Miracle-dz/UniFAR-FTUs huggingface dataset:
Karyopherin α deficiency contributes to human preimplantation embryo arrest

Preimplantation embryo arrest (PREMBA) is a common cause of female infertility and recurrent failure of assisted reproductive technology. However, the genetic basis of PREMBA is largely unrevealed. Here, using whole-exome sequencing data from 606 women experiencing PREMBA compared with 2,813 controls, we performed a population and gene–based burden test and identified a candidate gene, karyopherin subunit α7 (KPNA7). In vitro studies showed that identified sequence variants reduced KPNA7 protein levels, impaired KPNA7 capacity for binding to its substrate ribosomal L1 domain-containing protein 1 (RSL1D1), and affected KPNA7 nuclear transport activity. Comparison between humans and mice suggested that mouse KPNA2, rather than mouse KPNA7, acts as an essential karyopherin in embryonic development. Kpna2–/– female mice showed embryo arrest due to zygotic genome activation defects, recapitulating the phenotype of human PREMBA. In addition, female mice with an oocyte-specific knockout of Rsl1d1 recapitulated the phenotype of Kpna2–/– mice, demonstrating the vital role of substrate RSL1D1. Finally, complementary RNA (cRNA) microinjection of human KPNA7, but not mouse Kpna7, was able to rescue the embryo arrest phenotype in Kpna2–/– mice, suggesting mouse KPNA2 might be a homologue of human KPNA7. Our findings uncovered a mechanistic understanding for the pathogenesis of PREMBA, which acts by impairing nuclear protein transport, and provide a diagnostic marker for PREMBA patients.
Error-related Potential Variability: Exploring the Effects on Classification and Transferability

Brain-Computer Interfaces (BCI) have allowed for direct communication from the brain to external applications for the automatic detection of cognitive processes such as error recognition. Error-related potentials (ErrPs) are a particular brain signal elicited when one commits or observes an erroneous event. However, due to the noisy properties of the brain and recording devices, ErrPs vary from instance to instance as they are combined with an assortment of other brain signals, biological noise, and external noise, making the classification of ErrPs a non-trivial problem. Recent works have revealed particular cognitive processes such as awareness, embodiment, and predictability that contribute to ErrP variations. In this paper, we explore the performance of classifier transferability when trained on different ErrP variation datasets generated by varying the levels of awareness and embodiment for a given task. In particular, we look at transference between observational and interactive ErrP categories when elicited by similar and differing tasks. Our empirical results provide an exploratory analysis into the ErrP transferability problem from a data perspective.
Symbolic expression generation via variational auto-encoder

There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. Widespread deep neural networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution for the symbolic regression task, and we aim to reduce this gap with our algorithm. In this work, we propose a novel deep learning framework for symbolic expression generation via variational autoencoder (VAE). We suggest using a VAE to generate mathematical expressions, and our training strategy forces generated formulas to fit a given dataset. Our framework allows encoding apriori knowledge of the formulas into fast-check predicates that speed up the optimization process. We compare our method to modern symbolic regression benchmarks and show that our method outperforms the competitors under noisy conditions. The recovery rate of SEGVAE is 65% on the Ngyuen dataset with a noise level of 10%, which is better than the previously reported SOTA by 20%. We demonstrate that this value depends on the dataset and can be even higher.
Delving Deep into Pixel Alignment Feature for Accurate Multi-view Human Mesh Recovery

Regression-based methods have shown high efficiency and effectiveness for multi-view human mesh recovery. The key components of a typical regressor lie in the feature extraction of input views and the fusion of multi-view features. In this paper, we present Pixel-aligned Feedback Fusion (PaFF) for accurate yet efficient human mesh recovery from multi-view images. PaFF is an iterative regression framework that performs feature extraction and fusion alternately. At each iteration, PaFF extracts pixel-aligned feedback features from each input view according to the reprojection of the current estimation and fuses them together with respect to each vertex of the downsampled mesh. In this way, our regressor can not only perceive the misalignment status of each view from the feedback features but also correct the mesh parameters more effectively based on the feature fusion on mesh vertices. Additionally, our regressor disentangles the global orientation and translation of the body mesh from the estimation of mesh parameters such that the camera parameters of input views can be better utilized in the regression process. The efficacy of our method is validated in the Human3.6M dataset via comprehensive ablation experiments, where PaFF achieves 33.02 MPJPE and brings significant improvements over the previous best solutions by more than 29%. The project page with code and video results can be found at https://kairobo.github. io/PaFF/.
Sifted Colimits, Strongly Finitary Monads and Continuous Algebras

We characterize strongly finitary monads on categories $\mathsf{Pos}$, $\mathsf{CPO}$ and $\mathsf{DCPO}$ as precisely those preserving sifted colimits. Or, equivalently, enriched finitary monads preserving reflexive coinserters. We study sifted colimits in general enriched categories. For $\mathsf{CPO}$ and $\mathsf{DCPO}$ we characterize varieties of continuous algebras as precisely the monadic categories for strongly finitary monads.
Geometric characterization of vortex lines in turbulence

Vorticity in turbulent ﬂows is often organized into complex geometries that inﬂuence the dynamics. We use a relatively novel approach to describe these geometries: that of obtaining segments of vortex lines embedded in the ﬂow. This enables us to quantify the geometric features of these objects. Vortex lines diﬀer widely in their behaviour but we ﬁnd some unifying features. The decay from high levels of vorticity is shown to happen over a short fraction of the vortex length. The local curvature is inversely related with the vorticity magnitude. Strong parts of vortex lines bundle together. It is hoped that this ﬁrst work will generate interest in such quantiﬁcation and the physics of vortex dynamics in turbulence.
Probing active-sterile neutrino transition magnetic moments at LEP and CEPC

We consider the sterile neutrino, that is also know as heavy neutral lepton (HNL), interacting with the Standard Model (SM) active neutrino and photon via a transition magnetic moment, the so-called dipole portal, which can be induced from the more general dipole couplings which respect the full gauge symmetries of the SM. Depending on the interactions with ${\rm SU}(2)_L$ and ${\rm U(1)}_Y$ field strength tensors ${\cal W}_{\mu \nu}^a$ and $B^{\mu \nu}$, we consider four typical scenarios and probe the constraints on the couplings with photon $d_\gamma$ at LEP using the analyses to search monophoton signature and the measurement of $Z$ decay. We find that in the considered scenarios assuming the coupling with $Z$ boson $d_Z\neq 0$, the measurement of $Z$ decaying into photon plus invisible particles can provide stricter constraints than the monophoton searches at the LEP1. The complementary constraints to existing experiments can be provided by the LEP. We also investigate the sensitivity on the dipole portal coupling $d_\gamma$ from the monophoton searches at future electron colliders, such as CEPC, and find that CEPC can explore the previously unconstrained parameter space by current experiments.
Ember-IO: Effective Firmware Fuzzing with Model-Free Memory Mapped IO

Exponential growth in embedded systems is driving the research imperative to develop fuzzers to automate firmware testing to uncover software bugs and security vulnerabilities. But, employing fuzzing techniques in this context present a uniquely challenging proposition; a key problem is the need to deal with the diverse and large number of peripheral communications in an automated testing framework. Recent fuzzing approaches: i) employ re-hosting methods by executing code in an emulator because fuzzing on resource limited embedded systems is slow and unscalable; and ii) integrate models of hardware behaviour to overcome the challenges faced by the massive input-space to be explored created by peripheral devices and to generate inputs that are effective in aiding a fuzzer to make progress. Our efforts expounds upon program execution behaviours unique to firmware to address the resulting input-space search problem. The techniques we propose improve the fuzzer’s ability to generate values likely to progress execution and avoids time consumed on mutating inputs that are functionally equivalent to other test cases. We demonstrate the methods are highly efficient and effective at overcoming the input-space search problem. Our emulation-based implementation, Ember-IO, when compared to the existing state-of-the-art fuzzing framework across 21 firmware binaries, demonstrates up to 255% improvement in blocks covered. Further Ember-IO discovered 6 new bugs in the real-world firmware, previously not identified by state-of-the-art fuzzing frameworks. Importantly, Ember-IO integrated with the state-of-the-art fuzzer, Fuzzware, demonstrates similar or improved coverage across all firmware binaries whilst reproducing 3 of the 6 new bugs discovered by Ember-IO.
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics

This article presents a novel approach to construct Intrinsic Gaussian Processes for regression on unknown manifolds with probabilistic metrics (GPUM) in point clouds. In many real world applications, one often encounters high dimensional data (e.g. point cloud data) centred around some lower dimensional unknown manifolds. The geometry of manifold is in general different from the usual Euclidean geometry. Naively applying traditional smoothing methods such as Euclidean Gaussian Processes (GPs) to manifold valued data and so ignoring the geometry of the space can potentially lead to highly misleading predictions and inferences. A manifold embedded in a high dimensional Euclidean space can be well described by a probabilistic mapping function and the corresponding latent space. We investigate the geometrical structure of the unknown manifolds using the Bayesian Gaussian Processes latent variable models(BGPLVM) and Riemannian geometry. The distribution of the metric tensor is learned using BGPLVM. The boundary of the resulting manifold is defined based on the uncertainty quantification of the mapping. We use the the probabilistic metric tensor to simulate Brownian Motion paths on the unknown manifold. The heat kernel is estimated as the transition density of Brownian Motion and used as the covariance functions of GPUM. The applications of GPUM are illustrated in the simulation studies on the Swiss roll, high dimensional real datasets of WiFi signals and image data examples. Its performance is compared with the Graph Laplacian GP, Graph Matern GP and Euclidean GP.
Energy transfer in monolayers with cyanine dye Sheibe aggregates

A monolayer of an oxacyanine dye organized in Scheibe aggregates is doped with a thiacyanine dye (e.g., one molecule of the guest per 104 molecules of the host). Strong energy transfer from the excited host to the guest takes place at room temperature, the efficiency decreasing with lowering temperature T. The rate of energy transfer is proportional to T. A simple model of the excited state is discussed based on a coherent exciton extending over a certain domain. The exciton moves over the aggregate, occasionally reaching the vicinity of an acceptor molecule. The domain size of the exciton is inversely proportional to the absolute temperature T. As a consequence, the lifetime of the exciton is proportional to T, and therefore the energy transfer efficiency is proportional to T.
Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are three-fold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws of the vehicle engine into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.
Theoretical modeling of capillary surfer interactions on a vibrating fluid bath

We present and analyze a theoretical model for the dynamics and interactions of"capillary surfers,"which are millimetric objects that self-propel while floating at the interface of a vibrating fluid bath. In our companion paper [1], we reported the results of an experimental investigation of the surfer system, which showed that surfer pairs may lock into one of seven bound states, and that larger collectives of surfers self-organize into coherent flocking states. Our theoretical model for the surfers' positional and orientational dynamics approximates a surfer as a pair of vertically oscillating point sources of weakly viscous gravity-capillary waves. We derive an analytical solution for the associated interfacial deformation and thus the hydrodynamic force exerted by one surfer on another. Our model recovers the bound states found in experiments and exhibits good quantitative agreement with experimental data. Moreover, a linear stability analysis shows that the bound states are quantized on the capillary wavelength, with stable branches of equilibria separated by unstable ones. Generally, our work shows that self-propelling objects coupled by interfacial flows constitute a promising platform for studying active matter systems in which both inertial and viscous effects are relevant.
Data-Driven Estimation of Heterogeneous Treatment Effects

Estimating how a treatment affects different individuals, known as heterogeneous treatment effect estimation, is an important problem in empirical sciences. In the last few years, there has been a considerable interest in adapting machine learning algorithms to the problem of estimating heterogeneous effects from observational and experimental data. However, these algorithms often make strong assumptions about the observed features in the data and ignore the structure of the underlying causal model, which can lead to biased estimation. At the same time, the underlying causal mechanism is rarely known in real-world datasets, making it hard to take it into consideration. In this work, we provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning, broadly categorizing them as methods that focus on counterfactual prediction and methods that directly estimate the causal effect. We also provide an overview of a third category of methods which rely on structural causal models and learn the model structure from data. Our empirical evaluation under various underlying structural model mechanisms shows the advantages and deficiencies of existing estimators and of the metrics for measuring their performance.
Design, Modeling and Control of a Top-Loading Fully-Actuated Cargo Transportation Multirotor

Existing multirotor-based cargo transportation does not maintain a constant cargo attitude due to underactuation; however, fragile payloads may require a consistent posture. The conventional method is also cumbersome when loading cargo, and the size of the cargo to be loaded is limited. To overcome these issues, we propose a new fully-actuated multirotor unmanned aerial vehicle platform capable of translational motion while maintaining a constant attitude. Our newly developed platform has a cubic exterior and can freely place cargo at any point on the flat top surface. However, the center-of-mass (CoM) position changes when cargo is loaded, leading to undesired attitudinal motion due to unwanted torque generation. To address this problem, we introduce a new model-free center-of-mass position estimation method named as the MOCE (Model-free Online Center-of-mass Estimation) algorithm, which is inspired by the extremum-seeking control (ESC) technique. Experimental results are presented to validate the performance of the proposed estimation method, effectively estimating the CoM position and showing satisfactory constant-attitude flight performance.
Custom 8-bit floating point value format for reducing shared memory bank conflict in approximate nearest neighbor search

The k-nearest neighbor search is used in various applications such as machine learning, computer vision, database search, and information retrieval. While the computational cost of the exact nearest neighbor search is enormous, an approximate nearest neighbor search (ANNS) has been attracting much attention. IVFPQ is one of the ANNS methods. Although we can leverage the high bandwidth and low latency of shared memory to compute the search phase of the IVFPQ on NVIDIA GPUs, the throughput can degrade due to shared memory bank conflict. To reduce the bank conflict and improve the search throughput, we propose a custom 8-bit floating point value format. This format doesn't have a sign bit and can be converted from/to FP32 with a few instructions. We use this format for IVFPQ on GPUs and achieved better performance without significant recall loss compared to FP32 and FP16.
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization

We consider the optimization problem of the form $\min_{x \in \mathbb{R}^d} f(x) \triangleq \mathbb{E}_{\xi} [F(x; \xi)]$, where the component $F(x;\xi)$ is $L$-mean-squared Lipschitz but possibly nonconvex and nonsmooth. The recently proposed gradient-free method requires at most $\mathcal{O}( L^4 d^{3/2} \epsilon^{-4} + \Delta L^3 d^{3/2} \delta^{-1} \epsilon^{-4})$ stochastic zeroth-order oracle complexity to find a $(\delta,\epsilon)$-Goldstein stationary point of objective function, where $\Delta = f(x_0) - \inf_{x \in \mathbb{R}^d} f(x)$ and $x_0$ is the initial point of the algorithm. This paper proposes a more efficient algorithm using stochastic recursive gradient estimators, which improves the complexity to $\mathcal{O}(L^3 d^{3/2} \epsilon^{-3}+ \Delta L^2 d^{3/2} \delta^{-1} \epsilon^{-3})$.
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management

Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each candidate solution. The conventional approach carries out the optimization of resource-allocation parameters for each system configuration, characterized, e.g., by topology and traffic statistics, using global search methods such as Bayesian optimization (BO). These methods tend to require a large number of iterations, and hence a large number of key performance indicator (KPI) evaluations. In this paper, we propose the use of meta-learning to transfer knowledge from data collected from related, but distinct, configurations in order to speed up optimization on new network configurations. Specifically, we combine meta-learning with BO, as well as with multi-armed bandit (MAB) optimization, with the latter having the potential advantage of operating directly on a discrete search space. Furthermore, we introduce novel contextual meta-BO and meta-MAB algorithms, in which transfer of knowledge across configurations occurs at the level of a mapping from graph-based contextual information to resource-allocation parameters. Experiments for the problem of open loop power control (OLPC) parameter optimization for the uplink of multi-cell multi-antenna systems provide insights into the potential benefits of meta-learning and contextual optimization.
Analysis and Improvements of the Sender Keys Protocol for Group Messaging

Messaging between two parties and in the group setting has enjoyed widespread attention both in practice, and, more recently, from the cryptographic community. One of the main challenges in the area is constructing secure (end-to-end encrypted) and efficient messaging protocols for group conversations. The popular messaging applications WhatsApp and Signal utilise a protocol in which, instead of sharing a single group key, members have individual \textit{sender keys}, which are shared with all other group members. The Sender Keys protocol is claimed to offer forward security guarantees. However, despite its broad adoption in practice, it has never been studied formally in the cryptographic literature. In this paper we present the first analysis of the Sender Keys protocol along with some prospective improvements. To this end, we introduce a new cryptographic primitive, develop a game-based security model, present a security analysis in the passive and active settings, and propose several improvements to the protocol.
Adaptive Neural Networks Using Residual Fitting

Current methods for estimating the required neural-network size for a given problem class have focused on methods that can be computationally intensive, such as neural-architecture search and pruning. In contrast, methods that add capacity to neural networks as needed may provide similar results to architecture search and pruning, but do not require as much computation to find an appropriate network size. Here, we present a network-growth method that searches for explainable error in the network's residuals and grows the network if sufficient error is detected. We demonstrate this method using examples from classification, imitation learning, and reinforcement learning. Within these tasks, the growing network can often achieve better performance than small networks that do not grow, and similar performance to networks that begin much larger.