Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction

Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, limited labeled data often hinders the application of deep learning in this setting. Meanwhile advances in meta-learning have enabled state-of-the-art performances in few-shot learning benchmarks, naturally prompting the question: Can meta-learning improve deep learning performance in low-resource drug discovery projects? In this work, we assess the transferability of graph neural networks initializations learned by the Model-Agnostic Meta-Learning (MAML) algorithm - and its variants FO-MAML and ANIL - for chemical properties and activities tasks. Using the ChEMBL20 dataset to emulate low-resource settings, our benchmark shows that meta-initializations perform comparably to or outperform multi-task pre-training baselines on 16 out of 20 in-distribution tasks and on all out-of-distribution tasks, providing an average improvement in AUPRC of 11.2% and 26.9% respectively. Finally, we observe that meta-initializations consistently result in the best performing models across fine-tuning sets with $k \in \{16, 32, 64, 128, 256\}$ instances.
Robust Machine Learning for Encrypted Traffic Classification

Desktops and laptops can be maliciously exploited to violate privacy. In this paper, we consider the daily battle between the passive attacker who is targeting a specific user against a user that may be adversarial opponent. In this scenario, while the attacker tries to choose the best vector attack by surreptitiously monitoring the victims encrypted network traffic in order to identify users parameters such as the Operating System (OS), browser and apps. The user may use tools such as a Virtual Private Network (VPN) or even change protocols parameters to protect his/her privacy. We provide a large dataset of more than 20,000 examples for this task. We run a comprehensive set of experiments, that achieves high (above 85) classification accuracy, robustness and resilience to changes of features as a function of different network conditions at test time. We also show the effect of a small training set on the accuracy.
Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos

Augmented reality (AR) overlays digital content onto the reality. In AR system, correct and precise estimations of user's visual fixations and head movements can enhance the quality of experience by allocating more computation resources on the areas of interest. However, there is inadequate research about understanding the visual exploration of users when using an AR system or modeling AR visual attention. To bridge the gap between the saliency prediction on real-world scene and on scene augmented by virtual information, we construct the ARVR saliency dataset with 12 diverse videos viewed by 20 people. The virtual reality (VR) technique is employed to simulate the real-world. Annotations of object recognition and tracking as augmented contents are blended into the omnidirectional videos. The saliency annotations of head and eye movements for both original and augmented videos are collected and together constitute the ARVR dataset. We also design a model which is capable of solving the saliency prediction problem in AR. Local block images are extracted to simulate the viewport and offset the projection distortion. Conspicuous visual cues in local viewports are extracted to constitute the spatial features. The optical flow information is estimated as the important temporal feature. We also consider the interplay between virtual information and reality. The composition of the augmentation information is distinguished, and the joint effects of adversarial augmentation and complementary augmentation are estimated. We generate a graph by taking each block image as one node. Both the visual saliency mechanism and the characteristics of viewing behaviors are considered in the computation of edge weights on the graph which are interpreted as Markov chains. The fraction of the visual attention that is diverted to each block image is estimated through equilibrium distribution on of this chain.
Non-uniform Geometric Set Cover and Scheduling on Multiple Machines

We consider the following general scheduling problem studied recently by Moseley. There are $n$ jobs, all released at time $0$, where job $j$ has size $p_j$ and an associated arbitrary non-decreasing cost function $f_j$ of its completion time. The goal is to find a schedule on $m$ machines with minimum total cost. We give an $O(1)$ approximation for the problem, improving upon the previous $O(\log \log nP)$ bound ($P$ is the maximum to minimum size ratio), and resolving the open question of Moseley. We first note that the scheduling problem can be reduced to a clean geometric set cover problem where points on a line with arbitrary demands, must be covered by a minimum cost collection of given intervals with non-uniform capacity profiles. Unfortunately, current techniques for such problems based on knapsack cover inequalities and low union complexity, completely lose the geometric structure in the non-uniform capacity profiles and incur at least an $\Omega(\log\log P)$ loss. To this end, we consider general covering problems with non-uniform capacities, and give a new method to handle capacities in a way that completely preserves their geometric structure. This allows us to use sophisticated geometric ideas in a black-box way to avoid the $\Omega(\log \log P)$ loss in previous approaches. In addition to the scheduling problem above, we use this approach to obtain $O(1)$ or inverse Ackermann type bounds for several basic capacitated covering problems.
A polynomial time 12-approximation algorithm for restricted Santa Claus problem {\em (Subsequently a 4-Approximation Algorithm)

We consider the restricted case of the Santa Claus problem and improve the current best approximation ratio by presenting a polynomial time 12-approximation algorithm using linear programming and novel quadratic program that we consider. Our algorithm starts by solving the configuration LP and uses the optimum value to get a 12-gap instance. This is then followed by the well-known clustering technique of Bansal and Sviridenko\cite{bansal}. We then apply the analysis of Asadpour \textit{et al.} \cite{AFS,AFS2} to show that the clustered instance has an integer solution which is at least $\frac{1}{6}$ times the best possible value, which was computed by solving the configuration LP. From this solution we find a solution to a new quadratic program that we introduce to select one machine from each cluster, and then we show that the resulting instance has an Assignment LP fractional solution of value at least $\frac{1}{6}$ times the optimum. We then use the well known rounding technique due to Bezakova and Dani \cite{bezakova} on the 12-gap instance to get our 12-approximate solution. {\em More recently, we show that our technique gives a 4-approximation algorithm. We consider the 4-gap instance and the solution to the clustered instance obtained by Bansal and Sviridenko \cite{bansal}. We show that the soltuion can be used to select a single machine from each cluster so that the resulting assignment LP instance has a fractional solution of value at least the $\frac{1}{2}$ times the optimum. Using the rounding technique in Bezakova and Dani \cite{bezakova} we get our 4-approximate solution in polynomial time}
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation

In industrial product quality assessment, it is essential to determine whether a product is defect-free and further analyze the severity of anomality. To this end, accurate defect segmentation on images of products provides an important functionality. In industrial inspection tasks, it is common to capture abundant defect-free image samples but very limited anomalous ones. Therefore, it is critical to develop automatic and accurate defect segmentation systems using only a small number of annotated anomalous training images. This paper tackles the challenging few-shot defect segmentation task with sufficient normal (defect-free) training images but very few anomalous ones. We present two effective regularization techniques via incorporating abundant defect-free images into the training of a UNet-like encoder-decoder defect segmentation network. We first propose a Normal Background Regularization (NBR) loss which is jointly minimized with the segmentation loss, enhancing the encoder network to produce distinctive representations for normal regions. Secondly, we crop/paste defective regions to the randomly selected normal images for data augmentation and propose a weighted binary cross-entropy loss to enhance the training by emphasizing more realistic crop-and-pasted augmented images based on feature-level similarity comparison. Both techniques are implemented on an encoder-decoder segmentation network backboned by ResNet-34 for few-shot defect segmentation. Extensive experiments are conducted on the recently released MVTec Anomaly Detection dataset with high-resolution industrial images. Under both 1-shot and 5-shot defect segmentation settings, the proposed method significantly outperforms several benchmarking methods.
Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments

This research aims at developing path and motion planning algorithms for a tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated primary robot in unstructured or confined environments. The emerging state of the practice for nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the sensors by providing an external viewpoint. However, the benefits of using an assistant have been limited for at least three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground robot assistants are considered, ignoring the rapid evolution of small unmanned aerial systems for indoor flying, (3) introducing a whole crew for the second teleoperated robot is not cost effective, may introduce further teamwork demands, and therefore could lead to miscommunication. This dissertation proposes to use an autonomous tethered aerial visual assistant to replace the secondary robot and its operating crew. Along with a pre-established theory of viewpoint quality based on affordances, this dissertation aims at defining and representing robot motion risk in unstructured or confined environments. Based on those theories, a novel high level path planning algorithm is developed to enable risk-aware planning, which balances the tradeoff between viewpoint quality and motion risk in order to provide safe and trustworthy visual assistance flight. The planned flight trajectory is then realized on a tethered UAV platform. The perception and actuation are tailored to fit the tethered agent in the form of a low level motion suite, including a novel tether-based localization model with negligible computational overhead, motion primitives for the tethered airframe based on position and velocity control, and two different approaches to negotiate tether with complex obstacle-occupied environments. The proposed research provides a formal reasoning of motion risk in unstructured or confined spaces, contributes to the field of risk-aware planning with a versatile planner, and opens up a new regime of indoor UAV navigation: tethered indoor flight to ensure battery du-
Covariant Quantum Error Correcting Codes via Reference Frames

Error correcting codes with a universal set of transversal gates are the desiderata of realising quantum computing. Such codes, however, are ruled out by the Eastin-Knill theorem. Moreover, it also rules out codes which are covariant with respect to the action of transversal unitary operations forming continuous symmetries. In this work, starting from an arbitrary code, we construct approximate codes which are covariant with respect to local $SU(d)$ symmetries using quantum reference frames. We show that our codes are capable of efficiently correcting different types of erasure errors. When only a small fraction of the $n$ qudits upon which the code is built are erased, our covariant code has an error that scales as $1/n^2$, which is reminiscent of the Heisenberg limit of quantum metrology. When every qudit has a chance of being erased, our covariant code has an error that scales as $1/n$. We show that the error scaling is optimal in both cases. Our approach has implications for fault-tolerant quantum computing, reference frame error correction, and the AdS-CFT duality.
Assembly properties of fluorescein-labeled tubulin in vitro before and after fluorescence bleaching

Brain tubulin has been conjugated with dichlorotriazinyl- aminofluorescein (DTAF) to form a visualizable complex for the study of tubulin dynamics in living cells. By using several assays we confirm the finding of Keith et al. (Keith, C. H., J. R. Feramisco, and M. Shelanski, 1981, J. Cell Biol., 88:234-240) that DTAF-tubulin polymerizes like control tubulin in vitro. The fluorescein moiety of the complex is readily bleached by the 488-nm line from an argon ion laser. When irradiations are performed over short times (less than 1 s) and in the presence of 2 mM glutathione, a mixture of DTAF-tubulin and control protein (as occurs after microinjection of the fluorescent conjugate into living cells) will retain full polymerization activity. Slow bleaching (approximately 5 min) or bleaching without glutathione promotes formation of covalent cross-links between neighboring polypeptides and kills the polymerization activity of DTAF-tubulin, including some molecules that are neither cross-linked nor bleached. Even under conditions that damage DTAF-tubulin, however, DTAF- microtubules are not destroyed by bleaching. They will continue to elongate by addition of DTAF-tubulin subunits to their free ends, and they neither bind nor exchange subunits along their lateral surfaces. These results suggest that DTAF-tubulin is a suitable analog for tubulin, both in studies of protein incorporation and for investigations of fluorescence redistribution after photobleaching.
Two new species of long-fingered frogs of the genus Cardioglossa (Anura: Arthroleptidae) from Central African rainforests

Abstract We describe two new frog species of Cardioglossa (Family Arthroleptidae) from Central Africa. The new species are found in the rainforests of western-central Democratic Republic of the Congo and the Republic of the Congo near the Gabonese border, respectively. We demonstrate that these species are morphologically and genetically distinct from each other and all other species of Cardioglossa. Both new species lack the dorsal hour-glass pattern present in many species of the genus, but they can be distinguished from each other and related species by distinctive colour patterns on their lateral surfaces and extremities. Both new species most closely resemble C. gratiosa, which occurs in the Atlantic coastal forests extending from Cameroon through Gabon. The new species can be differentiated from C. gratiosa by the absence of black transverse bars on all limbs or by distinctive lateral colouration. Analysis of mitochondrial ribosomal 16S DNA sequences reveals low to moderate levels (1.9–7.5%) of divergence between these new species and closely related species of Cardioglossa. The floodplains of the Congo and Ubangi Rivers may be important geographic barriers for many of these species. The occurrence of these two new lowland species in the Congo Basin reveals that the distribution and diversity of Cardioglossa in this region was underestimated. In addition, we elevate C. nigromaculata inornata to species-level status, based in part on newly available colour photographs from 1950 of specimens from the only known locality.
1.4‐mJ High Energy Terahertz Radiation from Lithium Niobates

Free‐space super‐strong terahertz (THz) electromagnetic fields offer multifaceted capabilities for reaching extreme nonlinear THz optics. However, the lack of powerful solid‐state THz sources with single pulse energy >1 mJ is impeding the proliferation of extreme THz applications. The fundamental challenge lies in hard to achieve high efficiency due to high intensity pumping caused crystal damage, linear absorption, and nonlinear distortion induced short effective interaction length, and so on. Here, through cryogenically cooling the crystals, tailoring the pump laser spectra, chirping the pump pulses, and magnifying the laser energies, 1.4‐mJ THz pulses are successfully realized in lithium niobates under the excitation of 214‐mJ femtosecond laser pulses via tilted pulse front technique. The 800 nm‐to‐THz energy conversion efficiency reaches 0.7%, and a free‐space THz peak electric and magnetic field reaches 6.3 MV cm−1 and 2.1 Tesla. Numerical simulations reproduce the experimental optimization processes. To show the capability of this super‐strong THz source, nonlinear absorption in high conductive silicon induced by strong THz electric field is demonstrated. Such a high‐energy THz source with a relatively low peak frequency is very appropriate not only for electron acceleration toward table‐top X‐ray sources but also for extreme THz science and nonlinear applications.
Improving the Sensitivity of Planar Fabry–Pérot Cavities via Adaptive Optics and Mode Filtering

Fabry–Pérot (FP) cavities are fundamental and ubiquitous optical elements frequently used in various sensing applications. Here, the authors introduce a general theoretical framework to study arbitrary light–cavity mode interactions for planar FPs and show how optical aberrations, intrinsic to the interrogating beam or due to imperfect cavities, reduce optical sensitivity by exciting higher‐order spatial modes in the cavity. It is found that particular Zernike aberrations play a dominant role in sensitivity degradation, and that the general loss of sensitivity can be significantly recovered by appropriate wavefront correction or mode filtering. The authors then demonstrate their theoretical findings also experimentally and show that in practice the sensitivity of realistic planar FP micro‐cavity sensors can be improved up to threefold by a synergistic combination of adaptive optics and passive mode filtering.
Monochromatic Triangles, Triangle Listing and APSP

All-Pairs Shortest Paths (APSP) is one of the most basic problems in graph algorithms. Given an $n$-node directed or undirected graph with integer weights in $\{-n^{c}, \ldots, n^{c}\}$ and no negative cycles, APSP asks to compute the shortest paths distance between every pair of vertices. The fastest known algorithm for APSP runs in $n^{3}/2^{\Theta(\sqrt{\log n})}$ time [Williams'14], and no truly subcubic time algorithms are known. One of the main hypotheses in fine-grained complexity is that APSP requires $n^{3-o(1)}$ time. Another famous hypothesis in fine-grained complexity is that the 3SUM problem for $n$ integers (which can be solved in $O(n^{2})$ time) requires $n^{2-o(1)}$ time. Although there are no direct reductions between 3SUM and APSP, it is known that they are related: the (min, +)-convolution problem reduces in a fine-grained way to both, and both fine-grained reduce to the Exact Triangle problem. In this paper we find more relationships between these two problems and other basic problems. Pătraşcu had shown that under the 3SUM hypothesis the All-Edges Sparse Triangle problem in $m$-edge graphs requires $m^{4/3-o(1)}$ time. The latter problem asks to determine for every edge $e$, whether $e$ is in a triangle. It is equivalent to the problem of listing $m$ triangles in an $m$-edge graph where $m=-\tilde{O}(n^{1.5})$, and can be solved in $O(m^{1.41})$ time [Alon et al.'97] with the current matrix multiplication bounds, and in $\tilde{O}(m^{4/3})$ time if $\omega=2$. We show that one can reduce Exact Triangle to All-Edges Sparse Triangle, showing that All-Edges Sparse Triangle (and hence Triangle Listing) requires $m^{4/3-o(1)}$ time also assuming the APSP hypothesis. This allows us to provide APSP-hardness for many dynamic problems that were previously known to be hard under the 3SUM hypothesis. We also consider the All-Edges Monochromatic Triangle problem. Via work of [Lincoln et al.'20], our result on All-Edges Sparse Triangle implies that if the All-Edges Monochromatic Triangle problem has an $O(n^{2.5-\varepsilon})$ time algorithm for $\epsilon > 0$, then both the APSP and 3SUM hypotheses are false. The fastest algorithm for All-Edges Monochromatic Triangle runs in $\tilde{O}(n^{(3+\omega)/2})$ time [Vassilevska et al.'06], and our new reduction shows that if $\omega=2$, this algorithm is best possible, unless 3SUM or APSP can be solved faster. Besides 3SUM, previously the only problems known to be fine-grained reducible to All-Edges Monochromatic Triangle were the seemingly easier problems directed unweighted APSP and Min-Witness Product [Lincoln et al.'20]. Our reduction shows that this problem is much harder. We also connect the problem to other “intermediate” problems, whose runtimes are between $O(n^{\omega})$ and $O(n^{3})$, such as the Max-Min product problem.
On certain properties of the class $U(\lambda)$

Let ${\mathcal A}$ be the class of functions analytic in the unit disk ${\mathbb D} := \{ z\in {\mathbb C}:\, |z| < 1 \}$ and normalized such that $f(z)=z+a_2z^2+a_3z^3+\cdots$. In this paper we study the class $\mathcal{U}(\lambda)$, $0<\lambda \leq1$, consisting of functions $f$ from ${\mathcal{A}}$ satisfying \[\left|\left(\frac{z}{f(z)}\right)^2f'(z)-1\right| < \lambda \quad (z\in {\mathbb D}). \] and give results regarding the Zalcman Conjecture, the generalised Zalcman conjecture, the Krushkal inequality and the second and third order Hankel determinant.
Energy-Efficient Non-Orthogonal Multicast and Unicast Transmission of Cell-Free Massive MIMO Systems With SWIPT

This work investigates the energy-efficient resource allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission in cell-free massive multiple-input multiple-output (MIMO) systems, where each user equipment (UE) performs wireless information and power transfer simultaneously. To begin with, the achievable data rates for multicast and unicast services are derived in closed form, as well as the received radio frequency (RF) power at each UE. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem with complex constraints. To suit the massive access setting, a first-order algorithm is developed to find both initial feasible point and the nearly optimal solution. Moreover, an accelerated algorithm is designed to improve the convergence speed. Numerical results demonstrate that the proposed first-order algorithms can achieve almost the same EE as that of second-order approaches yet with much lower computational complexity, which provides insight into the superiority of the proposed algorithms for massive access in cell-free massive MIMO systems.