Miracle-dz/UniFAR-FTUs huggingface dataset:
Crowdfunding for Design Innovation: Prediction Model With Critical Factors

Online reward-based crowdfunding campaigns have emerged as an innovative approach for validating demands, discovering early adopters, and seeking learning and feedback in the design processes of innovative products. However, crowdfunding campaigns for innovative products are faced with a high degree of uncertainty and suffer meager rates of success to fulfill their values for design. To guide designers and innovators for crowdfunding campaigns, this article presents a data-driven methodology to build a prediction model with critical factors for crowdfunding success, based on public online crowdfunding campaign data. Specifically, the methodology filters 26 candidate factors in the real-win-worth framework and identifies the critical ones via stepwise regression to predict the amount of crowdfunding. We demonstrate the methods via deriving prediction models and identifying essential factors from three-dimensional printer and smartwatch campaign data on Kickstarter and Indiegogo. The critical factors can guide campaign developments, and the prediction model may evaluate crowdfunding potential of innovations in contexts, to increase the chance of crowdfunding success of innovative products.
Persistent Neurons

Most algorithms used in neural networks(NN)-based leaning tasks are strongly affected by the choices of initialization. Good initialization can avoid sub-optimal solutions and alleviate saturation during training. However, designing improved initialization strategies is a difficult task and our understanding of good initialization is still very primitive. Here, we propose persistent neurons, a strategy that optimizes the learning trajectory using information from previous converged solutions. More precisely, we let the parameters explore new landscapes by penalizing the model from converging to the previous solutions under the same initialization. Specifically, we show that persistent neurons, under certain data distribution, is able to converge to more optimal solutions while initializations under popular framework find bad local minima. We further demonstrate that persistent neurons helps improve the model's performance under both good and poor initializations. Moreover, we evaluate full and partial persistent model and show it can be used to boost the performance on a range of NN structures, such as AlexNet and residual neural network. Saturation of activation functions during persistent training is also studied.
On differences between consecutive numbers coprime to primorials

We consider the ordered sequence of coprimes to a given primorial number and investigate differences between consecutive elements. The Jacobsthal function applied to the concerning primorial turns out to represent the greatest of these differences. We will explore the smallest even number which does not occur as such a difference. Little is known about even natural numbers below the respective Jacobsthal function which cannot be represented as a difference between consecutive numbers coprime to a primorial. Existence and frequency of these numbers have not yet been clarified. Using the relation between restricted coverings of sequences of consecutive integers and the occuring differences, we derive a bound below which all even natural numbers are differences between consecutive numbers coprime to a given primorial $p_k\#$. Furthermore, we provide exhaustive computational results on non-existent differences for primes $p_k$ up to $k=44$. The data suggest the assumption that all even natural numbers up to $h(k-1)$ occur as differences of coprimes to $p_k\#$ where $h(n)$ is the Jacobsthal function applied to $p_n\#$.
Multiagent Low-Dimensional Linear Bandits

We study a multiagent stochastic linear bandit with side information, parameterized by an unknown vector <inline-formula><tex-math notation="LaTeX">$\theta ^* \in \mathbb {R}^{d}$</tex-math></inline-formula>. The side information consists of a finite collection of low-dimensional subspaces, one of which contains <inline-formula><tex-math notation="LaTeX">$\theta ^*$</tex-math></inline-formula>. In our setting, agents can collaborate to reduce regret by sending recommendations across a communication graph connecting them. We present a novel decentralized algorithm, where agents communicate subspace indices with each other and each agent plays a projected variant of LinUCB on the corresponding (low dimensional) subspace. By distributing the search for the optimal subspace across users and learning of the unknown vector by each agent in the corresponding low-dimensional subspace, we show that the per-agent finite-time regret is much smaller than the case when agents do not communicate. We finally complement these results through simulations.
Learning to Prune in Training via Dynamic Channel Propagation

In this paper, we propose a novel network training mechanism called “dynamic channel propagation” to prune the neural networks during the training period. In particular, we pick up a specific group of channels in each convolutional layer to participate in the forward propagation in training time according to the significance level of channel, which is defined as channel utility. The utility values with respect to all selected channels are updated simultaneously with the error back-propagation process and will adaptively change. Furthermore, when the training ends, channels with high utility values are retained whereas those with low utility values are discarded. Hence, our proposed scheme trains and prunes neural networks simultaneously. We empirically evaluate our novel training scheme on various representative benchmark datasets and advanced convolutional neural network (CNN) architectures, including VGGNet and ResNet. The experiment results verify the superior performance and robust effectiveness of our approach11Our code is available at https://github.com/shibo-shen/Dynamic-Channel-Propagation.
Caltech-NRAO Stripe 82 Survey (CNSS). IV. The Birth of Radio-loud Quasar 013815+00

It is believed that the gas accretion onto supermassive black holes is the main process of powering this quasar’s luminous emission, which occurs in optical, UV, and X-ray regimes and less frequently in radio waves. The observational fact that only a few percent of quasars are radio-loud is still an unresolved issue concerning the understanding of the active galactic nucleus (AGN) population. Here we present a detection of a rapid transition from the radio-quiet to the radio-loud mode in quasar 013815+00 (z = 0.94) which coincides with changes of its UV–optical continuum and the low ionization Mg ii broadline. We interpret this as an enhancement of accretion onto a central black hole of about 109 solar masses. As a consequence a new radio-loud AGN was born. Its spectral and morphological properties indicate that it went through the short gigahertz-peaked spectrum phase at the beginning of its activity and has now stabilized its flux density at the level of a few millijansky. The radio morphology of 013815+00 is very compact and we predict that with such short-term jet activity its development will be very slow. The observed luminosity changes of the accretion disk are shorter than the lifetime of the new radio phase in 013815+00.
Domain Adaptation Without Source Data

Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such an assumption is rarely plausible in the real world and possibly causes data privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. To avoid accessing source data that could contain sensitive information, we introduce source data free domain adaptation (SFDA). Our key idea is to leverage a pretrained model from the source domain and progressively update the target model in a self-learning manner. We observe that target samples with lower self-entropy measured by the pretrained source model are more likely to be classified correctly. From this, we select the reliable samples with the self-entropy criterion and define these as class prototypes. We then assign pseudolabels for every target sample based on the similarity score with class prototypes. We further propose point-to-set distance-based filtering, which does not require any tunable hyperparameters to reduce uncertainty from the pseudolabeling process. Finally, we train the target model with the filtered pseudolabels with regularization from the pretrained source model. Surprisingly, without direct usage of labeled source samples, our SFDA outperforms conventional domain adaptation methods on benchmark datasets. Impact Statement—This study addresses the data privacy issue, especially in unsupervised domain adaptation. Based on our privacy-preserving domain adaptation, various stakeholders, including enterprises and government organizations, can be free of concern about privacy issues with their labeled source dataset. Furthermore, the proposed data-free approach can contribute to creating a positive social impact, especially in large-scale datasets. Recently, since the size of data across various fields has been scaling up, it is almost incapable for individual researchers to directly utilize a large scale of data during training. For this reason, a new social trend of sharing pretrained models, e.g., EfficientNet and BERT, led by global enterprises with their huge amount of resources has been rising up. From this viewpoint, our approach thus enables more people to participate in the domain adaptation field specifically when the source data are large scale and contain sensitive attributes.
A Bayesian Multilingual Document Model for Zero-shot Topic Identification and Discovery

In this paper, we present a Bayesian multilingual document model for learning language-independent document embeddings. The model is an extension of BaySMM [Kesiraju et al 2020] to the multilingual scenario. It learns to represent the document embeddings in the form of Gaussian distributions, thereby encoding the uncertainty in its covariance. We propagate the learned uncertainties through linear classifiers that benefit zero-shot cross-lingual topic identification. Our experiments on 17 languages show that the proposed multilingual Bayesian document model performs competitively, when compared to other systems based on large-scale neural networks (LASER, XLM-R, mUSE) on 8 high-resource languages, and outperforms these systems on 9 mid-resource languages. We revisit cross-lingual topic identification in zero-shot settings by taking a deeper dive into current datasets, baseline systems and the languages covered. We identify shortcomings in the existing evaluation protocol (MLDoc dataset), and propose a robust alternative scheme, while also extending the cross-lingual experimental setup to 17 languages. Finally, we consolidate the observations from all our experiments, and discuss points that can potentially benefit the future research works in applications relying on cross-lingual transfers.
Impact of internal-delensing biases on searches for primordial 𝖡-modes of CMB polarisation

Searches for the imprint of primordial gravitational waves in degree-scale CMB B-mode polarisation data must account for significant contamination from gravitational lensing. Fortunately, the lensing effects can be partially removed by combining high-resolution E-mode measurements with an estimate of the projected matter distribution. In the near future, experimental characteristics will be such that the latter can be reconstructed internally with high fidelity from the observed CMB, with the EB quadratic estimator providing a large fraction of the signal-to-noise. It is a well-known phenomenon in this context that any overlap in modes between the B-field to be delensed and the B-field from which the reconstruction is derived leads to a suppression of delensed power going beyond that which can be attributed to a mitigation of the lensing effects. More importantly, the variance associated with this spectrum is also reduced, posing the question of whether the additional power suppression could help better constrain the tensor-to-scalar ratio, r. In this paper, we show this is not the case, as suggested but not quantified in previous work. We develop an analytic model for the biased delensed B-mode angular power spectrum, which suggests a simple renormalisation prescription to avoid bias on the inferred tensor-to-scalar ratio. With this approach, we learn that the bias necessarily leads to a degradation of the signal-to-noise on a primordial component compared to “unbiased delensing”. Next, we assess the impact of removing from the lensing reconstruction any overlapping B-modes on our ability to constrain r, showing that it is in general advantageous to do this rather than modelling or renormalising the bias. Finally, we verify these results within a maximum-likelihood inference framework applied to simulations.
Multi-Label Image Recognition with Multi-Class Attentional Regions

Multi-label image recognition is a practical and challenging task compared to single-label image classification. However, previous works may be suboptimal because of a great number of object proposals or complex attentional region generation modules. In this paper, we propose a simple but efficient two-stream framework to recognize multi-category objects from global image to local regions, similar to how human beings perceive objects. To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible. Our method can efficiently and effectively recognize multi-class objects with an affordable computation cost and a parameter-free region localization module. Over three benchmarks on multi-label image classification, we create new state-of-the-art results with a single model only using image semantics without label dependency. In addition, the effectiveness of the proposed method is extensively demonstrated under different factors such as global pooling strategy, input size and network architecture.
Pulsating chromosphere of classical Cepheids

1 Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, France, email : vincent.hocde@oca.eu 2 LESIA (UMR 8109), Observatoire de Paris, PSL, CNRS, UPMC, Univ. Paris-Diderot, 5 place Jules Janssen, 92195 Meudon, France 3 European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching, Germany 4 Smithsonian Astrophysical Observatory, MS 4, 60 Garden St. Cambridge MA 02138 5 Observatoire de Haute-Provence – CNRS/PYTHEAS/Université d’Aix-Marseille, 04870 Saint-Michel l’Observatoire, France 6 IRAP, Université de Toulouse, CNRS, CNES, UPS. 14 Av. E. Belin, 31400 Toulouse, France
Approximate quantum circuit synthesis using block encodings

Author(s): Camps, Daan; Beeumen, Roel Van | Abstract: One of the challenges in quantum computing is the synthesis of unitary operators into quantum circuits with polylogarithmic gate complexity. Exact synthesis of generic unitaries requires an exponential number of gates in general. We propose a novel approximate quantum circuit synthesis technique by relaxing the unitary constraints and interchanging them for ancilla qubits via block-encodings. This approach combines smaller block-encodings, which are easier to synthesize, into quantum circuits for larger operators. Due to the use of block-encodings, our technique is not limited to unitary operators and can also be applied for the synthesis of arbitrary operators. We show that operators which can be approximated by a canonical polyadic expression can be synthesized with polylogarithmic gate complexity under certain assumptions.
QED at NNLO with McMule

McMule is a framework for fully differential higher-order QED calculations of scattering and decay processes involving leptons. It keeps finite lepton masses, which regularises collinear singularities. Soft singularities are treated with dimensional regularisation and using FKS^\ellℓ subtraction. We describe the implementation of the framework in Fortran 95, list the processes that are currently implemented, and give instructions on how to run the code. In addition, we present new phenomenological results for muon-electron scattering and lepton-proton scattering, including the dominant NNLO corrections. While the applications presented focus on MUonE, MUSE, and P2, the code can be used for a large number of planned and running experiments.
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks

This paper proposes a new mean-field framework for over-parameterized deep neural networks (DNNs), which can be used to analyze neural network training. In this framework, a DNN is represented by probability measures and functions over its features (that is, the function values of the hidden units over the training data) in the continuous limit, instead of the neural network parameters as most existing studies have done. This new representation overcomes the degenerate situation where all the hidden units essentially have only one meaningful hidden unit in each middle layer, and further leads to a simpler representation of DNNs, for which the training objective can be reformulated as a convex optimization problem via suitable re-parameterization. Moreover, we construct a non-linear dynamics called neural feature flow, which captures the evolution of an over-parameterized DNN trained by Gradient Descent. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures. Furthermore, we show, for Res-Net, when the neural feature flow process converges, it reaches a global minimal solution under suitable conditions. Our analysis leads to the first global convergence proof for over-parameterized neural network training with more than $3$ layers in the mean-field regime.
AmpC Beta Lactamases Among ESBL Producing Escherichia Coli And Klebsiella Pneumoniae- If You Don’t Look, You Won’t Find

Amp C beta lactamases confer resistance to a wide variety of beta lactams and pose both diagnostic and therapeutic challenges, as their presence goes undetected in the presence of ESBLs. We evaluated 52 ESBL positive clinical isolates of E.coli (n=41) and K.pneumoniae (n=11) for AmpC production by a phenyl boronic acid disc (PBA) and the 3 dimensional enzyme test (3DET). The PBA method detected 24 (58.5%) and 9 (82%)E.coli and K.pneumoniae isolates as AmpC positive, while detection by the 3DET increased the numbers to 29 (70.7%) and 10 (91%) isolates, respectively. We conclude that a large number (75%) of ESBL producers are also found to produce AmpC and that the PBA disc method is a very useful and reliable method for routine use in laboratories.
Learn Faster and Forget Slower via Fast and Stable Task Adaptation

Training Deep Neural Networks (DNNs) is still highly time-consuming and compute-intensive. It has been shown that adapting a pretrained model may significantly accelerate this process. With a focus on classification, we show that current fine-tuning techniques make the pretrained models catastrophically forget the transferred knowledge even before anything about the new task is learned. Such rapid knowledge loss undermines the merits of transfer learning and may result in a much slower convergence rate compared to when the maximum amount of knowledge is exploited. We investigate the source of this problem from different perspectives and to alleviate it, introduce Fast And Stable Task-adaptation (FAST), an easy to apply fine-tuning algorithm. The paper provides a novel geometric perspective on how the loss landscape of source and target tasks are linked in different transfer learning strategies. We empirically show that compared to prevailing fine-tuning practices, FAST learns the target task faster and forgets the source task slower. The code is available at this https URL.
Quasinormal modes of black holes with a scalar hair in Einstein-Maxwell-dilaton theory

We compute the quasinormal frequencies for scalar perturbations of hairy black holes in four-dimensional Einstein-Maxwell-dilaton theory assuming a non-trivial scalar potential for the dilaton field. We investigate the impact on the spectrum of the angular degree, the overtone number, the charges of the black hole as well as the magnitude of the scalar potential. All modes are found to be stable. Our numerical results are summarized in tables, and for better visualization, we show them graphically as well.
Infectivity and Effectiveness of Five Endomycorrhizal Fungi: Competition with Indigenous Fungi in Field Soils

Five species of endomycorrhizal fungi differed in their ability to stimulate phosphorus uptake and growth of subterranean clover when inoculated into two untreated field soils which were phosphate deficient. Each soil contained a different indigenous endomycorrhizal fungus. The amount of roots converted to mycorrhizas by each indigenous and inoculant fungus was estimated by using differences in the morphology of the infection within the roots and a line-intercept method. Effectiveness of the inoculant fungi in increasing plant growth was related to the infectivity of the fungi from the inocula used. The fungi indigenous to the two soils used did not appear to affect the extent to which mycorrhizas were formed by any of the inoculant fungi.
Zooplankton population in the river Tigris: effects of Samarra impoundment

The influences of the Samarra impoundment on the ecology of the downstream sector of the river Tigris are investigated. Zooplankton were collected monthly from July 1987 to July 1988. It was found that zooplankton were most abundant during high river discharges in March. During the month of low river discharge, September, the zooplankton population was greatly reduced immediately below the barrage and remained so to the last downstream station. Several factors seem to explain such differences. The high discharge from the impoundment was found to be the dominating factor and may flush the small backwaters in which zooplankton were abundant, thus increasing the population in the river. Zooplankton species vary in their ability to sustain populations in the river, variations which are due mainly to species-specific characteristics.
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography

At x-ray beamlines of synchrotron light sources, the achievable time-resolution for 3D tomographic imaging of the interior of an object has been reduced to a fraction of a second, enabling rapidly changing structures to be examined. The associated data acquisition rates require sizable computational resources for reconstruction. Therefore, full 3D reconstruction of the object is usually performed after the scan has completed. Quasi-3D reconstruction—where several interactive 2D slices are computed instead of a 3D volume—has been shown to be significantly more efficient, and can enable the real-time reconstruction and visualization of the interior. However, quasi-3D reconstruction relies on filtered backprojection type algorithms, which are typically sensitive to measurement noise. To overcome this issue, we propose Noise2Filter, a learned filter method that can be trained using only the measured data, and does not require any additional training data. This method combines quasi-3D reconstruction, learned filters, and self-supervised learning to derive a tomographic reconstruction method that can be trained in under a minute and evaluated in real-time. We show limited loss of accuracy compared to training with additional training data, and improved accuracy compared to standard filter-based methods.