Miracle-dz/UniFAR-FTUs huggingface dataset:
Peculiarities of the chemical abundance distribution in galaxies NGC 3963 and NGC 7292

Spectroscopic observations of 32 H ii regions in the spiral galaxy NGC 3963 and the barred irregular galaxy NGC 7292 were carried out with the 2.5-m telescope of the Caucasus Mountain Observatory of the Sternberg Astronomical Institute using the Transient Double-beam Spectrograph with a dispersion of ≈1Å pixel−1 and a spectral resolution of ≈3Å. These observations were used to estimate the oxygen and nitrogen abundances and the electron temperatures in H ii regions through modern strong-line methods. In general, the galaxies have oxygen and nitrogen abundances typical of stellar systems with similar luminosities, sizes, and morphology. However, we have found some peculiarities in chemical abundance distributions in both galaxies. The distorted outer segment of the southern arm of NGC 3963 shows an excess oxygen and nitrogen abundances. Chemical elements abundances in NGC 7292 are constant and do not depend on the galactocentric distance. These peculiarities can be explained in terms of external gas accretion in the case of NGC 3963 and major merging for NGC 7292.
Adaptive Test-Time Augmentation for Low-Power CPU

Convolutional Neural Networks (ConvNets) are trained offline using the few available data and may therefore suffer from substantial accuracy loss when ported on the field, where unseen input patterns received under unpredictable external conditions can mislead the model. Test-Time Augmentation (TTA) techniques aim to alleviate such common side effect at inference-time, first running multiple feed-forward passes on a set of altered versions of the same input sample, and then computing the main outcome through a consensus of the aggregated predictions. Unfortunately, the implementation of TTA on embedded CPUs introduces latency penalties that limit its adoption on edge applications. To tackle this issue, we propose AdapTTA, an adaptive implementation of TTA that controls the number of feed-forward passes dynamically, depending on the complexity of the input. Experimental results on state-of-the-art ConvNets for image classification deployed on a commercial ARM Cortex-A CPU demonstrate AdapTTA reaches remarkable latency savings, from 1.49X to 2.21X, and hence a higher frame rate compared to static TTA, still preserving the same accuracy gain.
Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models

Mixed linear regression (MLR) model is among the most exemplary statistical tools for modeling non-linear distributions using a mixture of linear models. When the additive noise in MLR model is Gaussian, Expectation-Maximization (EM) algorithm is a widely-used algorithm for maximum likelihood estimation of MLR parameters. However, when noise is non-Gaussian, the steps of EM algorithm may not have closed-form update rules, which makes EM algorithm impractical. In this work, we study the maximum likelihood estimation of the parameters of MLR model when the additive noise has non-Gaussian distribution. In particular, we consider the case that noise has Laplacian distribution and we first show that unlike the the Gaussian case, the resulting sub-problems of EM algorithm in this case does not have closed-form update rule, thus preventing us from using EM in this case. To overcome this issue, we propose a new algorithm based on combining the alternating direction method of multipliers (ADMM) with EM algorithm idea. Our numerical experiments show that our method outperforms the EM algorithm in statistical accuracy and computational time in non-Gaussian noise case.
Perspective on Data Science

The field of data science currently enjoys a broad definition that includes a wide array of activities which borrow from many other established fields of study. Having such a vague characterization of a field in the early stages might be natural, but over time maintaining such a broad definition becomes unwieldy and impedes progress. In particular, the teaching of data science is hampered by the seeming need to cover many different points of interest. Data scientists must ultimately identify the core of the field by determining what makes the field unique and what it means to develop new knowledge in data science. In this review we attempt to distill some core ideas from data science by focusing on the iterative process of data analysis and develop some generalizations from past experience. Generalizations of this nature could form the basis of a theory of data science and would serve to unify and scale the teaching of data science to large audiences. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Semi-Supervised Variational Reasoning for Medical Dialogue Generation

Medical dialogue generation aims to provide automatic and accurate responses to assist physicians to obtain diagnosis and treatment suggestions in an efficient manner. In medical dialogues, two key characteristics are relevant for response generation: patient states (such as symptoms, medication) and physician actions (such as diagnosis, treatments). In medical scenarios large-scale human annotations are usually not available, due to the high costs and privacy requirements. Hence, current approaches to medical dialogue generation typically do not explicitly account for patient states and physician actions, and focus on implicit representation instead. We propose an end-to-end variational reasoning approach to medical dialogue generation. To be able to deal with a limited amount of labeled data, we introduce both patient state and physician action as latent variables with categorical priors for explicit patient state tracking and physician policy learning, respectively. We propose a variational Bayesian generative approach to approximate posterior distributions over patient states and physician actions. We use an efficient stochastic gradient variational Bayes estimator to optimize the derived evidence lower bound, where a 2-stage collapsed inference method is proposed to reduce the bias during model training. A physician policy network composed of an action-classifier and two reasoning detectors is proposed for augmented reasoning ability. We conduct experiments on three datasets collected from medical platforms. Our experimental results show that the proposed method outperforms state-of-the-art baselines in terms of objective and subjective evaluation metrics. Our experiments also indicate that our proposed semi-supervised reasoning method achieves a comparable performance as state-of-the-art fully supervised learning baselines for physician policy learning.
Universal Platform for Scalable Semiconductor‐Superconductor Nanowire Networks

Semiconductor‐superconductor hybrids are commonly used in research on topological quantum computation. Traditionally, top‐down approaches involving dry or wet etching are used to define the device geometry. These often aggressive processes risk causing damage to material surfaces, giving rise to scattering sites particularly problematic for quantum applications. Here, a method that maintains the flexibility and scalability of selective area grown nanowire networks while omitting the necessity of etching to create hybrid segments is proposed. Instead, it takes advantage of directional growth methods and uses bottom‐up grown indium phosphide (InP) structures as shadowing objects to obtain selective metal deposition. The ability to lithographically define the position and area of these objects and to grow a predefined height ensures precise control of the shadowed region. The approach by growing indium antimonide nanowire networks with well‐defined aluminium and lead (Pb) islands is demonstrated. Cross‐section cuts of the nanowires reveal a sharp, oxide‐free interface between semiconductor and superconductor. By growing InP structures on both sides of in‐plane nanowires, a combination of platinum and Pb can independently be shadow deposited, enabling a scalable and reproducible in situ device fabrication. The semiconductor‐superconductor nanostructures resulting from this approach are at the forefront of material development for Majorana based experiments.
SizeNet: Object Recognition via Object Real Size-based Convolutional Networks

Inspired by the conclusion that humans choose the visual cortex regions corresponding to the real size of an object to analyze its features when identifying objects in the real world, this paper presents a framework, SizeNet, which is based on both the real sizes and features of objects to solve object recognition problems. SizeNet was used for object recognition experiments on the homemade Rsize dataset, and was compared with the state-of-the-art methods AlexNet, VGG-16, Inception V3, Resnet-18, and DenseNet-121. The results showed that SizeNet provides much higher accuracy rates for object recognition than the other algorithms. SizeNet can solve the two problems of correctly recognizing objects with highly similar features but real sizes that are obviously different from each other, and correctly distinguishing a target object from interference objects whose real sizes are obviously different from the target object. This is because SizeNet recognizes objects based not only on their features, but also on their real size. The real size of an object can help exclude the interference object's categories whose real size ranges do not match the real size of the object, which greatly reduces the object's categories' number in the label set used for the downstream object recognition based on object features. SizeNet is of great significance for studying the interpretable computer vision. Our code and dataset will thus be made public.
An Open-Source Tool for Classification Models in Resource-Constrained Hardware

Sensor applications often face three main restrictions: power consumption, cost, and lack of infrastructure. Most of the challenges imposed by these limitations can be addressed by embedding Machine Learning (ML) classifiers in the sensor hardware, creating smart sensors able to interpret the low-level data stream. However, for this approach to be cost-effective, we need highly efficient classifiers suitable to execute in resource-constrained hardware, such as low-power microcontrollers. In this paper, we present an open-source tool named EmbML – Embedded Machine Learning that implements a pipeline to develop classifiers for resource-constrained hardware. We describe EmbML implementation details and comprehensively analyze its classifiers considering accuracy, classification time, and memory usage. Moreover, we compare the performance of EmbML classifiers with classifiers produced by related tools to demonstrate that our tool provides a diverse set of classification algorithms that are both compact and accurate. Finally, we validate EmbML classifiers to recognize disease vector mosquitoes in a smart sensor and trap application.
3D Restoration of sedimentary terrains: The GeoChron Approach

Three-dimensional restoration of complex structural models has become a recognized validation method. Bringing a sedimentary structural model back in time to various deposition stages may also help understand the geological history of a study area and follow the evolution of potential hydrocarbon source rocks, reservoirs and closures. Most current restoration methods rely on finite-element codes which require a mesh that conforms to both horizons and faults, a difficult object to generate in complex structural settings. Some innovative approaches use implicit horizon representations to circumvent meshing requirements. In all cases, finite-element restoration codes depend on elasticity theory which relies on mechanical parameters to characterize rock behavior during the physical unfolding process. In this paper, we present a geometric restoration method based on the mathematical theory provided by the GeoChron framework. No assumption is made on the extent of deformation, nor on the nature of terrains being restored. Equations derived from the theory developed for the GeoChron model ensure model consistency at each restored stage. As the only essential input is a GeoChron model, this restoration technique does not require any specialist knowledge and can be included in any existing structural model-building workflow as a standard validation tool. A model can quickly be restored to any desired stage without providing input mechanical parameters for each layer nor defining boundary conditions, enabling geologists to iterate on the structural model and refine their interpretations until they are satisfied with both input and restored models.
Guardian: Symbolic Validation of Orderliness in SGX Enclaves

Modern processors can offer hardware primitives that allow a process to run in isolation. These primitives implement a trusted execution environment (TEE) in which a program can run such that the integrity and confidentiality of its execution are guaranteed. Intel's Software Guard eXtensions (SGX) is an example of such primitives and its isolated processes are called enclaves. These guarantees, however, can be easily thwarted if the enclave has not been properly designed. Its interface with the untrusted software stack is a perhaps the largest attack surface that adversaries can exploit; unintended interactions with untrusted code can expose the enclave to memory corruption attacks, for instance. In this paper, we propose a notion of an orderly enclave which splits its behaviour into the following execution phases: entry, secure, ocall, and exit. Each of them imposes a set of restrictions that enforce a particular policy of access to untrusted memory and, in some cases, sanitisation conditions. A violation of these policies and conditions might indicate an undesired interaction with untrusted data/code or a lack of sanitisation, both of which can be harnessed to perpetrate attacks against the enclave. We also introduce Guardian: an open-source tool that uses symbolic execution to carry out the validation of an enclave against our notion of an orderly enclave; in this process, it also looks for some other typical attack primitives. We discuss how our approach can prevent and flag enclave vulnerabilities that have been identified in the literature. Moreover, we have evaluated how our approach fares in the analysis of some enclave samples. In this process, Guardian identified some security issues previously undetected in some of these samples that were acknowledged and fixed by the corresponding maintainers.
Tracelet Hopf algebras and decomposition spaces

Tracelets are the intrinsic carriers of causal information in categorical rewriting systems. In this work, we assemble tracelets into a symmetric monoidal decomposition space, inducing a cocommutative Hopf algebra of tracelets. This Hopf algebra captures important combinatorial and algebraic aspects of rewriting theory, and is motivated by applications of its representation theory to stochastic rewriting systems such as chemical reaction networks.
Outage Common Randomness Capacity Characterization of Multiple-Antenna Slow Fading Channels

We investigate the problem of common randomness (CR) generation from discrete correlated sources aided by one-way communication over single-user multiple-input multiple-output (MIMO) slow fading channels with additive white Gaussian noise (AWGN), arbitrary state distribution and with channel state information available at the receiver side (CSIR). We completely solve the problem by first characterizing the channel outage capacity of MIMO slow fading channels for arbitrary state distribution. For this purpose, we also provide an achievable rate for a specific compound MIMO Gaussian channel. Second, we define the outage CR capacity of the MIMO slow fading channel and establish a single-letter characterization of it using our result on its outage transmission capacity.
Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay

Sequential information contains short- to long-range dependencies; however, learning long-timescale information has been a challenge for recurrent neural networks. Despite improvements in long short-term memory networks (LSTMs), the forgetting mechanism results in the exponential decay of information, limiting their capacity to capture long-timescale information. Here, we propose a power law forget gate, which instead learns to forget information along a slower power law decay function. Specifically, the new gate learns to control the power law decay factor, p, allowing the network to adjust the information decay rate according to task demands. Our experiments show that an LSTM with power law forget gates (pLSTM) can effectively capture long-range dependencies beyond hundreds of elements on image classification, language modeling, and categorization tasks, improving performance over the vanilla LSTM. We also inspected the revised forget gate by varying the initialization of p, setting p to a fixed value, and ablating cells in the pLSTM network. The results show that the information decay can be controlled by the learnable decay factor p, which allows pLSTM to achieve its superior performance. Altogether, we found that LSTM with the proposed forget gate can learn long-term dependencies, outperforming other recurrent networks in multiple domains; such gating mechanism can be integrated into other architectures for improving the learning of long timescale information in recurrent neural networks.
Elliptic flowers: simply connected billiard tables where chaotic (non-chaotic) flows move around chaotic (non-chaotic) cores

We introduce a class of billiards with chaotic unidirectional flows (or non-chaotic unidirectional flows with ‘vortices’) which go around a chaotic or non-chaotic ‘core’, where orbits can change their orientation. Moreover, the corresponding billiard tables are simply connected in difference with many attempts to build billiards with interesting and/or exotic dynamics by putting inside billiard tables various ‘scatterers’ with funny shapes. Therefore the billiards in this new class are amenable to experimental studies in physics labs as well as to the rigorous mathematical ones, which may shed a new light on understanding of classical and quantum dynamics of Hamiltonian systems.
Weighted directed clustering: interpretations and requirements for heterogeneous, inferred, and measured networks

Weights and directionality of the edges carry a large part of the information we can extract from a complex network. However, many measures were formulated initially for undirected binary networks. The necessity to incorporate information about the weights led to the conception of the multiple extensions, particularly for definitions of the local clustering coefficient discussed here. We uncover that not all of these extensions are fully-weighted; some depend on the degree and thus change a lot when an infinitely small weight edge is exchanged for the absence of an edge, a feature that is not always desirable. We call these methods “hybrid” and argue that, in many situations, one should prefer fully-weighted definitions. After listing the necessary requirements for a method to analyze many various weighted networks properly, we propose a fully-weighted continuous clustering coefficient that satisfies all the previously proposed criteria while also being continuous with respect to vanishing weights. We demonstrate that the behavior and meaning of the Zhang–Horvath clustering and our new continuous definition provide complementary results and significantly outperform other definitions in multiple relevant conditions.
Conversational AI Systems for Social Good: Opportunities and Challenges

Conversational artificial intelligence (ConvAI) systems have attracted much academic and commercial attention recently, making significant progress on both fronts. However, little existing work discusses how these systems can be developed and deployed for social good in real-world applications, with comprehensive case studies and analyses of pros and cons. In this paper, we briefly review the progress the community has made towards better ConvAI systems and reflect on how existing technologies can help advance social good initiatives from various angles that are unique for ConvAI, or not yet become common knowledge in the community. We further discuss about the challenges ahead for ConvAI systems to better help us achieve these goals and highlight the risks involved in their development and deployment in the real world.
Thematic fit bits: Annotation quality and quantity for event participant representation

Modeling thematic ﬁt (a verb–argument compositional semantics task) currently requires a very large burden of data. We take a high-performing neural approach to modeling verb–argument ﬁt, previously trained on a linguistically machine-annotated large corpus, and replace corpus layers with output from higher-quality taggers. Contrary to popular beliefs that, in the deep learning era, more data is as effective as higher quality annotation, we discover that higher annotation quality dramatically reduces our data requirement while demonstrating better supervised predicate-argument classiﬁcation. But in applying the model to a psycholinguistic task outside the training objective, we saw only small gains in one of two thematic ﬁt estimation tasks, and none in the other. We replicate previous studies while modifying certain role representation details, and set a new state-of-the-art in event modeling, using a fraction of the data.
Joint Community Detection and Rotational Synchronization via Semidefinite Programming

In the presence of heterogeneous data, where randomly rotated objects fall into multiple underlying categories, it is challenging to simultaneously classify them into clusters and synchronize them based on pairwise relations. This gives rise to the joint problem of community detection and synchronization. We propose a series of semidefinite relaxations, and prove their exact recovery when extending the celebrated stochastic block model to this new setting where both rotations and cluster identities are to be determined. Numerical experiments demonstrate the efficacy of our proposed algorithms and confirm our theoretical result which indicates a sharp phase transition for exact recovery.
Conditional Entropy Production and Quantum Fluctuation Theorem of Dissipative Information: Theory and Experiments

We study quantum conditional entropy production, which quantiﬁes the irreversibility of system-environment evolution from the perspective of a third system, called the reference. The reference is initially correlated with the system. We show that the quantum unconditional entropy production with respect to the system is less than the conditional entropy production with respect to the reference, where the latter includes a reference-induced dissipative information. The dissipative information pinpoints the distributive correlation established between the environment and the reference, even though they do not interact directly. When reaching the thermal equilibrium, the system-environment evolution has a zero unconditional entropy production. However, one can still have a nonzero conditional entropy production with respect to the reference, which characterizes the informational nonequilibrium of the system-environment evolution in the view point of the reference. The additional contribution to the conditional entropy production, the dissipative information, characterizes a minimal thermodynamic cost that the system pays for maintaining the correlation with the reference. Positive dissipative information also characterizes potential work waste. We prove that both types of entropy production and the dissipative information follow quantum ﬂuctuation theorems when a two-point measurement is applied. We verify the quantum ﬂuctuation theorem for the dissipative information experimentally on IBM quantum computers. We also present examples based on the qubit collisional model and demonstrate universal nonzero dissipative information in the qubit Maxwell’s demon protocol.