, and propose two differential-privacy oriented gradient methods that can ensure both rigorous $\epsilon$-differential privacy and optimality. The first algorithm is based on static-consensus-based gradient methods, and the second algorithm is based on dynamic-consensus (gradient-tracking) based distributed optimization methods and, hence, is applicable to general directed interaction graph topologies. Both algorithms can simultaneously ensure almost sure convergence to an optimal solution and a finite privacy budget, even when the number of iterations goes to infinity. To the best of authors' knowledge, this is the first time that both goals are achieved simultaneously. Numerical simulations using a distributed estimation problem and experimental results on a benchmark dataset confirm the effectiveness of the proposed approaches.
Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions

Performance of optimization on quadratic problems sensitively depends on the low-lying part of the spectrum. For large (effectively infinite-dimensional) problems, this part of the spectrum can often be naturally represented or approximated by power law distributions, resulting in power law convergence rates for iterative solutions of these problems by gradient-based algorithms. In this paper, we propose a new spectral condition providing tighter upper bounds for problems with power law optimization trajectories. We use this condition to build a complete picture of upper and lower bounds for a wide range of optimization algorithms -- Gradient Descent, Steepest Descent, Heavy Ball, and Conjugate Gradients -- with an emphasis on the underlying schedules of learning rate and momentum. In particular, we demonstrate how an optimally accelerated method, its schedule, and convergence upper bound can be obtained in a unified manner for a given shape of the spectrum. Also, we provide first proofs of tight lower bounds for convergence rates of Steepest Descent and Conjugate Gradients under spectral power laws with general exponents. Our experiments show that the obtained convergence bounds and acceleration strategies are not only relevant for exactly quadratic optimization problems, but also fairly accurate when applied to the training of neural networks.
Low-Rank and Row-Sparse Decomposition for Joint DOA Estimation and Distorted Sensor Detection

Distorted sensors could occur randomly and may lead to the breakdown of a sensor array system. In this article, we consider an array model within which a small number of sensors are distorted by unknown sensor gain and phase errors. With such an array model, the problem of joint direction-of-arrival (DOA) estimation and distorted sensor detection is formulated under the framework of low-rank and row-sparse decomposition. We derive an iteratively reweighted least squares (IRLS) algorithm to solve the resulting problem. The convergence property of the IRLS algorithm is analyzed by means of the monotonicity and boundedness of the objective function. Extensive simulations are conducted regarding parameter selection, convergence speed, computational complexity, and performances of DOA estimation as well as distorted sensor detection. Even though the IRLS algorithm is slightly worse than the alternating direction method of multipliers in detecting the distorted sensors, the results show that our approach outperforms several state-of-the-art techniques in terms of convergence speed, computational cost, and DOA estimation performance.
AGN impact on the molecular gas in galactic centers as probed by CO lines

We present a detailed analysis of the X-ray, infrared, and carbon monoxide (CO) emission for a sample of 35 local (z ≤ 0.15), active (LX ≥ 1042 erg s−1) galaxies. Our goal is to infer the contribution of far-ultraviolet (FUV) radiation from star formation (SF), and X-ray radiation from the active galactic nuclei (AGN), respectively producing photodissociation regions (PDRs) and X-ray dominated regions (XDRs), to the molecular gas heating. To this aim, we exploit the CO spectral line energy distribution (CO SLED) as traced by Herschel, complemented with data from single-dish telescopes for the low-J lines, and high-resolution ALMA images of the mid-J CO emitting region. By comparing our results to the Schmidt-Kennicutt relation, we find no evidence for AGN influence on the cold and low-density gas on kpc-scales. On nuclear (r = 250 pc) scales, we find weak correlations between the CO line ratios and either the FUV or X-ray fluxes: this may indicate that neither SF nor AGN radiation dominates the gas excitation, at least at r = 250 pc. From a comparison of the CO line ratios with PDR and XDR models, we find that PDRs can reproduce observations only in presence of extremely high gas densities (n > 105 cm−3). In the XDR case, instead, the models suggest moderate densities (n ≈ 102−4 cm−3). We conclude that a mix of the two mechanisms (PDR for the mid-J, XDR or possibly shocks for the high-J) is necessary to explain the observed CO excitation in active galaxies.
Local Feature Matching with Transformers for low-end devices

LoFTR arXiv:2104.00680 is an efficient deep learning method for finding appropriate local feature matches on image pairs. This paper reports on the optimization of this method to work on devices with low computational performance and limited memory. The original LoFTR approach is based on a ResNet arXiv:1512.03385 head and two modules based on Linear Transformer arXiv:2006.04768 architecture. In the presented work, only the coarse-matching block was left, the number of parameters was significantly reduced, and the network was trained using a knowledge distillation technique. The comparison showed that this approach allows to obtain an appropriate feature detection accuracy for the student model compared to the teacher model in the coarse matching block, despite the significant reduction of model size. Also, the paper shows additional steps required to make model compatible with NVIDIA TensorRT runtime, and shows an approach to optimize training method for low-end GPUs.
On the Benefits of Selectivity in Pseudo-Labeling for Unsupervised Multi-Source-Free Domain Adaptation

Due to privacy, storage, and other constraints, there is a growing need for unsupervised domain adaptation techniques in machine learning that do not require access to the data used to train a collection of source models. Existing methods for such multi-source-free domain adaptation typically train a target model using supervised techniques in conjunction with pseudo-labels for the target data, which are produced by the available source models. However, we show that assigning pseudo-labels to only a subset of the target data leads to improved performance. In particular, we develop an information-theoretic bound on the generalization error of the resulting target model that demonstrates an inherent bias-variance trade-off controlled by the sub-set choice. Guided by this analysis, we develop a method that partitions the target data into pseudo-labeled and unlabeled subsets to balance the trade-off. In addition to exploiting the pseudo-labeled subset, our algorithm further leverages the information in the unlabeled subset via a traditional unsupervised domain adaptation feature alignment procedure. Experiments on multiple benchmark datasets demonstrate the superior performance of the proposed method.