Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation of the extreme halophiles from rock salt

Extreme halophiles are organisms that require 1.5 M-5.5 M NaCl concentrations (5.5 M is the saturation limit of NaCl) for their optimum growth. Over 100 species have been described as extreme halophiles, and were isolated from solar saltern, salt deposit, rock salt, and sometimes from non-salty environment such as forest soil. During our recent attempt to isolate extreme halophiles, 13 strains including candidate novel species have been isolated from rock salt of various localities. Based on the phylogenetic status, 12 strains were strongly suggested to be 3 kinds of novel species (one of archaea and two of bacteria). Interestingly, Himarayan rock salt gave three different species including two novel species.
The Effect of Extracts and Essential Oil from Invasive Solidago spp. and Fallopia japonica on Crop-Borne Fungi and Wheat Germination

SUMMARY Research background Many plant extracts and essential oils show antibacterial and antifungal activities, with potential to replace the use of synthetic fungicides. We used invasive alien plants goldenrod (Solidago spp.) and Japanese knotweed (Fallopia japonica) as source materials to determine their antifungal activities against seed-associated fungi from wheat grain (Alternaria alternata, Alternaria infectoria, Aspergillus flavus, Epicoccum nigrum and Fusarium poae). Experimental approach Aqueous and organic extracts (ethanol, methanol and acetone) were prepared from leaves and flowers of S. canadensis, S. gigantea and S. virgaurea, and leaves and rhizomes of F. japonica. Additionally, essential oils were distilled from Solidago flowers and leaves. The extracts and essential oils were tested as inhibitors of fungal growth in vitro. Solidago essential oils were tested also as antifungal agents for protection of wheat grain by determining its fungal infection and germination rate. Results and conclusions The extracts showed a wide spectrum of low to moderate antifungal activities, with those of Solidago spp. generally more effective than those of F. japonica, and organic extracts more effective than aqueous extracts. The essential oils from leaves and flowers had similar antifungal activity and whole shoots can be collected for their production. This study presents the systematic study of the composition of essential oils from flowers and leaves of three widely distributed Solidago spp. in Slovenia, with the major constituents of terpenes and terpenoids α-pinene, germacrene D and bornyl acetate. Novelty and scientific contribution The study presents the first use of Solidago spp. and Fallopia japonica extracts and essential oils against fungal strains isolated from wheat grain.
Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare

Federated learning (FL) has enabled training models collaboratively from multiple data owning parties without sharing their data. Given the privacy regulations of patient's healthcare data, learning-based systems in healthcare can greatly benefit from privacy-preserving FL approaches. However, typical model aggregation methods in FL are sensitive to local model updates, which may lead to failure in learning a robust and accurate global model. In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data. Furthermore, we show that such methods can detect and discard faulty or malicious local clients during training. We run two sets of experiments using two real-world healthcare datasets for training medical diagnosis classification tasks. Each dataset is used to simulate the performance of three different robust FL aggregation strategies when facing different poisoning attacks. The results show that privacy preserving methods can be successfully applied alongside Byzantine-robust aggregation techniques. We observed in particular how using differential privacy (DP) did not significantly impact the final learning convergence of the different aggregation strategies.
Captum: A unified and generic model interpretability library for PyTorch

In this paper we introduce a novel, unified, open-source model interpretability library for PyTorch [12]. The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms, also known as feature, neuron and layer importance algorithms, as well as a set of evaluation metrics for these algorithms. It can be used for both classification and non-classification models including graph-structured models built on Neural Networks (NN). In this paper we give a high-level overview of supported attribution algorithms and show how to perform memory-efficient and scalable computations. We emphasize that the three main characteristics of the library are multimodality, extensibility and ease of use. Multimodality supports different modality of inputs such as image, text, audio or video. Extensibility allows adding new algorithms and features. The library is also designed for easy understanding and use. Besides, we also introduce an interactive visualization tool called Captum Insights that is built on top of Captum library and allows sample-based model debugging and visualization using feature importance metrics.
Learning to Personalize for Web Search Sessions

The task of session search focuses on using interaction data to improve relevance for the user's next query at the session level. In this paper, we formulate session search as a personalization task under the framework of learning to rank. Personalization approaches re-rank results to match a user model. Such user models are usually accumulated over time based on the user's browsing behaviour. We use a pre-computed and transparent set of user models based on concepts from the social science literature. Interaction data are used to map each session to these user models. Novel features are then estimated based on such models as well as sessions' interaction data. Extensive experiments on test collections from the TREC session track show statistically significant improvements over current session search algorithms.
The relationship between dynamic programming and active inference: the discrete, finite-horizon case

Active inference is a normative framework for generating behaviour based upon the free energy principle, a theory of self-organisation. This framework has been successfully used to solve reinforcement learning and stochastic control problems, yet, the formal relation between active inference and reward maximisation has not been fully explicated. In this paper, we consider the relation between active inference and dynamic programming under the Bellman equation, which underlies many approaches to reinforcement learning and control. We show that, on partially observable Markov decision processes, dynamic programming is a limiting case of active inference. In active inference, agents select actions to minimise expected free energy. In the absence of ambiguity about states, this reduces to matching expected states with a target distribution encoding the agent's preferences. When target states correspond to rewarding states, this maximises expected reward, as in reinforcement learning. When states are ambiguous, active inference agents will choose actions that simultaneously minimise ambiguity. This allows active inference agents to supplement their reward maximising (or exploitative) behaviour with novelty-seeking (or exploratory) behaviour. This clarifies the connection between active inference and reinforcement learning, and how both frameworks may benefit from each other.
Confirming known planetary trends using a photometrically selected Kepler sample

Statistical studies of exoplanets and the properties of their host stars have been critical to informing models of planet formation. Numerous trends have arisen in particular from the rich Kepler data set, including that exoplanets are more likely to be found around stars with a high metallicity and the presence of a ‘gap’ in the distribution of planetary radii at 1.9 R⊕. Here we present a new analysis on the Kepler field, using the APOGEE spectroscopic survey to build a metallicity calibration based on Gaia, 2MASS and Strömgren photometry. This calibration, along with masses and radii derived from a Bayesian isochrone fitting algorithm, is used to test a number of these trends with unbiased, photometrically derived parameters, albeit with a smaller sample size in comparison to recent studies. We recover that planets are more frequently found around higher metallicity stars; over the entire sample, planetary frequencies are 0.88 ± 0.12 per cent for [Fe/H] < 0 and 1.37 ± 0.16 per cent for [Fe/H] ≥ 0 but at two sigma we find that the size of exoplanets influences the strength of this trend. We also recover the planet radius gap, along with a slight positive correlation with stellar mass. We conclude that this method shows promise to derive robust statistics of exoplanets. We also remark that spectrophotometry from Gaia DR3 will have an effective resolution similar to narrow band filters and allow to overcome the small sample size inherent in this study.
Micro- Mechanical assembly of high-quality Fabry-Perot microcavities for the integration with two-dimensional materials

Integrating monolayers of two-dimensional semiconductors in planar, and potentially microstructured microcavities is challenging because of the few, available approaches to overgrow the monolayers without damaging them. Some strategies have been developed, but they either rely on complicated experimental settings, expensive technologies or compromise the available quality factors. As a result, high quality Fabry-Perot microcavities are not widely available to the community focusing on light-matter coupling with atomically thin materials. Here, we provide details on a recently developed technique to micro-mechanically assemble Fabry-Perot Microcavities. Our approach does not rely on difficult or expensive technologies, and yields device characteristics marking the state of the art in cavities with integrated atomically thin semiconductors.
Genuine nonlinearity and its connection to the modified Korteweg–de Vries equation in phase dynamics

The study of hyperbolic waves involves various notions which help characterise how these structures evolve. One important facet is the notion of genuine nonlinearity, namely the ability for shocks and rarefactions to form instead of contact discontinuities. In the context of the Whitham modulation equations, this paper demonstrate that a loss of genuine nonlinearity leads to the appearance of a dispersive set of dynamics in the form of the modified Korteweg de-Vries equation governing the evolution of the waves instead. Its form is universal in the sense that its coefficients can be written entirely using linear properties of the underlying waves such as the conservation laws and linear dispersion relation. This insight is applied to two systems of physical interest, one an optical model and the other a stratified hydrodynamics experiment, to demonstrate how it can be used to provide insight into how waves in these systems evolve when genuine nonlinearity is lost.
FIGARO: Improving System Performance via Fine-Grained In-DRAM Data Relocation and Caching

Main memory, composed of DRAM, is a performance bottleneck for many applications, due to the high DRAM access latency. In-DRAM caches work to mitigate this latency by augmenting regular-latency DRAM with small-but-fast regions of DRAM that serve as a cache for the data held in the regular-latency (i.e., slow) region of DRAM. While an effective in-DRAM cache can allow a large fraction of memory requests to be served from a fast DRAM region, the latency savings are often hindered by inefficient mechanisms for migrating (i.e., relocating) copies of data into and out of the fast regions. Existing in-DRAM caches have two sources of inefficiency: (1) their data relocation granularity is an entire multi-kilobyte row of DRAM, even though much of the row may never be accessed due to poor data locality; and (2) because the relocation latency increases with the physical distance between the slow and fast regions, multiple fast regions are physically interleaved among slow regions to reduce the relocation latency, resulting in increased hardware area and manufacturing complexityWe propose a new substrate, FIGARO, that uses existing shared global buffers among subarrays within a DRAM bank to provide support for in-DRAM data relocation across subar-rays at the granularity of a single cache block. FIGARO has a distance-independent latency within a DRAM bank, and avoids complex modifications to DRAM (such as the interleaving of fast and slow regions). Using FIGARO, we design a fine-grained in-DRAM cache called FIGCache. The key idea of FIGCache is to cache only small, frequently-accessed portions of different DRAM rows in a designated region of DRAM. By caching only the parts of each row that are expected to be accessed in the near future, we can pack more of the frequently-accessed data into FIGCache, and can benefit from additional row hits in DRAM (i.e., accesses to an already-open row, which have a lower latency than accesses to an unopened row). FIGCache provides benefits for systems with both heterogeneous DRAM banks (i.e., banks with fast regions and slow regions) and conventional homogeneous DRAM banks (i.e., banks with only slow regions)Our evaluations across a wide variety of applications show that FIGCache improves the average performance of a system using DDR4 DRAM by 16.3% and reduces average DRAM energy consumption by 7.8% for 8-core workloads, over a conventional system without in-DRAM caching. We show that FIGCache outperforms state-of-the-art in-DRAM caching techniques, and that its performance gains are robust across many system and mechanism parameters.
nazgul: A statistical approach to gamma-ray burst localization. Triangulation via non stationary time series models

Context. Gamma-ray bursts can be located via arrival time signal triangulation using gamma-ray detectors in orbit throughout the solar system. The classical approach based on cross-correlations of binned light curves ignores the Poisson nature of the time-series data, and is unable to model the full complexity of the problem. Aims. To present a statistically proper and robust GRB timing/triangulation algorithm as a modern update to the original procedures used for the Interplanetary Network (IPN). Methods. A hierarchical Bayesian forward model for the unknown temporal signal evolution is learned via random Fourier features (RFF) and fitted to each detector's time-series data with time-differences that correspond to GRB's position on the sky via the appropriate Poisson likelihood. Results. Our novel method can robustly estimate the position of a GRB as verified via simulations. The uncertainties generated by the method are robust and in many cases more precise compared to the classical method. Thus, we have a method that can become a valuable tool for gravitational wave follow-up. All software and analysis scripts are made publicly available here (this https URL) for the purpose of replication.
Deuterated Polycyclic Aromatic Hydrocarbons in the Interstellar Medium: The C–D Band Strengths of Monodeuterated Species

Deuterium (D) is one of the light elements created in the Big Bang. As the Galaxy evolves, the D/H abundance in the interstellar medium (ISM) decreases from its primordial value due to astration. However, the observed gas-phase D/H abundances of some sightlines in the local Galactic ISM are substantially lower than the expected reduction by astration. The missing D could have been depleted onto polycyclic aromatic hydrocarbon (PAH) molecules which are ubiquitous and abundant in interstellar regions. To quantitatively explore the hypothesis of PAHs as a possible reservoir of interstellar D, we quantum-chemically compute the infrared vibrational spectra of monodeuterated PAHs and their cations. We find that, as expected, when H in PAHs is replaced by D, the C–H stretching and bending modes at 3.3, 8.6, and 11.3 μm shift to longer wavelengths at ∼4.4, 11.4, and 15.4 μm, respectively, by a factor of ∼ , the difference in reduced mass between the C–H and C–D oscillators. From the computed spectra we derive the mean intrinsic band strengths of the 3.3 μm C–H stretch and 4.4 μm C–D stretch to be and for neutral deuterated PAHs which would dominate the interstellar emission at 3.3 and 4.4 μm. By comparing the computationally derived mean band-strength ratio of for neutral PAHs with the mean ratio of the observed intensities of , we find that the degree of deuteration (i.e., the fraction of peripheral atoms attached to C atoms in the form of D) is ∼2.4%, corresponding to a D enrichment of a factor of ∼1200 with respect to the interstellar D/H abundance.
Classical dynamical density functional theory: from fundamentals to applications

Classical dynamical density functional theory (DDFT) is one of the cornerstones of modern statistical mechanics. It is an extension of the highly successful method of classical density functional theory (DFT) to nonequilibrium systems. Originally developed for the treatment of simple and complex fluids, DDFT is now applied in fields as diverse as hydrodynamics, materials science, chemistry, biology, and plasma physics. In this review, we give a broad overview over classical DDFT. We explain its theoretical foundations and the ways in which it can be derived. The relations between the different forms of deterministic and stochastic DDFT as well as between DDFT and related theories, such as quantum-mechanical time-dependent DFT, mode coupling theory, and phase field crystal models, are clarified. Moreover, we discuss the wide spectrum of extensions of DDFT, which covers methods with additional order parameters (like extended DDFT), exact approaches (like power functional theory), and systems with more complex dynamics (like active matter). Finally, the large variety of applications, ranging from fluid mechanics and polymer physics to solidification, pattern formation, biophysics, and electrochemistry, is presented.
ShapeAssembly

Manually authoring 3D shapes is difficult and time consuming; generative models of 3D shapes offer compelling alternatives. Procedural representations are one such possibility: they offer high-quality and editable results but are difficult to author and often produce outputs with limited diversity. On the other extreme are deep generative models: given enough data, they can learn to generate any class of shape but their outputs have artifacts and the representation is not editable. In this paper, we take a step towards achieving the best of both worlds for novel 3D shape synthesis. First, we propose ShapeAssembly, a domain-specific "assembly-language" for 3D shape structures. ShapeAssembly programs construct shape structures by declaring cuboid part proxies and attaching them to one another, in a hierarchical and symmetrical fashion. ShapeAssembly functions are parameterized with continuous free variables, so that one program structure is able to capture a family of related shapes. We show how to extract ShapeAssembly programs from existing shape structures in the PartNet dataset. Then, we train a deep generative model, a hierarchical sequence VAE, that learns to write novel ShapeAssembly programs. Our approach leverages the strengths of each representation: the program captures the subset of shape variability that is interpretable and editable, and the deep generative model captures variability and correlations across shape collections that is hard to express procedurally. We evaluate our approach by comparing the shapes output by our generated programs to those from other recent shape structure synthesis models. We find that our generated shapes are more plausible and physically-valid than those of other methods. Additionally, we assess the latent spaces of these models, and find that ours is better structured and produces smoother interpolations. As an application, we use our generative model and differentiable program interpreter to infer and fit shape programs to unstructured geometry, such as point clouds.
Layer-stacked Attention for Heterogeneous Network Embedding

The heterogeneous network is a robust data abstraction that can model entities of different types interacting in various ways. Such heterogeneity brings rich semantic information but presents nontrivial challenges in aggregating the heterogeneous relationships between objects - especially those of higher-order indirect relations. Recent graph neural network approaches for representation learning on heterogeneous networks typically employ the attention mechanism, which is often only optimized for predictions based on direct links. Furthermore, even though most deep learning methods can aggregate higher-order information by building deeper models, such a scheme can diminish the degree of interpretability. To overcome these challenges, we explore an architecture - Layer-stacked ATTention Embedding (LATTE) - that automatically decomposes higher-order meta relations at each layer to extract the relevant heterogeneous neighborhood structures for each node. Additionally, by successively stacking layer representations, the learned node embedding offers a more interpretable aggregation scheme for nodes of different types at different neighborhood ranges. We conducted experiments on several benchmark heterogeneous network datasets. In both transductive and inductive node classification tasks, LATTE can achieve state-of-the-art performance compared to existing approaches, all while offering a lightweight model. With extensive experimental analyses and visualizations, the framework can demonstrate the ability to extract informative insights on heterogeneous networks.
Coordinate Methods for Matrix Games

We develop primal-dual coordinate methods for solving bilinear saddle-point problems of the form $\min\nolimits_{x\in \mathcal{X}}\max\nolimits_{y\in \mathcal{Y}}y^{\top}Ax$ which contain linear programming, classification, and regression as special cases. Our methods push existing fully stochastic sublinear methods and variance-reduced methods towards their limits in terms of per-iteration complexity and sample complexity. We obtain nearly-constant per-iteration complexity by designing efficient data structures leveraging Taylor approximations to the exponential and a binomial heap. We improve sample complexity via low-variance gradient estimators using dynamic sampling distributions that depend on both the iterates and the magnitude of the matrix entries. Our runtime bounds improve upon those of existing primal-dual methods by a factor depending on sparsity measures of the $m$ by $n$ matrix $A$. For example, when rows and columns have constant $\ell_{1}/\ell_{2}$ norm ratios, we offer improvements by a factor of $m+n$ in the fully stochastic setting and $\sqrt{m+n}$ in the variance-reduced setting. We apply our methods to computational geometry problems, i.e. minimum enclosing ball, maximum inscribed ball, and linear regression, and obtain improved complexity bounds. For linear regression with an elementwise nonnegative matrix, our guarantees improve on exact gradient methods by a factor of $\sqrt{\text{nnz}(A)/(m+n)}$.
Strong scattering and parallel guiding of ultracold neutrons

For ultracold neutrons with a kinetic energy below 10 neV, strong scattering, characterized by $2\pi l_{c} / \lambda\leq 1$, can be obtained in metamaterials of C and $^7$Li. Here $l_{c}$ and $\lambda$ are the coherent scattering mean free path and the neutron wavelength, respectively. UCN interferometry and high-resolution spectroscopy (nano-electronvolt to pico-electronvolt resolution) in parallel waveguide arrays of neutronic metamaterials are given as examples of new experimental possibilities.