Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Non-parametric Mixture Models and the Recurrent Chinese Restaurant Process

1. The Temporal Dirichlet Process Mixture Model Dirichlet process mixture models provide a flexible Bayesian framework for estimating a distribution as an infinite mixture of simpler distributions that could identify latent classes in the data [1]. However the full exchangeability assumption they employ makes them an unappealing choice for modeling longitudinal data such as text, audio and video streams that can arrive or accumulate as epochs, where data points inside the same epoch can be assumed to be fully exchangeable, whereas across the epochs both the structure (i.e., the number of mixture components) and the parameteriza-tions of the data distributions can evolve and therefore unexchangeable. To solve this problem, we present the temporal Dirich-let process mixture model (TDPM) as a framework for modeling these complex longitudinal data, in which the number of mixture components at each time point is unbounded; the components themselves can retain, die out or emerge over time; and the actual param-eterization of each component can also evolve over time in a Markovian fashion. For lack of space, we restrict ourselves here to one construction of our process that we call the Recurrent Chinese Restaurant Process (RCRP), for other two equivalent constructions, please refer to [2]. The RCRP, showin in Figure 1, is a generalization of the CRP. The RCRP operates in epochs, say, days. Customers entered the restaurant in a given day are not allowed to stay beyond the end of this day. At the end of each day, the consumptions of dishes are analyzed by the owner of the restaurant who assumes that popular dishes will remain popular in the next day, and uses this fact to plan the ingredients to be bought, and the seating plan for the next day. To encourage customers in the next day to try out those pre-planed dishes, he records on each table the dish which was served there, as well as the number of customers who shared it. As another incentive, he allows the first customer to set on such a table to order a variation of the dish recorded there. The generative process proceeds as follows. At day t, customer i can pick an empty table, k, that was used to serve dish φ k,t−1 , and had n k,t−1 customers, with probability equals to n k,t−1 N t−1 +i+α−1 , he then chooses the current flavor of the dish,φ k,t , distributed according to φ k,t …
High-Speed Digital System Design: A Handbook of Interconnect Theory and Design Practices

From the Publisher: An understanding of high-speed interconnect phenomena is essential for digital designers who must deal with the challenges posed by the ever-increasing operating speeds of today's microprocessors. This book provides a much-needed, practical guide to the state of the art of modern digital system design, combining easily accessible explanations with immensely useful problem-solving strategies. Written by three leading Intel engineers, High-Speed Digital System Design clarifies difficult and often neglected topics involving the effects of high frequencies on digital buses and presents a variety of proven techniques and application examples. Extensive appendices, formulas, modeling techniques as well as hundreds of figures are also provided.
Method for Zero-Waste Circular Economy Using Worms for Plastic Agriculture: Augmenting Polystyrene Consumption and Plant Growth

Polystyrene (PS) is one of the major plastics contributing to environmental pollution with its durability and resistance to natural biodegradation. Recent research showed that mealworms (Tenebrio molitor) and superworms (Zophobas morio) are naturally able to consume PS as a carbon food source and degrade them without observable toxic effects. In this study, we explored the effects of possible food additives and use of worm frass as potential plant fertilizers. We found that small amounts of sucrose and bran increased PS consumption and that the worm frass alone could support dragon fruit cacti (Hylocereus undatus) growth, with superworm frass in particular, supporting better growth and rooting than mealworm frass and control media over a fortnight. As known fish and poultry feed, these findings present worms as a natural solution to simultaneously tackle both the global plastic problem and urban farming issue in a zero-waste sustainable bioremediation cycle.
Clockwork Neutrinogenesis: Baryogenesis from theory space

We propose a minimal clockwork model to illustrate the possibility of baryogenesis via leptogenesis in a theory space setting. The standard lepton sector is augmented with three copies of a clockwork lattice made of SM neutral fermions. The two boundaries of these one-dimensional lattices are endowed with couplings to the SM leptons and three dark sector fermions, respectively. Small neutrino masses and a resonance enhanced Dirac leptogenesis are naturally obtained for anarchic textures of the Yukawa matrices, with $\sim O(1)$ elements, provided the heavy clockwork fermions have masses $\gtrsim O(10 \, \mbox{TeV})$.
Three-dimensional shear-flow instability saturation via stable modes

Turbulence in three dimensions (3D) supports vortex stretching that has long been known to accomplish energy transfer to small scales. Moreover, net energy transfer from large-scale, forced, unstable flow-gradients to smaller scales is achieved by gradient-flattening instability. Despite such enforcement of energy transfer to small scales, it is shown here that the shear-flow-instability-supplied 3D-fluctuation energy is largely inverse-transferred from the fluctuation to the mean-flow gradient, and such inverse transfer is more efficient for turbulent fluctuations in 3D than in two dimensions (2D). The transfer is due to linearly stable eigenmodes that are excited nonlinearly. The stable modes, thus, reduce both the nonlinear energy cascade to small scales and the viscous dissipation rate. The vortex-tube stretching is also suppressed. Up-gradient momentum transport by the stable modes counters the instability-driven down-gradient transport, which also is more effective in 3D than in 2D (≈70% vs ≈50%). From unstable modes, these stable modes nonlinearly receive energy via zero-frequency fluctuations that vary only in the direction orthogonal to the plane of 2D shear flow. The more widely occurring 3D turbulence is thus inherently different from the commonly studied 2D turbulence, despite both saturating via stable modes.
Equivariant Matrix Function Neural Networks

Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications. However, MPNNs face challenges when modeling non-local interactions in graphs such as large conjugated molecules, and social networks due to oversmoothing and oversquashing. Although Spectral GNNs and traditional neural networks such as recurrent neural networks and transformers mitigate these challenges, they often lack generalizability, or fail to capture detailed structural relationships or symmetries in the data. To address these concerns, we introduce Matrix Function Neural Networks (MFNs), a novel architecture that parameterizes non-local interactions through analytic matrix equivariant functions. Employing resolvent expansions offers a straightforward implementation and the potential for linear scaling with system size. The MFN architecture achieves stateof-the-art performance in standard graph benchmarks, such as the ZINC and TU datasets, and is able to capture intricate non-local interactions in quantum systems, paving the way to new state-of-the-art force fields.
Upper and lower frequency curves for peak rates of runoff

Maximum annual peak rates of runoff plotted on extremal probability paper have many times been found to exhibit trends that may be defined by two straight lines designated as upper and lower frequency curves. Using a sample of 69 gaged watersheds with periods of record ranging from 26 to 48 years, a high degree of correlation is found to exist between the upper and lower frequency curves for 10- and 50-year recurrence intervals. Tests are made to show that these relations may be used in conjunction with periods of record as short as ten years to materially improve the precision of estimates of peaks on the upper frequency curve. Evidence is presented that suggests that the dog leg in frequency curves is the result of sampling from two different populations of peak rates of runoff.
Physics-informed neural wavefields with Gabor basis functions

Recently, Physics-Informed Neural Networks (PINNs) have gained significant attention for their versatile interpolation capabilities in solving partial differential equations (PDEs). Despite their potential, the training can be computationally demanding, especially for intricate functions like wavefields. This is primarily due to the neural-based (learned) basis functions, biased toward low frequencies, as they are dominated by polynomial calculations, which are not inherently wavefield-friendly. In response, we propose an approach to enhance the efficiency and accuracy of neural network wavefield solutions by modeling them as linear combinations of Gabor basis functions that satisfy the wave equation. Specifically, for the Helmholtz equation, we augment the fully connected neural network model with an adaptable Gabor layer constituting the final hidden layer, employing a weighted summation of these Gabor neurons to compute the predictions (output). These weights/coefficients of the Gabor functions are learned from the previous hidden layers that include nonlinear activation functions. To ensure the Gabor layer's utilization across the model space, we incorporate a smaller auxiliary network to forecast the center of each Gabor function based on input coordinates. Realistic assessments showcase the efficacy of this novel implementation compared to the vanilla PINN, particularly in scenarios involving high-frequencies and realistic models that are often challenging for PINNs.
Petroleum Migration, Alteration, and Remigration Within Troll Field, Norwegian North Sea

Troll field represents the largest petroleum discovery within the entire North Sea area in oil equivalents, with 74% of the accumulated petroleum present as dry gas and 26% as a heavy biodegraded oil leg. The field is divided into several provinces based on the distribution of gas and oil, and the gas and oil have been suggested to be cogenetic. The migration and filling model presented in this paper suggests that the oil and gas represent two different migration phases and that gas migration and filling predate oil emplacement. Two different oil populations have been characterized and mapped in Troll field applying conventional geochemical techniques. We suggest that the two oil populations migrated into the structure through two different migration systems. Oil and gas were subsequently biodegraded within the reservoir. The two oil populations have been found in neighboring oil and gas discoveries, and an oil-oil correlation with these discoveries has been used to determine the location of field filling points and regional migration routes. When oil biodegradation terminated, fresh oil continued to migrate into the reservoir and mixed with the residue of the biodegraded oil. The field was tilted downward to the west in the Neogene, and oil and gas remigrated within the field with a possible spillage of gas. Tilting resulted in a dominantly upward movement of the oil phase whereas gas migrated laterally. Residual oils in the water zone ©Copyright 1997. The American Association of Petroleum Geologists. All rights reserved.1Manuscript received July 18, 1995; revised manuscript received March 14, 1996; final acceptance August 16, 1996. 2Saga Petroleum a.s. , Kjorboveien 16, P.O. Box 490, N-1301 Sandvika, Norway. 3Newcastle Research Group, Drummond Building, University of Newcastle upon Tyne, Newcastle-upon-Tyne NE1 7RU, United Kingdom. We would like to thank the partners in the Troll license who allowed us to publish these results. Per Erling Johansen and Tone Molvik are acknowledged for the analytical work, and Snorre Olaussen, Nigel Mills, and Terje Hellem are thanked for helpful discussions and for assistance with the back tilting of the IRAP reservoir field model. Jill Sonrier is acknowledged for typing. We acknowledge K. M. Wolgemuth, John F. Brint, and two anonymous reviewers for helpful comments that improved the paper significantly.
Identifiability of Product of Experts Models

Product of experts (PoE) are layered networks in which the value at each node is an AND (or product) of the values (possibly negated) at its inputs. These were introduced as a neural network architecture that can efficiently learn to generate high-dimensional data which satisfy many low-dimensional constraints -- thereby allowing each individual expert to perform a simple task. PoEs have found a variety of applications in learning. We study the problem of identifiability of a product of experts model having a layer of binary latent variables, and a layer of binary observables that are iid conditional on the latents. The previous best upper bound on the number of observables needed to identify the model was exponential in the number of parameters. We show: (a) When the latents are uniformly distributed, the model is identifiable with a number of observables equal to the number of parameters (and hence best possible). (b) In the more general case of arbitrarily distributed latents, the model is identifiable for a number of observables that is still linear in the number of parameters (and within a factor of two of best-possible). The proofs rely on root interlacing phenomena for some special three-term recurrences.
Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria

In light of climate change's impacts on forests, including extreme drought and late-frost, leading to vitality decline and regional forest die-back, we assess univariate drought and late-frost risks and perform a joint risk analysis in Bavaria, Germany, from 1952 to 2020. Utilizing a vast dataset with 26 bioclimatic and topographic variables, we employ vine copula models due to the data's non-Gaussian and asymmetric dependencies. We use D-vine regression for univariate and Y-vine regression for bivariate analysis, and propose corresponding univariate and bivariate conditional probability risk measures. We identify"at-risk"regions, emphasizing the need for forest adaptation due to climate change.
Statistical and Causal Robustness for Causal Null Hypothesis Tests

Prior work applying semiparametric theory to causal inference has primarily focused on deriving estimators that exhibit statistical robustness under a prespecified causal model that permits identification of a desired causal parameter. However, a fundamental challenge is correct specification of such a model, which usually involves making untestable assumptions. Evidence factors is an approach to combining hypothesis tests of a common causal null hypothesis under two or more candidate causal models. Under certain conditions, this yields a test that is valid if at least one of the underlying models is correct, which is a form of causal robustness. We propose a method of combining semiparametric theory with evidence factors. We develop a causal null hypothesis test based on joint asymptotic normality of K asymptotically linear semiparametric estimators, where each estimator is based on a distinct identifying functional derived from each of K candidate causal models. We show that this test provides both statistical and causal robustness in the sense that it is valid if at least one of the K proposed causal models is correct, while also allowing for slower than parametric rates of convergence in estimating nuisance functions. We demonstrate the efficacy of our method via simulations and an application to the Framingham Heart Study.
Observations of H2SO4 and MSA during PEM-Tropics-A

Results are presented of measurements of the concentration of gas phase H2SO4 and methane sulfonic acid (MSA) performed aboard the NASA P3-b aircraft during the Pacific Exploratory Mission (PEM) Tropics study using the selected ion chemical ionization mass spectrometry (SICIMS) technique. During a nighttime portion of one flight the [H2SO4] was found to increase with decreasing relative humidity (RH). When compared to laboratory measurements of H2SO4 vapor pressure as a function of RH and particle neutralization (NH4+ and SO42− ionic composition) and model predictions using a liquid drop hydrate model, these measurements indicate that the particles from which the H2SO4 is evaporating are relatively unneutralized, a result which is in good agreement with filter measurements. Overall, the same increase in the gas phase [MSA] with decreasing RH or decreasing [NH4+] (obtained from filter measurements) was also observed, indicating a high volatility of MSA at low RH values or particle neutralization. When gas phase MSA values are compared to methane sulfonate (MS) values obtained from filter measurements, it was found that MSA was totally volatilized at low RH values, while MSA resides mainly in the particulate form at high RH values. Combining the gas phase and filter measurements, the boundary layer MS/(MS + SO42−) ratio showed a distinct increase with decreasing temperature and suggests that little or no MS or MSA is produced in the boundary layer at temperatures above 300 K.
Geochemistry of zinc, copper and lead in the sediments of the estuary and Gulf of St. Lawrence

Total elemental concentrations (Zn, 8–215 ppm; Cu, 3–76 ppm; Pb, 9–66 ppm) vary texturally and regionally in the sediments of the St. Lawrence estuary and open Gulf of St. Lawrence.Chemical and mineralogical analyses indicate that 8–39% of the total Zn, 7–20% of the total Cu and 15–26% of the total Pb are weakly held by fine-grained organic material, oxide grain coatings, ion exchange positions and carbonates in the sediments. These concentrations are potentially available to the biota. The remaining concentrations (61–93% of the total) are not readily available but are locked up in fine-grained sulphide, oxide and silicate minerals.The highest concentrations of the detrital and non-detrital contributions occur in the pelites or muds of the upper estuary. Seaward these concentrations decrease as the natural and anthropogenic supply of dissolved and suspended particulate matter from the St. Lawrence River diminishes. The upper estuary acts as a sink for these elements because most of the non-detrital Zn, P.
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting

Query rewriting plays a vital role in enhancing conversational search by transforming context-dependent user queries into standalone forms. Existing approaches primarily leverage human-rewritten queries as labels to train query rewriting models. However, human rewrites may lack sufficient information for optimal retrieval performance. To overcome this limitation, we propose utilizing large language models (LLMs) as query rewriters, enabling the generation of informative query rewrites through well-designed instructions. We define four essential properties for well-formed rewrites and incorporate all of them into the instruction. In addition, we introduce the role of rewrite editors for LLMs when initial query rewrites are available, forming a"rewrite-then-edit"process. Furthermore, we propose distilling the rewriting capabilities of LLMs into smaller models to reduce rewriting latency. Our experimental evaluation on the QReCC dataset demonstrates that informative query rewrites can yield substantially improved retrieval performance compared to human rewrites, especially with sparse retrievers.
Label Differential Privacy via Aggregation

In many real-world applications, due to recent developments in the privacy landscape, training data may be aggregated to preserve the privacy of sensitive training labels. In the learning from label proportions (LLP) framework, the dataset is partitioned into bags of feature-vectors which are available only with the sum of the labels per bag. A further restriction, which we call learning from bag aggregates (LBA) is where instead of individual feature-vectors, only the (possibly weighted) sum of the feature-vectors per bag is available. We study whether such aggregation techniques can provide privacy guarantees under the notion of label differential privacy (label-DP) previously studied in for e.g. [Chaudhuri-Hsu'11, Ghazi et al.'21, Esfandiari et al.'22]. It is easily seen that naive LBA and LLP do not provide label-DP. Our main result however, shows that weighted LBA using iid Gaussian weights with $m$ randomly sampled disjoint $k$-sized bags is in fact $(\varepsilon, \delta)$-label-DP for any $\varepsilon>0$ with $\delta \approx \exp(-\Omega(\sqrt{k}))$ assuming a lower bound on the linear-mse regression loss. Further, the $\ell_2^2$-regressor which minimizes the loss on the aggregated dataset has a loss within $\left(1 + o(1)\right)$-factor of the optimum on the original dataset w.p. $\approx 1 - exp(-\Omega(m))$. We emphasize that no additive label noise is required. The analogous weighted-LLP does not however admit label-DP. Nevertheless, we show that if additive $N(0, 1)$ noise can be added to any constant fraction of the instance labels, then the noisy weighted-LLP admits similar label-DP guarantees without assumptions on the dataset, while preserving the utility of Lipschitz-bounded neural mse-regression tasks. Our work is the first to demonstrate that label-DP can be achieved by randomly weighted aggregation for regression tasks, using no or little additive noise.
Quantum Error Channels in High Energetic Photonic Systems

In medical applications -- such as positron emission tomography (PET) -- $511$keV photons that experience Compton scattering are studied. We present a consistent framework based on error-correction channels to fully describe the quantum information-theoretic content of high energetic photons undergoing Compton scattering, characterized by the Klein-Nishina formula in unoriented matter. In this way, we can predict the expected spatial distribution of two or more, pure or mixed, polarization entangled or unentangled photons. This framework allows us to characterize the accessible and inaccessible information for different parameter ranges. It also answers the question of how to describe successive multi-photon scattering. In addition our formalism provides a complete framework for dealing with single and all multi-partite errors that can occur in the propagation, providing the basis for modeling future dedicated experiments that will then have applications in medicine, such as reducing errors in PET imaging.
Robot Imitation from Video Demonstration

This paper presents an attempt to replicate the robot imitation work conducted by Sermanet et al., with a specific focus on the experiments involving robot joint position prediction. While the original study utilized human poses to predict robot joint positions, this project aimed to achieve robot-to-robot imitation due to the challenges of obtaining human-to-robot translation data. The primary objective was to provide a neural network with robot images and have it predict end-effector positions through regression. The paper discusses the implementation process, including data collection using the open-source RoboSuite, where a Python module was developed to capture randomized action data for four different robots. Challenges in data collection, such as oscillations and limited action variety, were addressed through domain randomization. Results show high testing error and unsatisfactory imitation due to overfitting, necessitating improvements in the project.