Miracle-dz/UniFAR-FTUs huggingface dataset:
Jaccard Filtration and Stable Paths in the Mapper

The contributions of this paper are two-fold. We define a new filtration called the cover filtration built from a single cover based on a generalized Jaccard distance. We provide stability results for the cover filtration and show how the construction is equivalent to the Cech filtration under certain settings. We then develop a language and theory for stable paths within this filtration, inspired by ideas of persistent homology. We demonstrate how the filtration and paths can be applied to a variety of applications in which defining a metric is not obvious but a cover is readily available. We demonstrate the usefulness of this construction by employing it in the context of recommendation systems and explainable machine learning. We demonstrate a new perspective for modeling recommendation system data sets that does not require manufacturing a bespoke metric. This extends work on graph-based recommendation systems, allowing a topological perspective. For an explicit example, we look at a movies data set and we find the stable paths identified in our framework represent a sequence of movies constituting a gentle transition and ordering from one genre to another. For explainable machine learning, we apply the Mapper for model induction, providing explanations in the form of paths between subpopulations or observations. Our framework provides an alternative way of building a filtration from a single mapper that is then used to explore stable paths. As a direct illustration, we build a mapper from a supervised machine learning model trained on the FashionMNIST data set. We show that the stable paths in the cover filtration provide improved explanations of relationships between subpopulations of images.
Learning the Sampling Pattern for MRI

The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are “incomplete”. This is particularly interesting in magnetic resonance imaging (MRI), where long acquisition times can limit its use. In this work, we consider the problem of learning a sparse sampling pattern that can be used to optimally balance acquisition time versus quality of the reconstructed image. We use a supervised learning approach, making the assumption that our training data is representative enough of new data acquisitions. We demonstrate that this is indeed the case, even if the training data consists of just 7 training pairs of measurements and ground-truth images; with a training set of brain images of size 192 by 192, for instance, one of the learned patterns samples only 35% of k-space, however results in reconstructions with mean SSIM 0.914 on a test set of similar images. The proposed framework is general enough to learn arbitrary sampling patterns, including common patterns such as Cartesian, spiral and radial sampling.
Multi-grained Named Entity Recognition

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.
ID3 Learns Juntas for Smoothed Product Distributions

In recent years, there are many attempts to understand popular heuristics. An example of such a heuristic algorithm is the ID3 algorithm for learning decision trees. This algorithm is commonly used in practice, but there are very few theoretical works studying its behavior. In this paper, we analyze the ID3 algorithm, when the target function is a $k$-Junta, a function that depends on $k$ out of $n$ variables of the input. We prove that when $k = \log n$, the ID3 algorithm learns in polynomial time $k$-Juntas, in the smoothed analysis model of Kalai & Teng. That is, we show a learnability result when the observed distribution is a "noisy" variant of the original distribution.
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies

Policy gradient (PG) methods are a widely used reinforcement learning methodology in many applications such as video games, autonomous driving, and robotics. In spite of its empirical success, a rigorous understanding of the global convergence of PG methods is lacking in the literature. In this work, we close the gap by viewing PG methods from a nonconvex optimization perspective. In particular, we propose a new variant of PG methods for infinite-horizon problems that uses a random rollout horizon for the Monte-Carlo estimation of the policy gradient. This method then yields an unbiased estimate of the policy gradient with bounded variance, which enables the tools from nonconvex optimization to be applied to establish global convergence. Employing this perspective, we first recover the convergence results with rates to the stationary-point policies in the literature. More interestingly, motivated by advances in nonconvex optimization, we modify the proposed PG method by introducing periodically enlarged stepsizes. The modified algorithm is shown to escape saddle points under mild assumptions on the reward and the policy parameterization. Under a further strict saddle points assumption, this result establishes convergence to essentially locally-optimal policies of the underlying problem, and thus bridges the gap in existing literature on the convergence of PG methods. Results from experiments on the inverted pendulum are then provided to corroborate our theory, namely, by slightly reshaping the reward function to satisfy our assumption, unfavorable saddle points can be avoided and better limit points can be attained. Intriguingly, this empirical finding justifies the benefit of reward-reshaping from a nonconvex optimization perspective.
Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images

Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of salient object detection for nature scene image to the optical RSI. In this paper, we propose an end-to-end deep network called LV-Net based on the shape of network architecture, which detects salient objects from optical RSIs in a purely data-driven fashion. The proposed LV-Net consists of two key modules, i.e., a two-stream pyramid module (L-shaped module) and an encoder–decoder module with nested connections (V-shaped module). Specifically, the L-shaped module extracts a set of complementary information hierarchically by using a two-stream pyramid structure, which is beneficial to perceiving the diverse scales and local details of salient objects. The V-shaped module gradually integrates encoder detail features with decoder semantic features through nested connections, which aims at suppressing the cluttered backgrounds and highlighting the salient objects. In addition, we construct the first publicly available optical RSI data set for salient object detection, including 800 images with varying spatial resolutions, diverse saliency types, and pixel-wise ground truth. Experiments on this benchmark data set demonstrate that the proposed method outperforms the state-of-the-art salient object detection methods both qualitatively and quantitatively.
Resolving hydrogen atoms at metal-metal hydride interfaces

Unprecedented imaging of hydrogen atoms in a challenging material system paves way for novel research on light-element alloys. Hydrogen as a fuel can be stored safely with high volumetric density in metals. It can, however, also be detrimental to metals, causing embrittlement. Understanding fundamental behavior of hydrogen at the atomic scale is key to improve the properties of metal-metal hydride systems. However, currently, there is no robust technique capable of visualizing hydrogen atoms. Here, we demonstrate that hydrogen atoms can be imaged unprecedentedly with integrated differential phase contrast, a recently developed technique performed in a scanning transmission electron microscope. Images of the titanium-titanium monohydride interface reveal stability of the hydride phase, originating from the interplay between compressive stress and interfacial coherence. We also uncovered, 30 years after three models were proposed, which one describes the position of hydrogen atoms with respect to the interface. Our work enables previously unidentified research on hydrides and is extendable to all materials containing light and heavy elements, including oxides, nitrides, carbides, and borides.
Improving Zero-shot Translation with Language-Independent Constraints

An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving this ability kills two birds with one stone by providing an alternative to pivot translation which also allows us to better understand how the model captures information between languages. In this work, we carried out an investigation on this capability of the multilingual NMT models. First, we intentionally create an encoder architecture which is independent with respect to the source language. Such experiments shed light on the ability of NMT encoders to learn multilingual representations, in general. Based on such proof of concept, we were able to design regularization methods into the standard Transformer model, so that the whole architecture becomes more robust in zero-shot conditions. We investigated the behaviour of such models on the standard IWSLT 2017 multilingual dataset. We achieved an average improvement of 2.23 BLEU points across 12 language pairs compared to the zero-shot performance of a state-of-the-art multilingual system. Additionally, we carry out further experiments in which the effect is confirmed even for language pairs with multiple intermediate pivots.
Anomalous conductance scaling in strained Weyl semimetals

Magnetotransport provides key experimental signatures in Weyl semimetals. The longitudinal magnetoresistance is linked to the chiral anomaly and the transversal magnetoresistance to the dominant charge relaxation mechanism. Axial magnetic fields that act with opposite sign on opposite chiralities facilitate new transport experiments that probe the low-energy Weyl nodes. As recently realized, these axial fields can be achieved by straining samples or adding inhomogeneities to them. Here, we identify a robust signature of axial magnetic fields: an anomalous scaling of the conductance in the diffusive ultraquantum regime. In particular, we demonstrate that the longitudinal conductivity in the ultraquantum regime of a disordered Weyl semimetal subjected to an axial magnetic field increases with both the field strength and sample width due to a spatial separation of charge carriers. We contrast axial magnetic with real magnetic fields to clearly distinguish the different behavior of the conductance. Our results rely on numerical tight-binding simulations and are supported by analytical arguments. We argue that the spatial separation of charge carriers can be used for directed currents in microstructured electronic devices.
The large gamma-ray flare of the FSRQ PKS 0346-27

In this paper, we characterize the first $\gamma$-ray flaring episode of the FSRQ PKS 0346-27 (z=0.991), as revealed by Fermi-LAT monitoring data, and the concurrent multi-wavelength variability observed from radio through X-rays. The quasi-simultaneous multi-wavelength coverage allowed us to construct time-resolved spectral energy distributions (SEDs). PKS 0346-27 entered an elevated $\gamma$-ray activity state starting from the beginning of 2018. The high-state continued throughout the year, displaying the highest fluxes in May 2018. We find evidence of short-time scale variability down to $\sim$1.5 hours, which constrains the $\gamma$-ray emission region to be compact. The extended flaring period was characterized by a persistently harder spectrum with respect to the quiescent state, indicating changes in the broadband spectral properties of the source. This was confirmed by the multi-wavelength observations, which show a shift in the position of the two SED peaks by $\sim$2 orders of magnitude in energy and peak flux value. As a result, during the high state the non-thermal jet emission completely outshines the thermal contribution from the dust torus and accretion disk. The broadband SED of PKS 0346-27 transitions from a typical Low-Synchrotron-Peaked (LSP) to the Intermediate-Synchrotron-Peaked (ISP) class, a behavior previously observed in other flaring $\gamma$-ray sources. Our one-zone leptonic emission model of the high-state SEDs constrains the $\gamma$-ray emission region to have a lower magnetic field, larger radius, and higher maximum electron Lorentz factors with respect to the quiescent SED. Finally, we note that the bright and hard $\gamma$-ray spectrum observed during the peak of flaring activity in May 2018 implies that PKS 0346-27 could be a promising target for future ground-based Cherenkov observatories such as the CTA.
Back to Simplicity: How to Train Accurate BNNs from Scratch?

Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e.g., ImageNet. Previous work mainly focused on reducing quantization errors of weights and activations, whereby a series of approximation methods and sophisticated training tricks have been proposed. In this work, we make several observations that challenge conventional wisdom. We revisit some commonly used techniques, such as scaling factors and custom gradients, and show that these methods are not crucial in training well-performing BNNs. On the contrary, we suggest several design principles for BNNs based on the insights learned and demonstrate that highly accurate BNNs can be trained from scratch with a simple training strategy. We propose a new BNN architecture BinaryDenseNet, which significantly surpasses all existing 1-bit CNNs on ImageNet without tricks. In our experiments, BinaryDenseNet achieves 18.6% and 7.6% relative improvement over the well-known XNOR-Network and the current state-of-the-art Bi-Real Net in terms of top-1 accuracy on ImageNet, respectively.
Extinction and dust/gas ratio in the H i ridge region of the LMC based on the IRSF/SIRIUS near-infrared survey

We present a dust extinction AV map of the Large Magellanic Cloud (LMC) in the H i ridge region using the InfraRed Survey Facility (IRSF) near-infrared (IR) data, and compare the AV map with the total hydrogen column density N(H) maps derived from the CO and H i observations. In the LMC H i ridge region, the two-velocity H i components (plus an intermediate velocity component) are identified, and the young massive star cluster is possibly formed by collision between them. In addition, one of the components is suggested to be an inflow gas from the Small Magellanic Cloud (SMC) which is expected to have even lower metallicity gas (Fukui et al. 2017, PASJ, 69, L5). To evaluate dust/gas ratios in the H i ridge region in detail, we derive the AV map from the near-IR color excess of the IRSF data updated with the latest calibration, and fit the resultant AV map with a combination of the N(H) maps of the different velocity components to decompose it successfully into the three components. As a result, we find difference by a factor of 2 in AV/N(H) between the components. In additon, the CO-to-H2 conversion factor also indicates difference between the components, implying a difference in the metallicity. Our results are likely to support the scenario that the gas in the LMC H i ridge region is contaminated with an inflow gas from the SMC with a geometry consistent with the on-going collision between the two-velocity components.
The Age-Period-Cohort-Interaction Model for Describing and Investigating Inter-cohort Deviations and Intra-cohort Life-course Dynamics

Social scientists have frequently sought to understand the distinct effects of age, period, and cohort, but disaggregation of the three dimensions is difficult because cohort = period − age. We argue that this technical difficulty reflects a disconnection between how the cohort effect is conceptualized and how it is modeled in the traditional age-period-cohort framework. We propose a new method, called the age-period-cohort-interaction (APC-I) model, that is qualitatively different from previous methods in that it represents Ryder’s theoretical account about the conditions under which cohort differentiation may arise. This APC-I model does not require problematic statistical assumptions and the interpretation is straightforward. It quantifies inter-cohort deviations from the age and period main effects and also permits hypothesis testing about intra-cohort life-course dynamics. We demonstrate how this new model can be used to examine age, period, and cohort patterns in women’s labor force participation.
Active Linear Regression

We consider the problem of active linear regression where a decision maker has to choose between several covariates to sample in order to obtain the best estimate $\hat{\beta}$ of the parameter $\beta^{\star}$ of the linear model, in the sense of minimizing $\mathbb{E} \lVert\hat{\beta}-\beta^{\star}\rVert^2$. Using bandit and convex optimization techniques we propose an algorithm to define the sampling strategy of the decision maker and we compare it with other algorithms. We provide theoretical guarantees of our algorithm in different settings, including a $\mathcal{O}(T^{-2})$ regret bound in the case where the covariates form a basis of the feature space, generalizing and improving existing results. Numerical experiments validate our theoretical findings.
Vortex lattice formation in dipolar Bose-Einstein condensates via rotation of the polarization

The behaviour of a harmonically trapped dipolar Bose-Einstein condensate with its dipole moments rotating at angular frequencies lower than the transverse harmonic trapping frequency is explored in the co-rotating frame. We obtain semi-analytical solutions for the stationary states in the Thomas-Fermi limit of the corresponding dipolar Gross-Pitaevskii equation and utilise linear stability analysis to elucidate a phase diagram for the dynamical stability of these stationary solutions with respect to collective modes. These results are verified via direct numerical simulations of the dipolar Gross-Pitaevskii equation, which demonstrate that dynamical instabilities of the co-rotating stationary solutions lead to the seeding of vortices that eventually relax into a triangular lattice configuration. Our results illustrate that rotation of the dipole polarization represents a new route to vortex formation in dipolar Bose-Einstein condensates.
Light Field Saliency Detection With Deep Convolutional Networks

Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light. The detection of salient regions in a light field image benefits from the additional modeling of angular patterns. For RGB imaging, methods using CNNs have achieved excellent results on a range of tasks, including saliency detection. However, it is not trivial to use CNN-based methods for saliency detection on light field images because these methods are not specifically designed for processing light field inputs. In addition, current light field datasets are not sufficiently large to train CNNs. To overcome these issues, we present a new Lytro Illum dataset, which contains 640 light fields and their corresponding ground-truth saliency maps. Compared to current publicly available light field saliency datasets [1], [2], our new dataset is larger, of higher quality, contains more variation and more types of light field inputs. This makes our dataset suitable for training deeper networks and benchmarking. Furthermore, we propose a novel end-to-end CNN-based framework for light field saliency detection. Specifically, we propose three novel MAC (Model Angular Changes) blocks to process light field micro-lens images. We systematically study the impact of different architecture variants and compare light field saliency with regular 2D saliency. Our extensive comparisons indicate that our novel network significantly outperforms state-of-the-art methods on the proposed dataset and has desired generalization abilities on other existing datasets.
Integrated multiple objective controller design for one-way coupled systems

Many control system designs are challenging for two reasons in particular. Firstly, there are multiple performance requirements to be satisfied; secondly, the systems are made up of subsystems that may interact. Conventional engineering process follows a bottom-up approach, where the subsystem controllers are designed independently, and then assembled. Unaccounted for interactions are then handled by ad-hoc troubleshooting methods, which may compromise the original design specifications. In this paper, we present a new method for multiple-objective controller design of systems with undesirable one-way coupling. By choosing a block triangular controller structure to match that of the plant, the subsystems can be designed separately. Having designed these, an additional controller is designed to limit the effects of coupling between the subsystems.
Realization of robust controllers in evolutionary robotics: a dynamically-rearranging neural network approach

The evolutionary robotics approach has been attracting a lot of attention in the field of robotics and artificial life. In this approach, neural networks are widely used to construct controllers for autonomous mobile agents, since they intrinsically have generalization, noise-tolerant abilities and so on. However, there are still open questions: (1) the gap between simulated and real environments, (2) the evolutionary and learning phase are completely separated, and (3) the conflict between stability and evolvability/adaptability. In this paper, we try to overcome these problems by incorporating the concept of dynamic rearrangement function of biological neural networks with the use of neuromodulators. Simulation results show that the proposed approach is highly promising.
Encyclopedia of food science and technology

Citing the history and growth of the discipline, this encyclopaedia reflects recent changes in scientific and technical data. The principles and applications of food science and technology are discussed and current problems stressed include those associated with food additives and food spoilage. It presents a thorough alphabetical listing of articles dealing with the properties, analysis and processing of foods, including material on grains, beans, nuts, seeds, fruits and vegetables, bakery, dairy, meat, poultry and fish products, and alcoholic beverages.