Miracle-dz/UniFAR-FTUs huggingface dataset:
Pruning of Deep Spiking Neural Networks through Gradient Rewiring

Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are usually resource-constrained, the compression of SNNs is thus crucial along the road of practical use of SNNs. Most existing methods directly apply pruning approaches in artificial neural networks (ANNs) to SNNs, which ignore the difference between ANNs and SNNs, thus limiting the performance of the pruned SNNs. Besides, these methods are only suitable for shallow SNNs. In this paper, inspired by synaptogenesis and synapse elimination in the neural system, we propose gradient rewiring (Grad R), a joint learning algorithm of connectivity and weight for SNNs, that enables us to seamlessly optimize network structure without retraining. Our key innovation is to redefine the gradient to a new synaptic parameter, allowing better exploration of network structures by taking full advantage of the competition between pruning and regrowth of connections. The experimental results show that the proposed method achieves minimal loss of SNNs' performance on MNIST and CIFAR-10 datasets so far. Moreover, it reaches a ~3.5% accuracy loss under unprecedented 0.73% connectivity, which reveals remarkable structure refining capability in SNNs. Our work suggests that there exists extremely high redundancy in deep SNNs. Our codes are available at https://github.com/Yanqi-Chen/Gradient-Rewiring.
HuMoR: 3D Human Motion Model for Robust Pose Estimation

We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape. Though substantial progress has been made in estimating 3D human motion and shape from dynamic observations, recovering plausible pose sequences in the presence of noise and occlusions remains a challenge. For this purpose, we propose an expressive generative model in the form of a conditional variational autoencoder, which learns a distribution of the change in pose at each step of a motion sequence. Furthermore, we introduce a flexible optimization-based approach that leverages HuMoR as a motion prior to robustly estimate plausible pose and shape from ambiguous observations. Through extensive evaluations, we demonstrate that our model generalizes to diverse motions and body shapes after training on a large motion capture dataset, and enables motion reconstruction from multiple input modalities including 3D keypoints and RGB(-D) videos. See the project page at geometry.stanford. edu/projects/humor.
Graphical functions in even dimensions

Graphical functions are special position space Feynman integrals, which can be used to calculate Feynman periods and one- or two-scale processes at high loop orders. With graphical functions, renormalization constants have been calculated to loop orders seven and eight in four-dimensional φ 4 theory and to order ﬁve in six-dimensional φ 3 theory. In this article we present the theory of graphical functions in even dimensions ≥ 4 with detailed reviews of known properties and full proofs whenever possible.
Discovery of nonlinear dynamical systems using a Runge–Kutta inspired dictionary-based sparse regression approach

In this work, we blend machine learning and dictionary-based learning with numerical analysis tools to discover differential equations from noisy and sparsely sampled measurement data of time-dependent processes. We use the fact that given a dictionary containing large candidate nonlinear functions, dynamical models can often be described by a few appropriately chosen basis functions. As a result, we obtain parsimonious models that can be better interpreted by practitioners, and potentially generalize better beyond the sampling regime than black-box modelling. In this work, we integrate a numerical integration framework with dictionary learning that yields differential equations without requiring or approximating derivative information at any stage. Hence, it is utterly effective for corrupted and sparsely sampled data. We discuss its extension to governing equations, containing rational nonlinearities that typically appear in biological networks. Moreover, we generalized the method to governing equations subject to parameter variations and externally controlled inputs. We demonstrate the efficiency of the method to discover a number of diverse differential equations using noisy measurements, including a model describing neural dynamics, chaotic Lorenz model, Michaelis–Menten kinetics and a parameterized Hopf normal form.
Continuous Tur\'an numbers

One of the most famous open problems in combinatorics is the Zarankiewicz problem, which asks for the maximum number of ones in an n × n matrix that has no s × t submatrix of all ones. The Kővari-Sós-Turán theorem provides an upper bound of O(s 1 t n 1 t ) for this problem for fixed t ≥ 2, which is known to be sharp in some cases. The Zarankiewicz problem is a subproblem of the more general problem of determining the maximum number of ones ex(n,M) in an n× n 0-1 matrix that avoids the forbidden 0-1 matrix M . In this paper, we define a notion of containment and avoidance for subsets of R. Then we introduce a new, continuous and super-additive extremal function for subsets P ⊆ R called opx(n, P ), which is the supremum of μ2(S) over all open P -free subsets S ⊆ [0, n], where μ2(S) denotes the Lebesgue measure of S in R. We show that opx(n, P ) fully encompasses ex(n,M) up to a constant factor. More specifically, we define a natural correspondence between finite subsets P ⊆ R and 0-1 matrices MP , and we prove that opx(n, P ) = Θ(ex(n,MP )) for all finite subsets P ⊆ R, where the constants in the bounds depend only on the distances between the points in P . We also discuss bounded infinite subsets P for which opx(n, P ) grows faster than ex(n,M) for all fixed 0-1 matricesM . In particular, we show that opx(n, P ) = Θ(n) for any open subset P ⊆ R . We prove an even stronger result, that if QP is the set of points with rational coordinates in any open subset P ⊆ R, then opx(n,QP ) = Θ(n). Finally, we obtain a strengthening of the Kővari-Sós-Turán theorem that applies to infinite subsets of R. Specifically, for subsets Ps,t,c ⊆ R consisting of t horizontal line segments of length s with left endpoints on the same vertical line with consecutive segments a distance of c apart, we prove that opx(n, Ps,t,c) = O(s 1 t n 1 t ), where the constant in the bound depends on t and c. When t = 2, we show that this bound is sharp up to a constant factor that depends on c. We also extend opx(n, P ) to any number of dimensions, and we generalize most of our results including the strengthening of the Kővari-Sós-Turán theorem to any number of dimensions.
Sumsets and fixed points of substitutions

<p>In this paper we introduce a technique to determine the sumset <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper A plus upper A"> <mml:semantics> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>+</mml:mo> <mml:mi>A</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">A+A</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, where <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper A"> <mml:semantics> <mml:mi>A</mml:mi> <mml:annotation encoding="application/x-tex">A</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is the indicator function of the 0’s occurring in a fixed point <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="x"> <mml:semantics> <mml:mi>x</mml:mi> <mml:annotation encoding="application/x-tex">x</mml:annotation> </mml:semantics> </mml:math> </inline-formula> of a substitution on the alphabet <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="StartSet 0 comma 1 EndSet"> <mml:semantics> <mml:mrow> <mml:mo fence="false" stretchy="false">{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo fence="false" stretchy="false">}</mml:mo> </mml:mrow> <mml:annotation encoding="application/x-tex">\{0,1\}</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. </p>
Diffusion Models Beat GANs on Image Synthesis

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\times$128, 4.59 on ImageNet 256$\times$256, and 7.72 on ImageNet 512$\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.94 on ImageNet 256$\times$256 and 3.85 on ImageNet 512$\times$512. We release our code at https://github.com/openai/guided-diffusion
Chaotic switching in driven-dissipative Bose-Hubbard dimers: when a flip bifurcation meets a T-point in $R^4$

The Bose--Hubbard dimer model is a celebrated fundamental quantum mechanical model that accounts for the dynamics of bosons at two interacting sites. It has been realized experimentally by two coupled, driven and lossy photonic crystal nanocavities, which are optical devices that operate with only a few hundred photons due to their extremely small size. Our work focuses on characterizing the different dynamics that arise in the semiclassical approximation of such driven-dissipative photonic Bose--Hubbard dimers. Mathematically, this system is a four-dimensional autonomous vector field that describes two specific coupled oscillators, where both the amplitude and the phase are important. We perform a bifurcation analysis of this system to identify regions of different behavior as the pump power $f$ and the detuning $\delta$ of the driving signal are varied, for the case of fixed positive coupling. The bifurcation diagram in the $(f,\delta)$-plane is organized by two points of codimension-two bifurcations -- a $Z_2$-equivariant homoclinic flip bifurcation and a Bykov T-point -- and provides a roadmap for the observable dynamics, including different types of chaotic behavior. To illustrate the overall structure and different accumulation processes of bifurcation curves and associated regions, our bifurcation analysis is complemented by the computation of kneading invariants and of maximum Lyapunov exponents in the $(f,\delta)$-plane. The bifurcation diagram displays a menagerie of dynamical behavior and offers insights into the theory of global bifurcations in a four-dimensional phase space, including novel bifurcation phenomena such as degenerate singular heteroclinic cycles.
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

Multi-modal learning, which focuses on utilizing various modalities to improve the performance of a model, is widely used in video recognition. While traditional multi-modal learning offers excellent recognition results, its computational expense limits its impact for many real-world applications. In this paper, we propose an adaptive multi-modal learning framework, called AdaMML, that selects on-the-fly the optimal modalities for each segment conditioned on the input for efficient video recognition. Specifically, given a video segment, a multi-modal policy net-work is used to decide what modalities should be used for processing by the recognition model, with the goal of improving both accuracy and efficiency. We efficiently train the policy network jointly with the recognition model using standard back-propagation. Extensive experiments on four challenging diverse datasets demonstrate that our proposed adaptive approach yields 35% − 55% reduction in computation when compared to the traditional baseline that simply uses all the modalities irrespective of the in-put, while also achieving consistent improvements in accuracy over the state-of-the-art methods. Project page: https://rpand002.github. io/adamml.html.
Operation Embeddings for Neural Architecture Search

Neural Architecture Search (NAS) has recently gained increased attention, as a class of approaches that automatically searches in an input space of network architectures. A crucial part of the NAS pipeline is the encoding of the architecture that consists of the applied computational blocks, namely the operations and the links between them. Most of the existing approaches either fail to capture the structural properties of the architectures or use a hand-engineered vector to encode the operator information. In this paper, we propose the replacement of fixed operator encoding with learnable representations in the optimization process. This approach, which effectively captures the relations of different operations, leads to smoother and more accurate representations of the architectures and consequently to improved performance of the end task. Our extensive evaluation in ENAS benchmark demonstrates the effectiveness of the proposed operation embeddings to the generation of highly accurate models, achieving state-of-the-art performance. Finally, our method produces top-performing architectures that share similar operation and graph patterns, highlighting a strong correlation between architecture’s structural properties and performance.
Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs

—Consider a truck ﬁlled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action. Most high-ﬁdelity robotic simulators like ours are time-consuming. Therefore, we investigate the above learning problem with a focus on minimizing the number of simulation runs required. The optimal decision-making problem under this setting can be formulated as a multi-class classiﬁcation problem. However, to obtain the outcome of any action requires us to run the time-consuming simulator, thereby restricting the amount of training data that can be collected. Thus, we need a data-efﬁcient approach to learn the classiﬁer and generalize it with a minimal amount of data. A high-ﬁdelity physics-based simulator is common in general for complex manipulation tasks involving multi-body interactions. To this end, we train an optimal decision tree as the classiﬁer, and for each branch of the decision tree, we reason about the conﬁdence in the decision using a Probably Approximately Correct (PAC) framework to determine whether more simulator data will help reach a certain conﬁdence level. This provides us with a mechanism to evaluate when simulation can be avoided for certain decisions, and when simulation will improve the decision making. For the truck unloading problem, our experiments show that a signiﬁcant reduction in simulator runs can be achieved using the proposed method as compared to naively running the simulator to collect data to train equally performing decision trees.
Does listed real estate behave like direct real estate? Updated and broader evidence

ABSTRACT This study investigates a question relevant to many investors: do the broad Real Estate Investment Trust (REIT) return characteristics reflect those of the broad direct real estate markets. The paper makes several contributions to the literature in addition to using more recent data: (1) we use data for six countries (Australia, France, Germany, Netherlands, the U.K., and the U.S.); (2) we estimate both country-specific and panel models to increase the reliability and generalizability of the analysis; (3) we estimate a structural vector autoregressive model to be able to better and more reliably interpret the various shocks in the system; and (4) we investigate the effects of global liquidity shocks, among other shocks. Our results indicate that over the mid to long horizon, broad REIT and direct returns have similar characteristics and are highly correlated at the panel level. Also, the two types of exposure to real estate exhibit similar reactions to economic shocks. Thus, the paper makes a case that investors do not necessarily need to worry much about compositional effects when aiming to track broad international direct market performance by investing in listed real estate.
Adoption level of wheat seed production technologies in north-west zone and south-west zone of Haryana

The present study was carried out in Sirsa and Hisar districts of Haryana to find out the adoption level of wheat seed production technologies. In total 120 respondents, viz. 60 wheat seed producing farmers from each district and 30 farmers from each block were selected for the study. Empirical data were collected personally through a well-structured and pre-tested interview schedule and analyzed with the help of appropriate statistical tools using 26th version of Statistical Package for Social Sciences (SPSS). Findings revealed that more than half of the respondents (56.67%) had medium level of overall adoption level regarding wheat seed production technology. In case of practise-wise adoption, the adoption level was high about field preparation, recommended varieties, seed standards and field standards, while spacing and sowing time were less adopted practices. Study also concluded that independent variables like age, education, socio-economic status, land holding, extension contact, mass media exposure, change proneness, risk orientation, scientific orientation and seed production experience were positively and significantly correlated with adoption level.
The Parenting Premmies Support Program: Designing and developing a mobile health intervention for mothers of preterm infants.

Abstract This article describes the development of the Parenting Premmies Support Program, a mobile health intervention designed to support mothers during the transitional time following their preterm infant’s discharge from hospital. Examples of how research teams give voice to the target population throughout the development and design of mHealth programs is largely missing from the literature. A detailed description of the steps taken in the development of the mHealth intervention that formed the support program is the intention of this paper. An exploratory, sequential, mixed-methods approach with a three-phase design was conducted. In each phase, the experience and perspectives of mothers of preterm infants were acknowledged and included. Phase one sought women’s accounts of their experience collected in semi-structured interviews (n = 9) and subject to a descriptive content analysis. In the second phase, a collaborative, stakeholder interrogation of issues was conducted to develop content of the mHealth protocol. In this phase, two interdependent procedures were used with two participant panels; a stakeholder panel (n = 10) undertook a series of face-to-face meetings, and a user group panel (n = 18) of women who had birthed a preterm infant up to 12 months before undertook an online Delphi survey. In phase three a pilot implementation of the program was undertaken with women whose preterm infants were being discharged home from hospital. The outcome was an mHealth protocol, a resource designed to support women by giving them information to understand and normalise their experience with their preterm infants, and to help them make decisions which may enhance responsive mothering. Collaborative research integrating user feedback in partnership with experts in the field increases the likelihood the final product will be of value and prove supportive and useful to the target audience.
Effect of deficit irrigation on vine performance and grape composition of Vitis vinifera L. cv. Muscat of Alexandria

Background and Aims Previous research has investigated widely the effects of irrigation application in red winegrape cultivars; however, white grape cultivars have been the subject of a lesser number of field trials. An experiment was undertaken for three seasons in a drip-irrigated, cv. Muscat of Alexandria/161-49C vineyard to define the most sustainable irrigation strategy for white winegrapes in eastern Spain. Methods and Results The treatments were as follows: (i) Control, irrigated at 100% of estimated crop evapotranspiration (ETc) for the entire season; (ii) sustained deficit irrigation (SDI), irrigated at 50% of Control; (iii) early deficit (ED), where pre-veraison irrigation was withheld, followed by 100% ETc; and (iv) late deficit (LD), irrigated as for the Control until veraison and thereafter at 25% ETc until harvest. Yield in ED and LD was reduced 25 and 15%, respectively, compared with that of Control. Yield under SDI did not differ significantly from that of the Control and was similar to that of the ED and LD treatments. All the deficit irrigation treatments had some carry-over effects because of a significant decrease in shoot fruitfulness and in bunch mass due to smaller berries. The ED treatment, however, led to a greater reduction in berry growth compared with that of LD. Full irrigation increased pruning mass and leaf area mainly due to larger secondary shoots, and besides delayed grape ripening. Conclusions The SDI treatment resulted in the greatest water use efficiency, ensuring the high yield potential of the cultivar, providing that a threshold value of water stress integral of 35 MPa day during the periods from anthesis to veraison and from veraison to harvest was not surpassed. In addition, it favoured an improved grape composition, particularly in terms of grape sugar accumulation. Significance of the Study Sustained deficit irrigation at 50% of ETc should be applied where water is scarce or expensive, paying attention to possible carry-over effects if water withholding has to be continued for more than three seasons.
Development of a small unmanned aircraft system to derive CO2 emissions of anthropogenic point sources

<p>A reduction of the anthropogenic emissions of CO<sub>2</sub> (carbon dioxide) is necessary to stop or slow down man-made climate change. To verify mitigation strategies, a global monitoring system such as the envisaged European Copernicus anthropogenic CO<sub>2</sub> monitoring mission (CO2M) is required. Those satellite data are going to be complemented and validated with airborne measurements. UAV (unmanned aerial vehicle) based measurements can provide a cost-effective way to contribute to these activities. Here we present the development of a sUAS (small unmanned aircraft system) to quantify the CO<sub>2</sub> emissions of a nearby point source from its downwind mass flux without the need for any ancillary data. Specifically, CO<sub>2</sub> is measured by an in situ NDIR (non-dispersive infrared) detector and the wind speed and direction is measured with a 2D ultrasonic acoustic resonance anemometer. In order to minimize the effect of rotor downwash, we calibrate the anemometer by analyzing wind measurements taken while following a suitable flight pattern and assuming stationary wind conditions. We quantify the quality of the CO<sub>2</sub> and wind measurements with an in-flight validation at the ICOS (Integrated Carbon Observation System) atmospheric station Steinkimmen (STE) near Bremen, Germany. By means of two flights downwind of the ExxonMobil natural gas processing facility in Gro&#223;enkneten about 40km east of Bremen, Germany, we demonstrate how the measurements of elevated CO<sub>2</sub> concentrations can be used to infer mass fluxes of atmospheric CO<sub>2</sub> related to the emissions of the facility. </p>