Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying microbial drivers in biological phenotypes with a Bayesian Network Regression model

1. In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high-dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes have an effect on the response (main effects), not just the interactions. 2. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. 3. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. 4. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy-to-use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.
Penalized empirical likelihood estimation and EM algorithms for closed-population capture-recapture models

Capture–recapture experiments are widely used to estimate the abundance of a ﬁnite population. Based on capture–recapture data, the empirical likelihood (EL) method has been shown to outperform the conventional conditional likelihood (CL) method. However, the current literature on EL abundance estimation ignores behavioral eﬀects, and the EL estimates may not be stable, especially when the capture probability is low. We make three contributions in this paper. First, we extend the EL method to capture–recapture models that account for behavioral eﬀects. Second, to overcome the instability of the EL method, we propose a penalized EL (PEL) estimation method that penalizes large abundance values. We then investigate the asymptotics of the maximum PEL estimator and the PEL ratio statistic. Third, we develop standard expectation–maximization (EM) algorithms for PEL to improve its practical performance. The EM algorithm is also applicable to EL and CL with slight modiﬁcations. Our simulation and a real-world data analysis demonstrate that the PEL method successfully overcomes the instability of the EL method and the proposed EM algorithm produces more reliable results than existing optimization algorithms.
Correlating Effectiveness of Pointer Analysis Techniques with Patterns in Embedded System Code

A pointer analysis maps the pointers in a program to the memory locations they point to. In this work, we study the effectiveness of the three flavors of pointer analysis namely flow sensitive, flow insensitive, and context sensitive analysis on seven embedded code sets used in the industry. We compare precision gain i.e., the reduction in the number of spurious memory locations pointed by a pointer in each of these settings. We found that in 90% of cases the pointer information was same in all three settings. In other cases, context sensitive analysis was 2.6% more precise than flow sensitive analysis which was 6.8% more precise than flow insensitive analysis on average. We correlate precision gain with coding patterns in the embedded systems-which we believe to be first of its kind activity.
Future prospects on testing extensions to 
ΛCDM
 through the weak lensing of gravitational waves

With planned space-based and 3rd generation ground-based gravitational wave detectors (LISA, Einstein Telescope, Cosmic Explorer), and proposed DeciHz detectors (DECIGO, Big Bang Observer), it is timely to explore statistical cosmological tests that can be employed with the forthcoming plethora of data, $10^4-10^6$ mergers a year. We forecast the combination of the standard siren measurement with the weak lensing of gravitational waves from binary mergers. For 10 years of 3rd generation detector runtime, this joint analysis will constrain the dark energy equation of state with marginalised $1\sigma$ uncertainties of $\sigma(w_0)$~0.005 and $\sigma(w_a)$~0.04. This is comparable to or better than forecasts for future galaxy/intensity mapping surveys, and better constraints are possible when combining these and other future probes with gravitational waves. We find that combining mergers with and without an electromagnetic counterpart helps break parameter degeneracies. Using DeciHz detectors in the post-LISA era, we demonstrate for the first time how merging binaries could achieve a precision on the sum of neutrino masses of $\sigma(\Sigma m_{\nu})$~0.05 eV using $3\times10^6$ sources up to $z=3.5$ with a distance uncertainty of $1\%$, and ~percent or sub-percent precision also on curvature, dark energy, and other parameters, independently from other probes. Finally, we demonstrate how the cosmology dependence in the redshift distribution of mergers can be exploited to improve dark energy constraints if the cosmic merger rate is known, instead of relying on measured distributions as is standard in cosmology. In the coming decades gravitational waves will become a formidable probe of both geometry and large scale structure.
Travelling waves and heteroclinic networks in models of spatially-extended cyclic competition

Dynamical systems containing heteroclinic cycles and networks can be invoked as models of intransitive competition between three or more species. When populations are assumed to be well-mixed, a system of ordinary differential equations (ODEs) describes the interaction model. Spatially extending these equations with diffusion terms creates a system of partial differential equations which captures both the spatial distribution and mobility of species. In one spatial dimension, travelling wave solutions can be observed, which correspond to periodic orbits in ODEs that describe the system in a steady-state travelling frame of reference. These new ODEs also contain a heteroclinic structure. For three species in cyclic competition, the topology of the heteroclinic cycle in the well-mixed model is preserved in the steady-state travelling frame of reference. We demonstrate that with four species, the heteroclinic cycle which exists in the well-mixed system becomes a heteroclinic network in the travelling frame of reference, with additional heteroclinic orbits connecting equilibria not connected in the original cycle. We find new types of travelling waves which are created in symmetry-breaking bifurcations and destroyed in an orbit flip bifurcation with a cycle between only two species. These new cycles explain the existence of ‘defensive alliances’ observed in previous numerical experiments. We further describe the structure of the heteroclinic network for any number of species, and we conjecture how these results may generalise to systems of any arbitrary number of species in cyclic competition.
Warped Disk Galaxies. I. Linking U-type Warps in Groups/Clusters to Jellyfish Galaxies

Warped disk galaxies are classified into two morphologies: S and U types. Conventional theories routinely attribute both types to galactic tidal interaction and/or gas accretion, but reproducing U types in simulations is extremely challenging. Here we investigate whether both types are governed by the same mechanisms using the most extensive sample of ∼8000 nearby (0.02 < z < 0.06) massive (M */M ⊙ > 109) edge-on disks from the Sloan Digital Sky Survey. We find that U types show on average bluer optical colors and a higher specific star formation rate (sSFR) than S types, with more strongly warped U types having a higher sSFR. We also find that while the S-type warp properties correlate with the tidal force by the nearest neighbor regardless of the environment, there is no such correlation for U types in groups/clusters, suggesting a nontidal mechanism could be at play for U types, such as ram pressure stripping (RPS). Indeed, U types are more common in groups/clusters than in fields and they have stellar mass, gas fraction, sSFR enhancement, and phase-space distribution closely analogous to RPS-induced jellyfish galaxies in clusters. We furthermore show that the stellar disks of most RPS galaxies in the IllustrisTNG simulation are warped in a U shape and bent in the opposite direction of stripped gas tails, satisfying theoretical expectations for stellar warps embedded in jellyfishes. We therefore suggest that despite the majority of U types that live in fields being still less explained, RPS can be an alternative origin for those in groups/clusters.
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP

Web-crawled datasets have enabled remarkable generalization capabilities in recent image-text models such as CLIP (Contrastive Language-Image pre-training) or Flamingo, but little is known about the dataset creation processes. In this work, we introduce a testbed of six publicly available data sources - YFCC, LAION, Conceptual Captions, WIT, RedCaps, Shutterstock - to investigate how pre-training distributions induce robustness in CLIP. We find that the performance of the pre-training data varies substantially across distribution shifts, with no single data source dominating. Moreover, we systematically study the interactions between these data sources and find that combining multiple sources does not necessarily yield better models, but rather dilutes the robustness of the best individual data source. We complement our empirical findings with theoretical insights from a simple setting, where combining the training data also results in diluted robustness. In addition, our theoretical model provides a candidate explanation for the success of the CLIP-based data filtering technique recently employed in the LAION dataset. Overall our results demonstrate that simply gathering a large amount of data from the web is not the most effective way to build a pre-training dataset for robust generalization, necessitating further study into dataset design. Code is available at https://github.com/mlfoundations/clip_quality_not_quantity.
Diamonds are Forever in the Blockchain: Geometric Polyhedral Point-Set Pattern Matching

Motivated by blockchain technology for supply-chain tracing of ethically sourced diamonds, we study geometric polyhedral point-set pattern matching as minimum-width polyhedral annulus problems under translations and rotations. We provide two $(1 + \varepsilon)$-approximation schemes under translations with $O(\varepsilon^{-d} n)$-time for $d$ dimensions and $O(n\log \varepsilon^{-1} + \varepsilon^{-2})$-time for two dimensions, and we give an $O(f^{d-1}\varepsilon^{1-2d}n)$-time algorithm when also allowing for rotations, parameterized on $f$, which we define as the slimness of the point set.
Multi-Factor Key Derivation Function (MFKDF)

We present the first general construction of a Multi-Factor Key Derivation Function (MFKDF). Our function expands upon password-based key derivation functions (PBKDFs) with support for using other popular authentication factors like TOTP, HOTP, and hardware tokens in the key derivation process. In doing so, it provides an exponential security improvement over PBKDFs with less than 12 ms of additional computational overhead in a typical web browser. We further present a threshold MFKDF construction, allowing for client-side key recovery and reconstitution if a factor is lost. Finally, by"stacking"derived keys, we provide a means of cryptographically enforcing arbitrarily specific key derivation policies. The result is a paradigm shift toward direct cryptographic protection of user data using all available authentication factors, with no noticeable change to the user experience. We demonstrate the ability of our solution to not only significantly improve the security of existing systems implementing PBKDFs, but also to enable new applications where PBKDFs would not be considered a feasible approach.
Heterogeneity assessment in causal data fusion problems

Previous works have formalized the conditions under which findings from a source population could be reasonably extrapolated to another target population, the so-called"transportability"problem. While most of these works focus on a setting with two populations, many recent works have also provided the identifiability of a causal parameter when multiple data sources are available, under certain homogeneity assumptions. However, we know of little work examining transportability when data sources are possibly heterogeneous, e.g. in the distribution of mediators of the exposure-outcome relation. The presence of such heterogeneity generally invalidates the transportability assumption required in most of the literature. In this paper, we will propose a general approach for heterogeneity assessment when estimating the average exposure effect in a target population, with mediator and outcome data obtained from multiple external sources. To account for heterogeneity, we define different effect estimands when the mediator and outcome information is transported from different sources. We discuss the causal assumptions to identify these estimands, then propose efficient semi-parametric estimation strategies that allow the use of flexible data-adaptive machine learning methods to estimate the nuisance parameters. We also propose two new methods to investigate sources of heterogeneity in the transported estimates. These methods will inform users about how much of the observed statistical heterogeneity in the transported effects is due to the differences across data sources in: 1) conditional distribution of mediator variables, and/or 2) conditional distribution of the outcome. We illustrate the proposed methods using four sites that were part of the Moving to Opportunity Study, which was an experiment that randomized housing voucher receipt to participating families living in public housing.
A Modified UDP for Federated Learning Packet Transmissions

— This paper introduces a Modified User Datagram Protocol (UDP) for Federated Learning to ensure efficiency and reliability in the model parameter transport process, maximizing the potential of the Global model in each Federated Learning round. In developing and testing this protocol, the NS3 simulator is utilized to simulate the packet transport over the network and Google TensorFlow is used to create a custom Federated learning environment. In this preliminary implementation, the simulation contains three nodes where two nodes are client nodes, and one is a server node. The results obtained in this paper provide confidence in the capabilities of the protocol in the future of Federated Learning therefore, in future the Modified UDP will be tested on a larger Federated learning system with a TensorFlow model containing more parameters and a comparison between the traditional UDP protocol and the Modified UDP protocol will be simulated. Optimization of the Modified UDP will also be explored to improve efficiency while ensuring reliability.
Engineered entropic forces allow ultrastrong dynamical backaction

When confined within an optical cavity light can exert strong radiation pressure forces. Combined with dynamical backaction, this enables important processes, such as laser cooling, and applications ranging from precision sensors to quantum memories and interfaces. However, the magnitude of radiation pressure forces is constrained by the energy mismatch between photons and phonons. Here, we overcome this barrier using entropic forces arising from the absorption of light. We show that entropic forces can exceed the radiation pressure force by eight orders of magnitude and demonstrate this using a superfluid helium third-sound resonator. We develop a framework to engineer the dynamical backaction from entropic forces, applying it to achieve phonon lasing with a threshold three orders of magnitude lower than previous work. Our results present a pathway to exploit entropic forces in quantum devices and to study nonlinear fluid phenomena such as turbulence and solitons.
A Sensitive Flexible Network Approach

This paper takes an axiomatic approach to find rules for allocating the value of a network when the externalities generated across components are identifiable. Two new, and different, allocation rules are defined and characterized in this context. The first one is an extension of the player-based flexible-network allocation rule (Jackson (2005)). The second one follows the flexible network approach from a component-wise point of view, where the notion of network flexibility is adjusted with a flavor of core stability. Furthermore, two other allocation rules are proposed by relaxing the axiom of equal treatment of vital players. These collapse into the player-based flexible-network allocation rule (Jackson (2005)) for zero-normalized value functions with no externalities across components.
Regularizing Deep Neural Networks with Stochastic Estimators of Hessian Trace

In this paper, we develop a novel regularization method for deep neural networks by penalizing the trace of Hessian. This regularizer is motivated by a recent guarantee bound of the generalization error. We explain its benefits in finding flat minima and avoiding Lyapunov stability in dynamical systems. We adopt the Hutchinson method as a classical unbiased estimator for the trace of a matrix and further accelerate its calculation using a dropout scheme. Experiments demonstrate that our method outperforms existing regularizers and data augmentation methods, such as Jacobian, Confidence Penalty, Label Smoothing, Cutout, and Mixup.
Fractional Laplacian – Quadrature Rules for Singular Double Integrals in 3D

Abstract In this article, quadrature rules for the efficient computation of the stiffness matrix for the fractional Laplacian in three dimensions are presented. These rules are based on the Duffy transformation, which is a common tool for singularity removal. Here, this transformation is adapted to the needs of the fractional Laplacian in three dimensions. The integrals resulting from this Duffy transformation are regular integrals over less-dimensional domains. We present bounds on the number of Gauss points to guarantee error estimates which are of the same order of magnitude as the finite element error. The methods presented in this article can easily be adapted to other singular double integrals in three dimensions with algebraic singularities.
Language Tokens: Simply Improving Zero-Shot Multi-Aligned Translation in Encoder-Decoder Models

This paper proposes a simple and effective method to improve direct translation for the zero-shot case and when direct data is available. We modify the input tokens at both the encoder and decoder to include signals for the source and target languages. We show a performance gain when training from scratch, or finetuning a pretrained model with the proposed setup. In in-house experiments, our method shows nearly a 10.0 BLEU points difference depending on the stoppage criteria. In a WMT-based setting, we see 1.3 and 0.4 BLEU points improvement for the zero-shot setting, and when using direct data for training, respectively, while from-English performance improves by 4.17 and 0.85 BLEU points. In the low-resource setting, we see a 1.5 ∼ 1.7 point improvement when finetuning on directly translated domain data.
Scattering of a particle on the $q$-deformed Euclidean space

We develop a formalism for the scattering of a particle on the q -de-formed Euclidean space. We write down q -versions of the Lippmann-Schwinger equation. Their iterative solutions for a weak scattering potential lead us to q -versions of the Born series. With the expressions for the wave functions of the scattered particle, we can write down S-matrix elements. We show that these S-matrix elements satisfy unitarity conditions. Considerations about the interaction picture for a quantum system in the q -deformed Euclidean space and a discussion of a q -version of time-dependent perturbation theory conclude our studies.
Network Emulation in Large-Scale Virtual Edge Testbeds: A Note of Caution and the Way Forward

The growing research and industry interest in the Internet of Things and the edge computing paradigm has increased the need for cost-efficient virtual testbeds for large-scale distributed applications. Researchers, students, and practitioners need to test and evaluate the interplay of hundreds or thousands of real software components and services connected with a realistic edge network without access to phvsical infrastructure. While advances in virtualization technologies have enabled parts of this, network emulation as a crucial part in the development of edge testbeds is lagging behind: As we show in this paper, NetEm, the current state-of-the-art network emulation tooling included in the Linux kernel, imposes prohibitive scalability limits. We quantify these limits, investigate possible causes, and present a way forward for network emulation in large-scale virtual edge testbeds based on eBPFs.
Stiefel-Whitney Classes of Representations of $\text{SL}(2,q)$

We describe the Stiefel-Whitney classes (SWCs) of orthogonal representations $\pi$ of the finite special linear groups $G=\text{SL}(2,\mathbb F_q)$, in terms of character values of $\pi$. From this calculation, we can answer interesting questions about SWCs of $\pi$. For instance, we determine the subalgebra of $H^*(G,\mathbb Z/2\mathbb Z)$ generated by the SWCs of orthogonal $\pi$, and we also determine which $\pi$ have nontrivial mod $2$ Euler class.
Imbalance Trouble: Revisiting Neural-Collapse Geometry

Neural Collapse refers to the remarkable structural properties characterizing the geometry of class embeddings and classifier weights, found by deep nets when trained beyond zero training error. However, this characterization only holds for balanced data. Here we thus ask whether it can be made invariant to class imbalances. Towards this end, we adopt the unconstrained-features model (UFM), a recent theoretical model for studying neural collapse, and introduce Simplex-Encoded-Labels Interpolation (SELI) as an invariant characterization of the neural collapse phenomenon. Specifically, we prove for the UFM with cross-entropy loss and vanishing regularization that, irrespective of class imbalances, the embeddings and classifiers always interpolate a simplex-encoded label matrix and that their individual geometries are determined by the SVD factors of this same label matrix. We then present extensive experiments on synthetic and real datasets that confirm convergence to the SELI geometry. However, we caution that convergence worsens with increasing imbalances. We theoretically support this finding by showing that unlike the balanced case, when minorities are present, ridge-regularization plays a critical role in tweaking the geometry. This defines new questions and motivates further investigations into the impact of class imbalances on the rates at which first-order methods converge to their asymptotically preferred solutions.