Miracle-dz/UniFAR-FTUs huggingface dataset:
High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow

In this paper, we propose a panorama stitching algorithm based on asymmetric bidirectional optical flow. This algorithm expects multiple photos captured by fisheye lens cameras as input, and then, through the proposed algorithm, these photos can be merged into a high-quality 360-degree spherical panoramic image. For photos taken from a distant perspective, the parallax among them is relatively small, and the obtained panoramic image can be nearly seamless and undistorted. For photos taken from a close perspective or with a relatively large parallax, a seamless though partially distorted panoramic image can also be obtained. Besides, with the help of Graphics Processing Unit (GPU), this algorithm can complete the whole stitching process at a very fast speed: typically, it only takes less than 30s to obtain a panoramic image of 9000-by-4000 pixels, which means our panorama stitching algorithm is of high value in many real-time applications. Our code is available at https://github.com/MungoMeng/Panorama-OpticalFlow.
Detection of gravitational-wave signals from binary neutron star mergers using machine learning

As two neutron stars merge, they emit gravitational waves that can potentially be detected by earth bound detectors. Matched-filtering based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from non-spinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of 10 per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. However, this approach is not yet competitive with traditional matched-filtering based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can not only be applied to machine learning based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches.
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks

Protecting the privacy of input data is of growing importance as machine learning applications reach new application domains. Cloud companies are providing machine learning as a service to make it easier for data holders to create customized training and inference paradigms. In this paper, we provide a unified training and inference framework for large DNNs while protecting input privacy. Our approach called DarKnight relies on cooperative execution between GPUs and trusted execution environment (TEE) to train complex models. The cooperative execution allows DarKnight to exploit the computational power of GPUs to perform linear operations while exploiting TEEs to protect input privacy. In particular, DarKnight uses a novel encoding to linearly combine multiple inputs along with an additive stream cipher noise to obfuscate the inputs. The proposed encoding process allows DarKnight to efficiently decode the computed data even as the model parameters continuously evolve during the backward propagation of DNN training. DarKnight further simplifies the encoding process for inference where the model parameters are unchanged. Unlike prior approaches, DarKnight does not need to store model parameters within the TEE memory thereby getting around the TEE's limited secure memory limitations. By encoding and decoding multiple inputs during each iteration, DarKnight is well suited for the current generation batch training process. We implement DarKnight on an Intel SGX enclave augmented with a GPU to demonstrate our new training capabilities.
Streaming Coresets for Symmetric Tensor Factorization

Factorizing tensors has recently become an important optimization module in a number of machine learning pipelines, especially in latent variable models. We show how to do this efficiently in the streaming setting. Given a set of $n$ vectors, each in $\mathbb{R}^d$, we present algorithms to select a sublinear number of these vectors as coreset, while guaranteeing that the CP decomposition of the $p$-moment tensor of the coreset approximates the corresponding decomposition of the $p$-moment tensor computed from the full data. We introduce two novel algorithmic techniques: online filtering and kernelization. Using these two, we present six algorithms that achieve different tradeoffs of coreset size, update time and working space, beating or matching various state of the art algorithms. In the case of matrices ($2$-ordered tensor), our online row sampling algorithm guarantees $(1 \pm \epsilon)$ relative error spectral approximation. We show applications of our algorithms in learning single topic modeling.
AGE, WEIGHT, BACKFAT AND TIME OF MATING EFFECTS ON PERFORMANCE OF GILTS

An experiment was conducted involving 429 gilts that were managed to stimulate estrus after 140 d of age, bred at first, second or third observed estrus and to study factors relating to estrus and reproductive performance to weaning of the first litter. The average at first estrus followed by the coefficient of variation (%) in parentheses were: age 167.2 d (11.8); weight 106.8 kg (15.5); backfat 19.1 mm (17.8); backfat/weight 0.18 (16.2) and weight/age 0.64 (11.5). Sixty-two and one-half percent of the gilts exhibiting estrus did so between 140 and 170 d of age. Fifty-eight percent of the gilts exhibited estrus prior to 180 d of age and were less than 110 kg liveweight. There were relatively low correlations between indices of growth rate and fatness with age at first estrus. Reproductive performance was not enhanced by delaying breeding to second or third estrus after puberty. Key words: Pigs, reproduction, sexual maturity, age, weight, backfat
Existence of equivalent local martingale deflators in semimartingale market models

This paper offers a systematic investigation on the existence of equivalent local martingale deflators, which are multiplicative special semimartingales, in financial markets given by positive semimartingales. In particular, it shows that the existence of such deflators can be characterized by means of the modified semimartingale characteristics. Several examples illustrate our results. Furthermore, we provide interpretations of the deflators from an economic point of view.
Theory of classical metastability in open quantum systems

We present a general theory of classical metastability in open quantum systems. Metastability is a consequence of a large separation in timescales in the dynamics, leading to the existence of a regime when states of the system appear stationary, before eventual relaxation towards a true stationary state at much larger times. In this work, we focus on the emergence of classical metastability, i.e., when metastable states of an open quantum system with separation of timescales can be approximated as probabilistic mixtures of a finite number of states. We find that a number of classical features follow from this approximation, for both the manifold of metastable states and long-time dynamics between them. Namely, those states are approximately disjoint and thus play the role of metastable phases, and the relaxation towards the stationary state is approximated by a classical stochastic dynamics between them. Importantly, the classical dynamics is observed not only on average, but also at the level of individual quantum trajectories: we show that time coarse-grained continuous measurement records can be viewed as noisy classical trajectories, while their statistics can be approximated by that of the classical dynamics. Among others, this explains how first-order dynamical phase transitions arise from metastability. Finally, in order to verify the presence of classical metastability in a given open quantum system, we develop an efficient numerical approach that delivers the set of metastable phases together with the effective classical dynamics. Since the proximity to a first-order dissipative phase transition manifests as metastability, the theory and tools introduced in this work can be used to investigate such transitions - which occur in the large size limit - through the metastable behavior of many-body systems of moderate sizes accessible to numerics.
Ancient DNA (aDNA) studies of man and microbes: general similarities, specific differences

ThestudyofancientDNA(aDNA)usingmolecularmethodsisanincreasinglyvaluabletacticin bioarchaeology. While this method must be carefully undertaken to ensure that the molecules detected are representative of the ancient sample and not modern contaminates, there is a dangerthata'onesizefitsall'approachtovalidationwillleadtomisinterpretationand/ormissed opportunities of valuable findings. When comparing human and pathogen aDNA, there are many shared technical means that can ensure best practice. However, there are a number of assumptionsthatshouldnotbeusedforbothscenarios.Wediscusstheseaspectsinreference to a recent article published by this journal and highlight some of the latest advances in molecular detection of ancient pathogen DNA that can further improve this endeavour. Copyright 2009 John Wiley & Sons, Ltd.
Vyasa: A High-Performance Vectorizing Compiler for Tensor Convolutions on the Xilinx AI Engine

Xilinx's AI Engine is a recent industry example of energy-efficient vector processing that includes novel support for 2D SIMD datapaths and shuffle interconnection network. The current approach to programming the AI Engine relies on a C/C++ API for vector intrinsics. While an advance over assembly-level programming, it requires the programmer to specify a number of low-level operations based on detailed knowledge of the hardware. To address these challenges, we introduce Vyasa, a new programming system that extends the Halide DSL compiler to automatically generate code for the AI Engine. We evaluated Vyasa on 36 CONV2D workloads, and achieved geometric means of 7.6 and 24.2 MACs/cycle for 32-bit and 16-bit operands (which represent 95.9% and 75.6% of the peak performance respectively).
Surprisal-Triggered Conditional Computation with Neural Networks

Autoregressive neural network models have been used successfully for sequence generation, feature extraction, and hypothesis scoring. This paper presents yet another use for these models: allocating more computation to more difficult inputs. In our model, an autoregressive model is used both to extract features and to predict observations in a stream of input observations. The surprisal of the input, measured as the negative log-likelihood of the current observation according to the autoregressive model, is used as a measure of input difficulty. This in turn determines whether a small, fast network, or a big, slow network, is used. Experiments on two speech recognition tasks show that our model can match the performance of a baseline in which the big network is always used with 15% fewer FLOPs.
MusicID: A Brainwave-Based User Authentication System for Internet of Things

We propose MusicID, an authentication solution for smart devices that uses music-induced brainwave patterns as a behavioral biometric modality. We experimentally evaluate MusicID using data collected from real users whilst they are listening to two forms of music: 1) a popular English song and 2) individual’s favorite song. We show that an accuracy over 98% for user identification and an accuracy over 97% for user verification can be achieved by using data collected from a 4-electrode commodity brainwave headset. We further show that a single electrode is able to provide an accuracy of approximately 85% and the use of two electrodes provides an accuracy of approximately 95%. As already shown by commodity brain-sensing headsets for meditation applications, we believe, including dry electroencephalogram electrodes in smart headsets are feasible and MusicID has the potential of providing an entry point and continuous authentication framework for upcoming surge of smart devices mainly driven by augmented reality (AR)/virtual reality (VR) applications.
Shapley Value as Principled Metric for Structured Network Pruning

Structured pruning is a well-known technique to reduce the storage size and inference cost of neural networks. The usual pruning pipeline consists of ranking the network internal filters and activations with respect to their contributions to the network performance, removing the units with the lowest contribution, and fine-tuning the network to reduce the harm induced by pruning. Recent results showed that random pruning performs on par with other metrics, given enough fine-tuning resources. In this work, we show that this is not true on a low-data regime when fine-tuning is either not possible or not effective. In this case, reducing the harm caused by pruning becomes crucial to retain the performance of the network. First, we analyze the problem of estimating the contribution of hidden units with tools suggested by cooperative game theory and propose Shapley values as a principled ranking metric for this task. We compare with several alternatives proposed in the literature and discuss how Shapley values are theoretically preferable. Finally, we compare all ranking metrics on the challenging scenario of low-data pruning, where we demonstrate how Shapley values outperform other heuristics.
A study of host specificity in the entomopathogenic fungus Beauveria bassiana (Hypocreales, Clavicipitaceae)

Abstract Beauveria bassiana (Balsamo – Crivelli) Vuillemin based mycoinsecticides are used against agricultural, veterinary and medical insect pests. The fungus has a very diverse and extensive host range. Variation in virulence among isolates of B. bassiana to different insect species has been abundantly documented. Given the effect of multiple factors on virulence, it is not certain whether the observed difference in virulence can be labelled as host specificity. Environmental conditions and susceptibility of the insect population are two main factors that affect successful fungal infection. Keeping the environmental factors constant, if virulence of an isolate to different insect species and different populations within an insect species is compared, the scale of difference between the two responses can be estimated. If differences in virulence of an isolate to different insect species are greater than the difference in virulence to different insect populations within an insect species, then, the isolate can be considered as exhibiting specific preference to those insect species towards which it exhibits high virulence. To examine this feature, a worldwide sample of B. bassiana was bioassayed on nine insect species and two different populations within two insect species. Laboratory bioassays were done on: Bombyx mori (Lepidoptera), Spodoptera litura (Lepidoptera), Chilo partellus (Lepidoptera), Helicoverpa armigera (Lepidoptera), Epilachna vigintioctopunctata (Coleoptera), Mylabris pustulata (Coleoptera), Aphis craccivora (Homoptera), Maconellicoccus hirsutus (Hemiptera) and Oecophylla smaragdina (Hymenoptera). The range of variation in virulence of a B. bassiana isolate to different insect species was not more than that observed with different populations within a single insect species. B. bassiana is thus a generalist with no strict host preference. B. bassiana based biopesticide can be used as a broad spectrum insecticide against a myriad of insect pests.
Neural Speaker Diarization with Speaker-Wise Chain Rule

Speaker diarization is an essential step for processing multi-speaker audio. Although an end-to-end neural diarization (EEND) method achieved state-of-the-art performance, it is limited to a fixed number of speakers. In this paper, we solve this fixed number of speaker issue by a novel speaker-wise conditional inference method based on the probabilistic chain rule. In the proposed method, each speaker's speech activity is regarded as a single random variable, and is estimated sequentially conditioned on previously estimated other speakers' speech activities. Similar to other sequence-to-sequence models, the proposed method produces a variable number of speakers with a stop sequence condition. We evaluated the proposed method on multi-speaker audio recordings of a variable number of speakers. Experimental results show that the proposed method can correctly produce diarization results with a variable number of speakers and outperforms the state-of-the-art end-to-end speaker diarization methods in terms of diarization error rate.
Deep Context-Aware Novelty Detection

A common assumption of novelty detection is that the distribution of both "normal" and "novel" data are static. However, this is often not the case in scenarios where data evolves over time, or when the definition of normal and novel depends on contextual information, leading to changes in these distributions. This can lead to significant difficulties when attempting to train a model on datasets where the distribution of normal data in one scenario is similar to that of novel data in another scenario. In this paper we propose a context-aware approach to novelty detection for deep autoencoders. We create a semi-supervised network architecture which utilises auxiliary labels in order to reveal contextual information and allows the model to adapt to a variety of normal and novel scenarios. We evaluate our approach on both synthetic image data and real world audio data displaying these characteristics.
Conjecture about the general cosmological solution of Einstein’s equations

We introduce consideration of a new factor, synchronisation of spacetime Mixmaster oscillations, that may play a simplifying role in understanding the nature of the general inhomogeneous cosmological solution to Einstein's equations. We conjecture that, on approach to a singularity, the interaction of spacetime Mixmaster oscillations in different regions of an inhomogeneous universe can produce a synchronisation of these oscillations through a coupling to their mean field in the way first demonstrated by the Kuramoto coupled oscillator model.
Ant Community Development on Rehabilitated Ash Dams in the South African Highveld

Ant communities have been widely used as indicators of minesite rehabilitation in Australia and are beginning to play a similar role in other parts of the world. Here we examine ant communities on rehabilitated ash dams associated with a coal‐fired power station on the highveld of South Africa, to improve our understanding of ecosystem development on these substrates. Ants were sampled using pitfall traps at 11 ash‐dam sites, ranging from unrehabilitated to 9‐year‐old rehabilitated sites, as well as two adjacent natural grassland sites. Sampling was conducted on 12 occasions from March 1997 to January 1999. Forty‐nine ant species from 19 genera were recorded during the study. Site species richness was positively correlated with rehabilitation age, ranging from 10 to 25 at ash‐dam sites, compared with 28 and 34 at the two natural grassland sites. There was a humped relationship between total ant abundance and rehabilitation age, with abundance peaking after 5–7 years at levels far higher than those at natural sites. Ordination analysis showed clear separation between ash‐dam and natural sites along the first axis. The unrehabilitated ash‐dam site was also separated from rehabilitated sites along the first axis. Sites of different rehabilitation age were separated along the second axis. Individual ant species showed clear successional patterns across the rehabilitation gradient. Although there was a clear successional trend for the development of ant communities on rehabilitated ash dams, this trend was not toward natural grassland. The lack of convergence toward ant communities of natural grasslands reflects the markedly different substrate and plant composition on ash dams and supports the widely held view that restoration of natural grassland communities is not a realistic goal of ash‐dam rehabilitation. However, the development of species‐rich ant communities, containing at least some late‐successional species, indicates the potential for rehabilitated ash dams to support diverse and complex ecosystems.
A Kantian Solution for the Freedom of Choice Loophole in Bell Experiments

Bell's theorem is based on three assumptions: realism, locality, and measurement independence. The third assumption is identified by Bell as linked to the freedom of choice hypothesis. He holds that ultimately the human free will can ensure the measurement independence assumption. The incomplete experimental conditions for supporting this third assumption are known in the literature as "freedom-of-choice loophole" (FOCL). In a recent publication, Abellan et al [2018] address this problem and follow this same strategy embraced by Bell [2004]. Nevertheless, the possibility of human freedom of choice has been a matter of philosophical debate for more than 2000 years, and there is no consensus among philosophers on this topic. If human choice is not free, Bell's solution would not be sufficient to close FOCL. Therefore, in order to support the basic assumption of this experiment, it is necessary to argue that human choice is indeed free. In this paper, we present a Kantian position on this topic and defend the view that this philosophical position is the best way to ensure that BigBell Test (Abellan et al. [2018]) can in fact close the loophole.
Order Indices and Entanglement Production in Quantum Systems

The review is devoted to two important quantities characterizing many-body systems, order indices and the measure of entanglement production. Order indices describe the type of order distinguishing statistical systems. Contrary to the order parameters characterizing systems in the thermodynamic limit and describing long-range order, the order indices are applicable to finite systems and classify all types of orders, including long-range, mid-range, and short-range orders. The measure of entanglement production quantifies the amount of entanglement produced in a many-partite system by a quantum operation. Despite that the notions of order indices and entanglement production seem to be quite different, there is an intimate relation between them, which is emphasized in the review.
Reserve Size, Local Human Density, and Mammalian Extinctions in U.S. Protected Areas

Abstract: We examined quantitatively the interaction of reserve size and surrounding local human density in the United States and their relative effect on extinction of large mammals in 13 national parks of the western United States. Data on reserve size and human density were obtained from publicly available sources. Local human density was calculated as the mean density in the 50‐ or 100‐km zone surrounding the reserves' borders. Reliable extinction data are extraordinarily hard to find. Using a variety of definitions of extinct, we collated information on extinctions of large mammals (>5 kg) that spanned the size of U.S. national parks as a whole. Human density surrounding reserves varies considerably. Overall, small reserves were in areas of higher human density than were large reserves (p < 0.0001, r 2 = −0.24, n = 864; excluding Hawaii), and many of the small reserves were at higher local density than the mean for the contiguous United States. Extinction rates of large mammals correlate significantly with local human density, but not with park area. These findings together emphasize that (1) processes occurring outside of a reserve's boundary may unexpectedly strongly affect species within the reserve; (2) small reserves might suffer the double jeopardy of not only their size but also their situation in especially adverse surrounds; and thus (3) small reserves might suffer more intense edge effects and be more isolated than large reserves. If so, conservation workers need to incorporate the relationship into their models and management decisions.