Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-shot ToF sensing with sub-mm precision using conventional CMOS sensors

We present a novel single-shot interferometric ToF camera targeted for precise 3D measurements of dynamic objects. The camera concept is based on Synthetic Wavelength Interferometry, a technique that allows retrieval of depth maps of objects with optically rough surfaces at submillimeter depth precision. In contrast to conventional ToF cameras, our device uses only off-the-shelf CCD/CMOS detectors and works at their na-tive chip resolution (as of today, theoretically up to 20 Mp and beyond). Moreover, we can obtain a full 3D model of the object in single-shot, meaning that no temporal sequence of exposures or temporal illumination modulation (such as amplitude or frequency modulation) is necessary, which makes our camera robust against object motion. In this paper, we introduce the novel camera concept and show ﬁrst measurements that demonstrate the capabilities of our system. We present 3D measurements of small (cm-sized) objects with > 2 Mp point cloud resolution (the resolution of our used detector) and up to sub-mm depth precision. We also report a “single-shot 3D video” acquisition and a ﬁrst single-shot “Non-Line-of-Sight” measurement. Our technique has great potential for high-precision applications with dynamic object movement, e.g., in AR/VR, industrial inspection, medical imaging, and imaging through scattering media like fog or human tissue.
High acceleration events : An introduction and review of expert opinion

Abstract This investigation was designed as a rapid review of available knowledge on high acceleration events in practice. The aim was to consider if the risks associated with regular exposure justify further investigation of the problem. Three linked investigations were carried out: to gather published information, to gather expert opinion, and to analyse available vehicle records. The literature showed that although no fully acceptable epidemiological data are available, exposure to high levels of vibration and shock provably increases the risk of back problems. Experts considered high acceleration events important as regards the health of drivers of certain vehicles. They thought that more research into the effects on health is needed but comfort and epidemiological investigations have some severe practical drawbacks as well as advantages. More biodynamic research is needed to engender plausible hypotheses on the effects on health before consideration of further epidemiological research and generation of dose-response relationships. Vehicle measurements confirmed information from the literature that BS6841 [1]Wbweighted r.m.s. values over 4 s of up to 5·6 m/s2and unweighted peak values of at least 20 m/s2occur withWbweighted peak values of up to 15 m/s2and sometimes nearly 30 m/s2. Daily Vibration Dose Values of the order of 20 ms−1·75are quite probable. Measured accelerations suggested some common features of high acceleration events. Many are of an oscillatory nature with frequencies of the order of 2–3 Hz, whilst large magnitude transient accelerations of the order of 25–20 ms duration also occur in certain situations. The long term weighted r.m.s. value does not reflect the presence of high acceleration events that occur once every few minutes and may underestimate the health risk.
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models

This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or interactions between users in a social network. Our goal is to forecast the future evolution of ensembles of multivariate time series in such applications (e.g., the future return of a financial asset or the future popularity of a Twitter account). Designing ML algorithms for such systems requires addressing the challenges of high-dimensional interactions and non-linearity. Existing approaches usually adopt an ad-hoc approach to integrating high-dimensional techniques into non-linear models and recent studies have shown these approaches have questionable efficacy in time-evolving interacting systems. To this end, we propose a novel framework, which we dub as the additive influence model. Under our modeling assumption, we show that it is possible to decouple the learning of high-dimensional interactions from the learning of non-linear feature interactions. To learn the high-dimensional interactions, we leverage kernel-based techniques, with provable guarantees, to embed the entities in a low-dimensional latent space. To learn the non-linear feature-response interactions, we generalize prominent machine learning techniques, including designing a new statistically sound non-parametric method and an ensemble learning algorithm optimized for vector regressions. Extensive experiments on two common applications demonstrate that our new algorithms deliver significantly stronger forecasting power compared to standard and recently proposed methods.
Nonlinear controllability and function representation by neural stochastic differential equations

There has been a great deal of recent interest in learning and approximation of functions that can be expressed as expectations of a given nonlinearity with respect to its random internal parameters. Examples of such representations include"infinitely wide"neural nets, where the underlying nonlinearity is given by the activation function of an individual neuron. In this paper, we bring this perspective to function representation by neural stochastic differential equations (SDEs). A neural SDE is an It\^o diffusion process whose drift and diffusion matrix are elements of some parametric families. We show that the ability of a neural SDE to realize nonlinear functions of its initial condition can be related to the problem of optimally steering a certain deterministic dynamical system between two given points in finite time. This auxiliary system is obtained by formally replacing the Brownian motion in the SDE by a deterministic control input. We derive upper and lower bounds on the minimum control effort needed to accomplish this steering; these bounds may be of independent interest in the context of motion planning and deterministic optimal control.
Predict-and-Critic: Accelerated End-to-End Predictive Control for Cloud Computing through Reinforcement Learning

Cloud computing holds the promise of reduced costs through economies of scale. To realize this promise, cloud computing vendors typically solve sequential resource allocation problems, where customer workloads are packed on shared hardware. Virtual machines (VM) form the foundation of modern cloud computing as they help logically abstract user compute from shared physical infrastructure. Traditionally, VM packing problems are solved by predicting demand, followed by a Model Predictive Control (MPC) optimization over a future horizon. We introduce an approximate formulation of an industrial VM packing problem as an MILP with soft-constraints parameterized by the predictions. Recently, predict-and-optimize (PnO) was proposed for end-to-end training of prediction models by back-propagating the cost of decisions through the optimization problem. But, PnO is unable to scale to the large prediction horizons prevalent in cloud computing. To tackle this issue, we propose the Predict-and-Critic (PnC) framework that outperforms PnO with just a two-step horizon by leveraging reinforcement learning. PnC jointly trains a prediction model and a terminal Q function that approximates cost-to-go over a long horizon, by back-propagating the cost of decisions through the optimization problem \emph{and from the future}. The terminal Q function allows us to solve a much smaller two-step horizon optimization problem than the multi-step horizon necessary in PnO. We evaluate PnO and the PnC framework on two datasets, three workloads, and with disturbances not modeled in the optimization problem. We find that PnC significantly improves decision quality over PnO, even when the optimization problem is not a perfect representation of reality. We also find that hardening the soft constraints of the MILP and back-propagating through the constraints improves decision quality for both PnO and PnC.
Impact of generalized holonomy corrections on the cosmological primordial power spectra

The propagation of perturbations is studied with generalized holonomy corrections in a fully consistent way, ensuring that the deformed algebra of constraints remains closed. The primordial cosmological power spectra are calculated. It is shown that, although the detailed form of the correction does unavoidably impact the observables, the main known results of loop quantum cosmology are robust in this respect.
Some comments on transient and steady‐state reconnection at the dayside magnetopause

Reconnection at the dayside magnetopause is the principal method by which energy is transferred from the solar wind into the magnetosphere‐ionosphere system. There is still considerable uncertainty as to whether reconnection is transient or quasi‐steady. Here we discuss some of the implications of assuming that reconnection occurring in regions where the magnetosheath flow is super‐Alfvénic must be transient. We use a simple magnetospheric model to illustrate where on the magnetopause transient reconnection is most likely to occur, and show that the location of these regions is dependent critically upon the dipole tilt angle and the interplanetary magnetic field orientation. Although our idealised examples do not take into account temporal variations of the solar wind conditions, or the influence of the bow shock or magnetosheath, we believe that they demonstrate interesting features. For example, the results suggest that reconnection for northward IMF is almost always likely to be transient.
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments

In reinforcement learning (RL) research, simulations enable benchmarks between algorithms, as well as prototyping and hyper-parameter tuning of agents. In order to promote RL both in research and real-world applications, frameworks are required which are on the one hand efficient in terms of running experiments as fast as possible. On the other hand, they must be flexible enough to allow the integration of newly developed optimization techniques, e.g. new RL algorithms, which are continuously put forward by an active research community. In this paper, we introduce Karolos, a RL framework developed for robotic applications, with a particular focus on transfer scenarios with varying robot-task combinations reflected in a modular environment architecture. In addition, we provide implementations of state-of-the-art RL algorithms along with common learning-facilitating enhancements, as well as an architecture to parallelize environments across multiple processes to significantly speed up experiments. The code is open source and published on GitHub with the aim of promoting research of RL applications in robotics.
Viral RNA as a branched polymer

Myriad viruses use positive-strand RNA molecules as their genomes. Far from being only a repository of genetic material, viral RNA performs numerous other functions mediated by its physical structure and chemical properties. In this chapter, we focus on its structure and discuss how long RNA molecules can be treated as branched polymers through planar graphs. We describe the major results that can be obtained by this approach, in particular the observation that viral RNA genomes have a characteristic compactness that sets them aside from similar random RNAs. We also discuss how different parameters used in the current RNA folding software influence the resulting structures and how they can be related to experimentally observable quantities. Finally, we show how the connection to branched polymers can be extended to take advantage of known results from polymer physics and can be further moulded to include additional interactions, such as excluded volume or electrostatics.
Distributed ADMM with linear updates over directed networks

We propose a distributed version of the Alternating Direction Method of Multipliers (ADMM) with linear updates for directed networks. We show that if the objective function of the minimization problem is smooth and strongly convex, our distributed ADMM algorithm achieves a geometric rate of convergence to the optimal point. Our algorithm exploits the robustness inherent to ADMM by not enforcing accurate consensus, thereby significantly improving the convergence rate. We illustrate this by numerical examples, where we compare the performance of our algorithm with that of state-of-the-art ADMM methods over directed graphs.
Hough Dönüşümü Kullanılarak Protein Yapısal Bloklarının Karşılaştırılması - Protein Structural Block Comparison by using Hough Transform

This paper presents a new approach for motif retrieval by comparing protein secondary structures. This approach is tested as two different methods based on Generalized Hough Transform (GHT). In the first one secondary structure couples are used, and midpoint distance, axis distance and axis angle, related to the couple, are considered comparison parameters. In the second one secondary structure triplets are used, the triangles are defined by joining the midpoints of secondary structures and the edge lengths of triangles are considered as comparison parameters. In both methods the barycenter of the motif is assigned as reference point (RP). Then motif couples and motif triplets are compared with protein couples and triplets respectively using the first and second methods, and
Optimizing the clipping frequency and nitrogen topdressing in a dual-purpose oat used for fodder and cover cropping

Summary Management strategies such as nitrogen (N) topdressing and clipping can be used to optimize a cover crop for the dual purpose of soil cover and forage. The present study tested oat (Avena sativa) for a holistic provision of soil cover and forage under various levels of clipping frequency and N topdressing. Effects on root and above-ground biomass, acid detergent fiber, neutral detergent fiber, and crude protein (CP) were evaluated. Clipping frequency had four levels, namely clipped only at termination (C1), clipped at 28 days after emergence (DAE) and termination (C2), clipped at 28, 42 DAE, and termination (C3), and clipped at 28, 42, 56 DAE, and termination (C4). Nitrogen topdressing had two levels, namely with (N1) and without (N0) the recommended N topdressing. Increasing clipping frequency reduced the root and aerial biomass and did not affect the forage quality harvested before termination. However, N topdressing increased biomass and CP content across the clipping frequencies. Results suggest clipping thrice combined with N topdressing (C4 + N1) provides the best option to satisfy both soil cover and livestock demands. The treatment (C4 + N1) gave > 2 t ha−1 of biomass during the growing period and 6 t ha−1 at termination which can be used for livestock forage and soil cover, respectively. Clipping thrice without N topdressing (C4 + N0) was the best option for resource-constrained farmers.
On the ecological interpretation of limits of interaural time difference sensitivity

Human listeners, and other animals too, use interaural time differences (ITD) to localize pure tones, but this ability abruptly diminishes as the frequency of a pure tone increases. The diminished sensitivity appears to serve a useful function. It prevents the confusion that would otherwise arise from the large interaural phase differences that occur at high frequency as sound waves diffract around the head. Possibly this benefit offers an ecological explanation for the diminished sensitivity of the nervous system. However, comparison of the frequency dependence of ITD sensitivity, as measured in headphone experiments, and the frequency dependence of the physical phase shifts, as measured in an anechoic room, reveals a bad match between these two functions. The decrease in neural sensitivity to ITD is seen to be far too rapid, casting doubt on this form of ecological reasoning. If one wants to maintain an ecological context, it is more plausible to argue that our binaural architecture, with its neurophysi.
Health Equity Is No Spectator Sport: The Radical Rooting of a Post-Pandemic Bioethics

ABSTRACT:The relationship between equality and equity has been theorized and described in many ways. Recently, this relationship has been popularly illustrated via a meme depicting three people watching a baseball game while standing on boxes. The meme's analogy, that achieving health equity is the ability to view a spectator sport, is a neoliberal account of health. The analogy defines equality at the expense of equity, characterizes health as individualistic, describes health equity as a static outcome, and implies that the bioethical questions for achieving health equity are about the distribution and redistribution of resources. As the SARS CoV-2 pandemic vividly reminded us, health equity is no spectator sport. Health equity is the intentional removal of obstacles to health and the enabling of human flourishing to assure that everyone has fair and just opportunities for health. That relational and radical account of health equity requires different analogies. This article reimagines and re-illustrates the relationship between equality and equity using an organic analogy of three plants. The analogy calls for bioethics to nurture communal relationships and engage community leaders in pursuit of communal flourishing.
Dynamical models and our virgocentric deviation from Hubble flow

Dynamical models are used to calculate . delta.V, the velocity deviation from pure Hubble flow of the Local Group due to the gravitational effect of the Virgo galaxy cluster and its surrounding supercluster, for various values of the cosmological density parameter . cap omega. We require our models to fit the detailed observations of the dispersion of systemic galaxy velocities in and around the cluster core, but allow the ratio of mass density rho to luminosity density L to vary with distance from the cluster center. Our simplest such models show that . delta.V actually decreases with increasing . cap omega. , essentially because . delta.V depends on the excess mass (i.e., density contrast) rather than on the total mass. We show that this trend is not reversed by ''energy segregation'' between the luminous galaxies and the ''invisible matter,'' nor by the effects of the observed flattening of the outskirts of the Supercluster.
Photoresponses in polycrystalline silicon phototransistors incorporating germanium quantum dots in the gate dielectrics

Polycrystalline silicon (poly-Si) thin-film transistors (TFTs) incorporating germanium (Ge) quantum dots (QDs) in the gate oxide were fabricated as efficient blue to near ultraviolet phototransistors for light detection and amplification. Under 405–450 nm light illumination, Ge QDs poly-Si TFTs exhibit not only strong photoresponses in the drive current but also much improved subthreshold characteristics than that measured in darkness. This originates from the fact that only photoexcited holes within Ge QDs are injected into the active channel via vertical electric field and contribute excess mobile carriers for photocurrent but without the associated photogenerated electron induced junction barrier lowering.
Metagenome-derived virus-microbe ratios across ecosystems

It is generally assumed that viruses outnumber cells on Earth by at least tenfold. Virus-to-microbe ratios (VMR) are largely based on counts of fluorescently labelled virus-like particles. However, these exclude intracellular viruses and potentially include false positives (DNA-containing vesicles, gene-transfer agents, unspecifically stained inert particles). Here, we develop a metagenome-based VMR estimate (mVRM) that accounts for DNA viruses across all stages of their replication cycles (virion, intracellular lytic and lysogenic) by using normalised RPKM (reads per kilobase of gene sequence per million of mapped metagenome reads) counts of the major capsid protein (MCP) genes and cellular universal single-copy genes (USCGs) as proxies for virus and cell counts, respectively. After benchmarking this strategy using mock metagenomes with increasing VMR, we inferred mVMR across different biomes. To properly estimate mVMR in aquatic ecosystems, we generated metagenomes from co-occurring cellular and viral fractions (>50 kDa-200 µm size-range) in freshwater, seawater and solar saltern ponds (10 metagenomes, 2 control metaviromes). Viruses outnumbered cells in freshwater by ∼13 fold and in plankton from marine and saline waters by ∼2-4 fold. However, across an additional set of 121 diverse non-aquatic metagenomes including microbial mats, microbialites, soils, freshwater and marine sediments and metazoan-associated microbiomes, viruses, on average, outnumbered cells by barely two-fold. Although viruses likely are the most diverse biological entities on Earth, their global numbers might be closer to those of cells than previously estimated.
Unclearing Microscopy

The spatial resolution and contrast sensitivity of the human eye is limited, restricting our ability to directly see subcellular structures. We report a new principle for unaided eye cellular visualization in a method we call Unclearing Microscopy. By expanding cells and tissue >8,000 volumetrically and opaquing their bulk with light-scattering molecules of sufficient density, cell microstructure can now be discerned with a contrast visible to the unaided eye. We further inspect uncleared samples with transmitted light microscopy modalities and prove that 3D ultrastructural features, previously accessible only with super-resolution fluorescence or electron microscopy methods, can now be visualized with simple magnification optics alone.
Trade-offs between baseline thermal tolerance and thermal tolerance plasticity are much less common than it appears

Thermal tolerance plasticity is a core mechanism by which organisms can mitigate the effects of climate change. As a result, there is a need to understand how variation in tolerance plasticity arises. The Baseline Tolerance/Plasticity Trade-off Hypothesis (hereafter referred to as the Trade-off Hypothesis, TOH) has recently emerged as a potentially powerful explanation. The TOH posits that organisms with high baseline thermal tolerance have reduced thermal tolerance plasticity relative to those with low baseline tolerance. Many studies have found support for the TOH. However, this support must be regarded cautiously because the most common means of testing the TOH can yield spurious ‘trade-offs’ due to regression to the mean. I acquired data for 25 previously-published analyses that supported the TOH at the intraspecific level and reanalyzed them after applying a method that adjusts plasticity estimates for regression to the mean. Only six of the 25 analyses remained statistically significant after adjustment, and effect size and variance explained decreased in all cases. The few data sets in which support for the TOH was maintained after adjustment point to areas of future study, but are too few to make generalizations at this point. In sum, regression to the mean has led to a substantial overestimation of support for the TOH and must be accounted for in future tests of the hypothesis.