Miracle-dz/UniFAR-FTUs huggingface dataset:
Haemodynamic effects of spinally administered ketamine and its combination with xylazine or medetomidine in goats

The study was conducted in 9 adult goats of either sex weighing 15 to 20 kg. Nine trials each of the 3 treatments were conducted using ketamine (2.5 mg/kg), ketamine and xylazine (2.5mg/kg and 0.05mg/kg), and ketamine and medetomidine (2.5mg/kg and 10mg/kg). The drugs were administered at the lumbosacral subarachnoid space under aseptic conditions. The treatments were evaluated on the basis of physiological and haemodynamic observations. Cardiopulmonary depression in the combination groups and cardiopulmonary stimulation in ketamine group was recorded. A decrease in MAP and an increase in CVP was recorded in the combination groups. ECG showed bradycardia and occasional arrhythmias in combination groups. The results showed that the combination of ketamine with xylazine or medetomidine produce comparable degree of transient cardiopulmonary and haemodynamic changes.
Spectra2pix: Generating Nanostructure Images from Spectra

The design of the nanostructures that are used in the field of nano-photonics has remained complex, very often relying on the intuition and expertise of the designer, ultimately limiting the reach and penetration of this groundbreaking approach. Recently, there has been an increasing number of studies suggesting to apply Machine Learning techniques for the design of nanostructures. Most of these studies engage Deep Learning techniques, which entails training a Deep Neural Network (DNN) to approximate the highly non-linear function of the underlying physical process between spectra and nanostructures. At the end of the training, the DNN allows an on-demand design of nanostructures, i.e. the model can infer nanostructure geometries for desired spectra. In this work, we introduce spectra2pix, which is a model DNN trained to generate 2D images of the designed nanostructures. Our model architecture is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry. We show, for the first time, a successful generalization ability by designing a completely unseen sub-family of geometries. This generalization capability highlights the importance of our model architecture, and allows higher applicability for real-world design problems.
A geometric characterization of minimal codes and their asymptotic performance

In this paper, we give a geometric characterization of minimal linear codes. In particular, we relate minimal linear codes to cutting blocking sets, introduced in a recent paper by Bonini and Borello. Using this characterization, we derive some bounds on the length and the distance of minimal codes, according to their dimension and the underlying field size. Furthermore, we show that the family of minimal codes is asymptotically good. Finally, we provide some geometrical constructions of minimal codes.
Separateness of Variables - A Novel Perspective on Decidable First-Order Fragments

The classical decision problem, as it is understood today, is the quest for a delineation between the decidable and the undecidable parts of first-order logic based on elegant syntactic criteria. In this paper, we treat the concept of separateness of variables and explore its applicability to the classical decision problem. Two disjoint sets of first-order variables are separated in a given formula if variables from the two sets never co-occur in any atom of that formula. This simple notion facilitates extending many well-known decidable first-order fragments significantly and in a way that preserves decidability. We will demonstrate that for several prefix fragments, several guarded fragments, the two-variable fragment, and for the fluted fragment. Altogether, we will investigate nine such extensions more closely. Interestingly, each of them contains the relational monadic first-order fragment without equality. Although the extensions exhibit the same expressive power as the respective originals, certain logical properties can be expressed much more succinctly. In three cases the succinctness gap cannot be bounded using any elementary function.
Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes

The asymptotic analysis of covariance parameter estimation of Gaussian processes has been subject to intensive investigation. However, this asymptotic analysis is very scarce for non-Gaussian processes. In this paper, we study a class of non-Gaussian processes obtained by regular non-linear transformations of Gaussian processes. We provide the increasing-domain asymptotic properties of the (Gaussian) maximum likelihood and cross validation estimators of the covariance parameters of a non-Gaussian process of this class. We show that these estimators are consistent and asymptotically normal, although they are defined as if the process was Gaussian. They do not need to model or estimate the non-linear transformation. Our results can thus be interpreted as a robustness of (Gaussian) maximum likelihood and cross validation towards non-Gaussianity. Our proofs rely on two technical results that are of independent interest for the increasing-domain asymptotic literature of spatial processes. First, we show that, under mild assumptions, coefficients of inverses of large covariance matrices decay at an inverse polynomial rate as a function of the corresponding observation location distances. Second, we provide a general central limit theorem for quadratic forms obtained from transformed Gaussian processes. Finally, our asymptotic results are illustrated by numerical simulations.
High precision numerical approach for the Davey-Stewartson II equation for Schwartz class initial data

We present an efficient high-precision numerical approach for the Davey-Stewartson (DS) II equation, treating initial data from the Schwartz class of smooth, rapidly decreasing functions. As with previous approaches, the presented code uses discrete Fourier transforms for the spatial dependence and Driscoll's composite Runge-Kutta method for the time dependence. Since the DS equation is a nonlocal, nonlinear Schrodinger equation with a singular symbol for the nonlocality, standard Fourier methods in practice only reach accuracies of the order of $10^{-6}$ or less for typical examples. This was previously demonstrated for the defocusing case by comparison with a numerical approach for DS via inverse scattering. By applying a regularization to the singular symbol, originally developed for D-bar problems, the presented code is shown to reach machine precision. The code can treat integrable and non-integrable DS II equations. Moreover, it has the same %complexity numerical complexity as existing codes for DS II. Several examples for the integrable defocusing DS II equation are discussed as test cases. In an appendix by C. ~Kalla, a doubly periodic solution to the defocusing DS II equation is presented, providing a test for direct DS codes based on Fourier methods.
PIQA: Reasoning about Physical Commonsense in Natural Language

To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to today's natural language understanding systems. While recent pretrained models (such as BERT) have made progress on question answering over more abstract domains – such as news articles and encyclopedia entries, where text is plentiful – in more physical domains, text is inherently limited due to reporting bias. Can AI systems learn to reliably answer physical commonsense questions without experiencing the physical world? In this paper, we introduce the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Though humans find the dataset easy (95% accuracy), large pretrained models struggle (∼75%). We provide analysis about the dimensions of knowledge that existing models lack, which offers significant opportunities for future research.
The Metallicity Gradient and Complex Formation History of the Outermost Halo of the Milky Way

We present an examination of the metallicity distribution function of the outermost stellar halo of the Galaxy based on an analysis of both local (within 4 kpc of the Sun, ∼16,500 stars) and non-local (∼21,700 stars) samples. These samples were compiled using spectroscopic metallicities from the Sloan Digital Sky Survey and photometric metallicities from the SkyMapper Southern Survey. We detect a negative metallicity gradient in the outermost halo (r > 35 kpc from the Galactic center), and find that the frequency of very metal-poor ([Fe/H] < −2.0) stars in the outer-halo region reaches up to ∼60% in our most distant sample, commensurate with previous theoretical predictions. This result provides clear evidence that the outer-halo formed hierarchically. The retrograde stars in the outermost halo exhibit a roughly constant metallicity, which may be linked to the accretion of the Sequoia progenitor. In contrast, prograde stars in the outermost halo exhibit a strong metallicity-distance dependence, indicating that they likely originated from the accretion of galaxies less massive than the Sequoia progenitor galaxy.
Nonuniversal transition to condensate formation in two-dimensional turbulence

The occurrence of system-scale coherent structures, so-called condensates, is a well-known phenomenon in two-dimensional turbulence. Here, the transition to condensate formation is investigated as a function of the magnitude of the force and for different types of forcing. Random forces with constant mean energy input lead to a supercritical transition, while forcing through a small-scale linear instability results in a subcritical transition with bistability and hysteresis. That is, the transition to condensate formation in two-dimensional turbulence is nonuniversal. For the supercritical case we quantify the effect of large-scale friction on the value of the critical exponent and the location of the critical point.
Privacy preserving Neural Network Inference on Encrypted Data with GPUs

Machine Learning as a Service (MLaaS) has become a growing trend in recent years and several such services are currently offered. MLaaS is essentially a set of services that provides machine learning tools and capabilities as part of cloud computing services. In these settings, the cloud has pre-trained models that are deployed and large computing capacity whereas the clients can use these models to make predictions without having to worry about maintaining the models and the service. However, the main concern with MLaaS is the privacy of the client's data. Although there have been several proposed approaches in the literature to run machine learning models on encrypted data, the performance is still far from being satisfactory for practical use. In this paper, we aim to accelerate the performance of running machine learning on encrypted data using combination of Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). We use a number of optimization techniques, and efficient GPU-based implementation to achieve high performance. We evaluate a CNN whose architecture is similar to AlexNet to classify homomorphically encrypted samples from the Cars Overhead With Context (COWC) dataset. To the best of our knowledge, it is the first time such a complex network and large dataset is evaluated on encrypted data. Our approach achieved reasonable classification accuracy of 95% for the COWC dataset. In terms of performance, our results show that we could achieve several thousands times speed up when we implement GPU-accelerated FHE operations on encrypted floating point numbers.
On the Complexity of Minimum-Cost Networked Estimation of Self-Damped Dynamical Systems

In this article, we consider the optimal design of networked estimators to minimize the communication/measurement cost under the networked observability constraint. This problem is known as the minimum-cost networked estimation problem, which is generally claimed to be NP-hard. The main contribution of this work is to provide a polynomial-order solution for this problem under the constraint that the underlying dynamical system is self-damped. Using structural analysis, we subdivide the main problem into two NP-hard subproblems known as (i) optimal sensor selection, and (ii) minimum-cost communication network. For self-damped dynamical systems, we provide a polynomial-order solution for subproblem (i). Further, we show that the subproblem (ii) is of polynomial-order complexity if the links in the communication network are bidirectional. We provide an illustrative example to explain the methodologies.
Inverse Random Source Scattering for the Helmholtz Equation with Attenuation

In this paper, a new model is proposed for the inverse random source scattering problem of the Helmholtz equation with attenuation. The source is assumed to be driven by a fractional Gaussian field whose covariance is represented by a classical pseudo-differential operator. The work contains three contributions. First, the connection is established between fractional Gaussian fields and rough sources characterized by their principal symbols. Second, the direct source scattering problem is shown to be well-posed in the distribution sense. Third, we demonstrate that the micro-correlation strength of the random source can be uniquely determined by the passive measurements of the wave field in a set which is disjoint with the support of the strength function. The analysis relies on careful studies on the Green function and Fourier integrals for the Helmholtz equation.
Shot noise in multitracer constraints on fNL and relativistic projections: Power spectrum

Multiple tracers of the same surveyed volume can enhance the signal-to-noise on a measurement of local primordial non-Gaussianity and the relativistic projections. Increasing the number of tracers comparably increases the number of shot noise terms required to describe the stochasticity of the data. Although the shot noise is white on large scales, it is desirable to investigate the extent to which it can degrade constraints on the parameters of interest. In a multi-tracer analysis of the power spectrum, a marginalization over shot noise does not degrade the constraints on $f_\text{NL}$ by more than $\sim 30$% so long as halos of mass $M\lesssim 10^{12}M_\odot$ are resolved. However, ignoring cross shot noise terms induces large systematics on a measurement of $f_\text{NL}$ at redshift $z<1$ when small mass halos are resolved. These effects are less severe for the relativistic projections, especially for the dipole term. In the case of a low and high mass tracer, the optimal sample division maximizes the signal-to-noise on $f_\text{NL}$ and the projection effects simultaneously, reducing the errors to the level of $\sim 10$ consecutive mass bins of equal number density. We also emphasize that the non-Poissonian noise corrections that arise from small-scale clustering effects cannot be measured with random dilutions of the data. Therefore, they must either be properly modeled or marginalized over.
Pseudo-deterministic Streaming

A pseudo-deterministic algorithm is a (randomized) algorithm which, when run multiple times on the same input, with high probability outputs the same result on all executions. Classic streaming algorithms, such as those for finding heavy hitters, approximate counting, $\ell_2$ approximation, finding a nonzero entry in a vector (for turnstile algorithms) are not pseudo-deterministic. For example, in the instance of finding a nonzero entry in a vector, for any known low-space algorithm $A$, there exists a stream $x$ so that running $A$ twice on $x$ (using different randomness) would with high probability result in two different entries as the output. In this work, we study whether it is inherent that these algorithms output different values on different executions. That is, we ask whether these problems have low-memory pseudo-deterministic algorithms. For instance, we show that there is no low-memory pseudo-deterministic algorithm for finding a nonzero entry in a vector (given in a turnstile fashion), and also that there is no low-dimensional pseudo-deterministic sketching algorithm for $\ell_2$ norm estimation. We also exhibit problems which do have low memory pseudo-deterministic algorithms but no low memory deterministic algorithm, such as outputting a nonzero row of a matrix, or outputting a basis for the row-span of a matrix. We also investigate multi-pseudo-deterministic algorithms: algorithms which with high probability output one of a few options. We show the first lower bounds for such algorithms. This implies that there are streaming problems such that every low space algorithm for the problem must have inputs where there are many valid outputs, all with a significant probability of being outputted.
F3Net: Fusion, Feedback and Focus for Salient Object Detection

Most of existing salient object detection models have achieved great progress by aggregating multi-level features extracted from convolutional neural networks. However, because of the different receptive fields of different convolutional layers, there exists big differences between features generated by these layers. Common feature fusion strategies (addition or concatenation) ignore these differences and may cause suboptimal solutions. In this paper, we propose the F3Net to solve above problem, which mainly consists of cross feature module (CFM) and cascaded feedback decoder (CFD) trained by minimizing a new pixel position aware loss (PPA). Specifically, CFM aims to selectively aggregate multi-level features. Different from addition and concatenation, CFM adaptively selects complementary components from input features before fusion, which can effectively avoid introducing too much redundant information that may destroy the original features. Besides, CFD adopts a multi-stage feedback mechanism, where features closed to supervision will be introduced to the output of previous layers to supplement them and eliminate the differences between features. These refined features will go through multiple similar iterations before generating the final saliency maps. Furthermore, different from binary cross entropy, the proposed PPA loss doesn't treat pixels equally, which can synthesize the local structure information of a pixel to guide the network to focus more on local details. Hard pixels from boundaries or error-prone parts will be given more attention to emphasize their importance. F3Net is able to segment salient object regions accurately and provide clear local details. Comprehensive experiments on five benchmark datasets demonstrate that F3Net outperforms state-of-the-art approaches on six evaluation metrics. Code will be released at https://github.com/weijun88/F3Net.
Accelerated orbital decay of supermassive black hole binaries in merging nuclear star clusters

The coalescence of supermassive black holes (SMBHs) should generate the strongest sources of gravitational waves (GWs) in the Universe. However, the dynamics of their coalescence is the subject of much debate. In this study, we use a suite of N-body simulations to follow the merger of two nuclear star clusters (NSCs), each hosting an SMBH in their centre. We find that the presence of distinct star clusters around each SMBH has important consequences for the dynamical evolution of the SMBH binary: (i) The separation between the SMBHs decreases by a few orders of magnitude in the first few Myrs by the combined effects of dynamical friction and a drag force caused by tidally stripped stars. In fact, this is a significant speedup for equal mass ratio binaries, and becomes extreme for unequal mass ratios, e.g. 1:10 or 1:100, which traditional dynamical friction alone would not permit to bind. (ii) The subsequent binary hardening is driven by the gravitational slingshots between the SMBH binary and stars, and also depends on the mass ratio between the SMBHs. Thus, with this additional drag force, we find that all SMBHs in our suite coalesce within a Hubble time. Given that about 50 per cent of Milky Way-sized galaxies host NSCs, our results are encouraging for upcoming GW observations with the Laser Interferometer Space Antenna – LISA – which will detect SMBH coalescence in the 104–107 M⊙ mass range.
On the Hewitt Stromberg dimension of product sets

In this paper, we construct new multifractal measures, on the Euclidean space $\mathbb{R}^n$, in a similar manner to Hewitt-Stomberg measures but using the class of all $n$-dimensional half-open binary cubes of covering sets in the definition rather than the class of all balls. As an application we shall be concerned with evaluation of Hewitt-Stromberg dimension of cartesian product sets by means of the dimensions of their components.
Neural Network for Pricing and Universal Static Hedging of Contingent Claims

We present here a regress later based Monte Carlo approach that uses neural networks for pricing high-dimensional contingent claims. The choice of specific architecture of the neural networks used in the proposed algorithm provides for interpretability of the model, a feature that is often desirable in the financial context. Specifically, the interpretation leads us to demonstrate that any contingent claim -- possibly high dimensional and path-dependent -- under the Markovian and the no-arbitrage assumptions, can be semi-statically hedged using a portfolio of short maturity options. We show how the method can be used to obtain an upper and lower bound to the true price, where the lower bound is obtained by following a sub-optimal policy, while the upper bound by exploiting the dual formulation. Unlike other duality based upper bounds where one typically has to resort to nested simulation for constructing super-martingales, the martingales in the current approach come at no extra cost, without the need for any sub-simulations. We demonstrate through numerical examples the simplicity and efficiency of the method for both pricing and semi-static hedging of path-dependent options
A strange star scenario for the formation of isolated millisecond pulsars

According to the recycling model, neutron stars in low-mass X-ray binaries were spun up to millisecond pulsars (MSPs), which indicates that all MSPs in the Galactic plane ought to be harbored in binaries. However, about 20% Galactic field MSPs are found to be solitary. To interpret this problem, we assume that the accreting neutron star in binaries may collapse and become a strange star when it reaches some critical mass limit. Mass loss and a weak kick induced by asymmetric collapse during the phase transition (PT) from neutron star to strange star can result in isolated MSPs. In this work, we use a population-synthesis code to examine the PT model. The simulated results show that a kick velocity of ∼60 km s−1 can produce ∼6 × 103 isolated MSPs and birth rate of ∼6.6 × 10−7 yr−1 in the Galaxy, which is approximately in agreement with predictions from observations. For the purpose of comparisons with future observation, we also give the mass distributions of radio and X-ray binary MSPs, along with the delay time distribution.
Learning to Learn Words from Narrated Video

When we travel, we often encounter new scenarios we have never experienced before, with new sights and new words that describe them. We can use our language-learning ability to quickly learn these new words and correlate them with the visual world. In contrast, language models often do not robustly generalize to novel words and compositions. We propose a framework that learns how to learn text representations from visual context. Experiments show that our approach significantly outperforms the state-of-the-art in visual language modeling for acquiring new words and predicting new compositions. Model ablations and visualizations suggest that the visual modality helps our approach more robustly generalize at these tasks. Project webpage is available at https://expert.cs. columbia.edu/