Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Lithium Niobate Intensity Modulator on a Silicon Handle With Slow-Wave Electrodes

Segmented, or slow-wave electrodes have emerged as an index-matching solution to improve bandwidth of traveling-wave Mach Zehnder (MZM) and phase modulators on the thin-film lithium niobate on insulator platform. However, these devices require the use of a quartz handle or substrate removal, adding cost and additional processing. In this work, a high-speed dual-output electro-optic intensity modulator in the thin-film silicon nitride and lithium niobate material system that uses segmented electrodes for RF and optical index matching is presented. The device uses a silicon handle and does not require substrate removal. A silicon handle allows the use of larger wafer sizes to increase yield, and lends itself to processing in established silicon foundries that may not have the capability to process a quartz or fused silica wafer. The balanced MZM has an interaction region of 10 mm, shows a DC half wave voltage of 3.75 V with a 92 mW $\text{P}\pi $ , an ultra-high extinction ratio of roughly 45 dB consistent with previous work, and a fiber-to-fiber insertion loss of 7.47 dB with a 95 GHz 3 dB bandwidth.
Consistent discretization of finite/fixed-time controllers

The paper proposes an algorithm for a discretization (sampled-time implementation) of a homogeneous control preserving the finite-time and nearly fixed-time stability property of the original (sampling-free) system. The sampling period is assumed to be constant. Both single-input and multiple-input cases are considered. The robustness (Input-to-State Stability) of the obtained sampled-time control system is studied as well. Theoretical results are supported by numerical simulations.
Mechanisms of True and False Rumor Sharing in Social Media: Wisdom-of-Crowds or Herd Behavior?

Social media platforms disseminate extensive volumes of online content, including true and, in particular, false rumors. Previous literature has studied the diffusion of offline rumors, yet more research is needed to understand the diffusion of online rumors. In this paper, we examine the role of lifetime and crowd effects in social media sharing behavior for true vs. false rumors. Based on 126 , 301 Twitter cascades, we find that the sharing behavior is characterized by lifetime and crowd effects that explain differences in the spread of true as opposed to false rumors. All else equal, we find that a longer lifetime is associated with less sharing activities, yet the reduction in sharing is larger for false than for true rumors. Hence, lifetime is an important determinant explaining why false rumors die out. Furthermore, we find that the spread of false rumors is characterized by herding tendencies (rather than wisdom of crowds), whereby the spread of false rumors becomes proliferated at a larger retweet depth. These findings explain differences in the diffusion dynamics of true and false rumors and further offer practical implications for social media platforms. are important determinants for explaining the underlying mechanism of sharing behavior. We find that the extent to which false and true rumors spread differently depending on the lifetime is stronger for rumor cascades embedding negative emotions. In contrast, we observe a higher difference in the effect of retweet depth for false vs. true rumor cascades embedding positive emotions. Here the combination of false veracity and replies conveying positive emotions is relatively more viral at higher depths.
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data

Small molecules in biological samples are studied to provide information about disease states, environmental toxins, natural product drug discovery, and many other applications. The primary window into the composition of small molecule mixtures is tandem mass spectrometry (MS2), which produces data that are of high sensitivity and part per million resolution. We adopt multi-scale sinusoidal embeddings of the mass data in MS2 designed to meet the challenge of learning from the full resolution of MS2 data. Using these embeddings, we provide a new state of the art model for spectral library search, the standard task for initial evaluation of MS2 data. We also introduce a new task, chemical property prediction from MS2 data, that has natural applications in high-throughput MS2 experiments and show that an average $R^2$ of 80\% for novel compounds can be achieved across 10 chemical properties prioritized by medicinal chemists. We use dimensionality reduction techniques and experiments with different floating point resolutions to show the essential role multi-scale sinusoidal embeddings play in learning from MS2 data.
Cooperative Backscatter NOMA with Imperfect SIC: Towards Energy Efficient Sum Rate Maximization in Sustainable 6G Networks

The combination of backscatter communication with non-orthogonal multiple access (NOMA) has the potential to support low-powered massive connections in upcoming sixth-generation (6G) wireless networks. More specifically, backscatter communication can harvest and use the existing RF signals in the atmosphere for communication, while NOMA provides communication to multiple wireless devices over the same frequency and time resources. This paper has proposed a new resource management framework for backscatter-aided cooperative NOMA communication in upcoming 6G networks. In particular, the proposed work has simultaneously optimized the base station's transmit power, relaying node, the reflection coefficient of the backscatter tag, and time allocation under imperfect successive interference cancellation to maximize the sum rate of the system. To obtain an efficient solution for the resource management framework, we have proposed a combination of the bisection method and dual theory, where the sub-gradient method is adopted to optimize the Lagrangian multipliers. Numerical results have shown that the proposed solution provides excellent performance. When the performance of the proposed technique is compared to a brute-forcing search technique that guarantees optimal solution however, is very time-consuming, it was seen that the gap in performance is actually 0\%. Hence, the proposed framework has provided performance equal to a cumbersome brute-force search technique while offering much less complexity. The works in the literature on cooperative NOMA considered equal time distribution for cooperation and direct communication. Our results showed that optimizing the time-division can increase the performance by more than 110\% for high transmission powers.
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

We study federated contextual linear bandits, where $M$ agents cooperate with each other to solve a global contextual linear bandit problem with the help of a central server. We consider the asynchronous setting, where all agents work independently and the communication between one agent and the server will not trigger other agents' communication. We propose a simple algorithm named \texttt{FedLinUCB} based on the principle of optimism. We prove that the regret of \texttt{FedLinUCB} is bounded by $\tilde{O}(d\sqrt{\sum_{m=1}^M T_m})$ and the communication complexity is $\tilde{O}(dM^2)$, where $d$ is the dimension of the contextual vector and $T_m$ is the total number of interactions with the environment by $m$-th agent. To the best of our knowledge, this is the first provably efficient algorithm that allows fully asynchronous communication for federated contextual linear bandits, while achieving the same regret guarantee as in the single-agent setting.
Some results on locally repairable codes with minimum distance 7 and locality 2

Locally repairable codes(LRCs) play important roles in distributed storage systems(DSS). LRCs with small locality have their own advantages since fewer available symbols are needed in the recovery of erased symbols. In this paper, we prove an upper bound on the dimension of LRCs with minimum distance $d\geq 7$. An upper bound on the length of almost optimal LRCs with $d=7$, $r=2$ at $q^2+q+3$ is proved. Then based on the $t$-spread structure, we give an algorithm to construct almost optimal LRCs with $d=7$, $r=2$ and length $n\geq 3\lceil\frac{\sqrt{2}q}{3}\rceil$ when $q\geq 4$, whose dimension attains the aforementioned upper bound.
Model Selection in Reinforcement Learning with General Function Approximations

We consider model selection for classic Reinforcement Learning (RL) environments -- Multi Armed Bandits (MABs) and Markov Decision Processes (MDPs) -- under general function approximations. In the model selection framework, we do not know the function classes, denoted by $\mathcal{F}$ and $\mathcal{M}$, where the true models -- reward generating function for MABs and and transition kernel for MDPs -- lie, respectively. Instead, we are given $M$ nested function (hypothesis) classes such that true models are contained in at-least one such class. In this paper, we propose and analyze efficient model selection algorithms for MABs and MDPs, that \emph{adapt} to the smallest function class (among the nested $M$ classes) containing the true underlying model. Under a separability assumption on the nested hypothesis classes, we show that the cumulative regret of our adaptive algorithms match to that of an oracle which knows the correct function classes (i.e., $\cF$ and $\cM$) a priori. Furthermore, for both the settings, we show that the cost of model selection is an additive term in the regret having weak (logarithmic) dependence on the learning horizon $T$.
Shock Breakout from Stellar Envelopes: The Relativistic Limit

We calculate the observed luminosity and spectrum following the emergence of a relativistic shock wave from a stellar edge. Shock waves propagating at 0.6 < Γsh β sh, where Γsh is the shock Lorentz factor, and β sh is its associated reduced velocity, heat the stellar envelope to temperatures exceeding ∼50 keV, allowing for a vigorous production of electron and positron pairs. Pairs significantly increase the electron-scattering optical depth and regulate the temperature through photon generation, producing distinct observational signatures in the escaping emission. Assuming Wien equilibrium, we find analytic expressions for the temperature and pair density profiles in the envelope immediately after shock passage, and compute the emission during the expansion phase. Our analysis shows that, in pair-loaded regions, photons are produced at a roughly uniform rest-frame energy of ∼200 keV, and reinforce previous estimates that the shock breakout signal will be detected as a short burst of energetic γ-ray photons, followed by a longer phase of X-ray emission. We test our model on a sample of low-luminosity gamma-ray bursts using a closure relation between the γ-ray burst duration, the radiation temperature, and the γ-ray isotropic equivalent energy, and find that some of the events are consistent with the relativistic shock breakout model. Finally, we apply our results to explosions in white dwarfs and neutron stars, and find that typical type Ia supernovae emit ∼1041 erg in the form of ∼1 MeV photons.
Imaging techniques in infl ammatory bowel disease : recent trends , questions and answers Imagerie des maladies infl ammatoires chroniques

Imaging techniques have undergone substantial progress in recent years and contribute significantly in the diagnosis of inflammatory bowel diseases in conjunction with patient history, clinical and laboratory examination. Modern cross-sectional imaging modalities such as computed tomography and magnetic resonance imaging allow an evaluation not only of the complete bowel wall of the small intestine, but also of extraluminal structures. They constitute a major diagnostic component in the initial workup, in stricturing or fistulizing disease and in suspected abscess. Transabdominal ultrasonography has been re-appreciated in these settings as an easyand ready-to-use tool yielding real-time information. Positron emission tomography was found useful to add functional diagnosis of inflammation. Colonoscopy techniques still represent the gold standard for evaluation of inflammatory activity and for cancer surveillance. Here, chromoendoscopy has proven efficacy for enhanced detection of flat intraepithelial neoplasias in ulcerative colitis and has been incorporated into recent surveillance guidelines. Narrow band imaging may provide virtual chromoendoscopy in the future, but confirmatory studies are still on the way. Confocal endomicroscopy allows in vivo microscopy at high resolution and with excellent accuracy in first trials to predict histology of inflammatory and neoplastic lesions. The current data from endoscopic studies should result in an integrated approach to both identify and characterize a suspicious lesion during ongoing endoscopy for reliable, accurate diagnosis and targeted, immediate therapy. © 2009 Elsevier Masson SAS. All rights reserved. Imaging techniques in infl ammatory bowel disease: recent trends, questions and answers Imagerie des maladies infl ammatoires chroniques intestinales
Unsupervised Manifold Alignment with Joint Multidimensional Scaling

We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences between data instances across the datasets, to a common low-dimensional Euclidean space. Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between instances from two different datasets, while only requiring intra-dataset pairwise dissimilarities as input. This unique characteristic makes our approach applicable to datasets without access to the input features, such as solving the inexact graph matching problem. We propose an alternating optimization scheme to solve the problem that can fully benefit from the optimization techniques for MDS and Wasserstein Procrustes. We demonstrate the effectiveness of our approach in several applications, including joint visualization of two datasets, unsupervised heterogeneous domain adaptation, graph matching, and protein structure alignment. The implementation of our work is available at https://github.com/BorgwardtLab/JointMDS
MCTS with Refinement for Proposals Selection Games in Scene Understanding

We propose a novel method applicable in many scene understanding problems that adapts the Monte Carlo Tree Search (MCTS) algorithm, originally designed to learn to play games of high-state complexity. From a generated pool of proposals, our method jointly selects and optimizes proposals that minimize the objective term. In our first application for floor plan reconstruction from point clouds, our method selects and refines the room proposals, modelled as 2D polygons, by optimizing on an objective function combining the fitness as predicted by a deep network and regularizing terms on the room shapes. We also introduce a novel differentiable method for rendering the polygonal shapes of these proposals. Our evaluations on the recent and challenging Structured3D and Floor-SP datasets show significant improvements over the state-of-the-art both in speed and quality of reconstructions, without imposing hard constraints nor assumptions on the floor plan configurations. In our second application, we extend our approach to reconstruct general 3D room layouts from a color image and obtain accurate room layouts. We also show that our differentiable renderer can easily be extended for rendering 3D planar polygons and polygon embeddings. Our method shows high performance on the Matterport3D-Layout dataset, without introducing hard constraints on room layout configurations.
Multi-Label Learning to Rank through Multi-Objective Optimization

Learning to Rank (LTR) technique is ubiquitous in Information Retrieval systems, especially in search ranking applications. The relevance labels used to train ranking models are often noisy measurements of human behavior, such as product ratings in product searches. This results in non-unique ground truth rankings and ambiguity. To address this, Multi-Label LTR (MLLTR) is used to train models using multiple relevance criteria, capturing conflicting but important goals, such as product quality and purchase likelihood for improved revenue in product searches. This research leverages Multi-Objective Optimization (MOO) in MLLTR and employs modern MOO algorithms to solve the problem. A general framework is proposed to combine label information to characterize trade-offs among goals, and allows for the use of gradient-based MOO algorithms. We test the proposed framework on four publicly available LTR datasets and one E-commerce dataset to show its efficacy.
Extending Logical Neural Networks using First-Order Theories

Logical Neural Networks (LNNs) are a type of architecture which combine a neural network’s abilities to learn and systems of formal logic’s abilities to perform symbolic reasoning. LLNs provide programmers the ability to implicitly modify the underlying structure of the neural network via logical formulae. In this paper, we take advantage of this abstraction to extend LNNs to support equality and function symbols via first-order theories. This extension improves the power of LNNs by significantly increasing the types of problems they can tackle. As a proof of concept, we add support for the first-order theory of equality to IBM’s LNN library and demonstrate how the introduction of this allows the LNN library to now reason about expressions without needing to make the unique-names assumption.
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration

Night imaging with modern smartphone cameras is troublesome due to low photon count and unavoidable noise in the imaging system. Directly adjusting exposure time and ISO ratings cannot obtain sharp and noise-free images at the same time in low-light conditions. Though many methods have been proposed to enhance noisy or blurry night images, their performances on real-world night photos are still unsatisfactory due to two main reasons: 1) Limited information in a single image and 2) Domain gap between synthetic training images and real-world photos (e.g., differences in blur area and resolution). To exploit the information from successive long- and short-exposure images, we propose a learning-based pipeline to fuse them. A D2HNet framework is developed to recover a high-quality image by deblurring and enhancing a long-exposure image under the guidance of a short-exposure image. To shrink the domain gap, we leverage a two-phase DeblurNet-EnhanceNet architecture, which performs accurate blur removal on a fixed low resolution so that it is able to handle large ranges of blur in different resolution inputs. In addition, we synthesize a D2-Dataset from HD videos and experiment on it. The results on the validation set and real photos demonstrate our methods achieve better visual quality and state-of-the-art quantitative scores. The D2HNet codes and D2-Dataset can be found at https://github.com/zhaoyuzhi/D2HNet.
Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes

We design a novel calibration procedure that is designed to handle the specific characteristics of options on cryptocurrency markets, namely large bid-ask spreads and the possibility of missing or incoherent prices in the considered data sets. We show that this calibration procedure is significantly more robust and accurate than the ordinary one based on trade and mid-prices.
Improving Spectral Clustering Using Spectrum-Preserving Node Aggregation

Spectral clustering is an important graph clustering technique. However, it suffers from a scalability problem due to the involved computationally expensive eigen-decomposition procedure and is highly sensitive to noisy nodes in the graph. In this work we solve the two problems simultaneously by using spectrum-preserving node aggregation to generate a nearly-noiseless concise representation of the data set. Specifically, we create a small set of spectral-representative pseudo-nodes based on spectral analysis. Then, standard spectral clustering algorithm is performed on the smaller node set. Finally, we map the clusters of pseudo nodes back to find the cluster memberships of the data points in the original data set. The proposed framework has a nearly-linear time complexity. Meanwhile, the clustering accuracy can be significantly improved. The experimental results show dramatically improved clustering performance when compared with state-of-the-art methods.
How social networks inﬂuence human behavior: An integrated latent space approach for social inﬂuence

Abstract How human behavior is inﬂuenced by their social network has been an interested topic in applied research. Existing methods often utilized scale-level behavioral data to estimate the inﬂuence of a social network on human behavior. This study proposes a novel approach to studying social inﬂuence by using item-level behavioral measures. Under the latent space modeling framework, we integrate the two interaction maps for respondents’ social network data and item-level behavior measures. We then measure social inﬂuence as the impact of the interaction map conﬁguration contributed by the social network data on the behavior data. The performance and properties of the proposed approach are evaluated via simulation studies. We apply the proposed model to an empirical dataset to explain how students’ friendship network inﬂuences their participation in school activities.
Cadmium Bio-Accumulation and the Associated Biomarkers in Edible Frog Species (Hoplobatrachus Occipitalis) in Ibadan, Oyo State, Nigeria

The spate of natural emissions and anthropogenic activities has comparatively increased cadmium pollution in recent times. This has also increased the attendant hazardous implication on both the aquatic and terrestrial ecosystems. In this study, a total of 50 edible frog species (Hoplobatrachus occipitalis) sourced from the Ogunpa river in Ibadan, Oyo state were sampled. Atomic Absorption Spectrophotometry (AAS) was used for the evaluation of the blood, kidney and liver cadmium level. The frogs were grouped into Below Permissible Limit (BPL) and Above Permissible Limit (APL) groups using the FAO/WHO cadmium permissible level of 0.5mg/kg. 86% of the sampled frogs had blood cadmium level above the permissible limit while the liver and kidney cadmium levels exceeded the permissible limits in all the frogs. The highest cadmium level was detected in the liver (3.02±1.23 mg/kg). The erythrocyte parameters were significantly lower in the APL compared to the BPL group while the leucocyte parameters were higher in the APL than the BPL group. The histopathological lesions were consistent with pathological changes associated with renotoxic, hepatotoxic and reproductive features of cadmium toxicity. The study highlights the elevated cadmium levels in the tissue of the frog as a biomarker of exposure while the haematological and histopathological changes served as biomarkers of effect associated with cadmium toxicity in naturally exposed frogs. It also serves to underscore the importance of frogs as important sentinels of environmental cadmium toxicity, creation of public health awareness for cadmium toxicity and the evaluation of cadmium toxicity in the ecosystem.
O ct 2 00 4 Mechanisms of Electrical Conductivity in Y

Systematic studies of transport properties in deoxygenated Y1−xCaxBa2Cu3O6.1 series allowed to propose a diagram of conductivity mechanisms for this system. At intermediate temperature a variable range hopping (VRH) in 2 dimensions prevails. At lower temperature VRH in the presence of a Coulomb gap for smaller x and VRH in 2 dimensions for larger x are found. In a vicinity of superconductivity we observe conductivity proportional to √ T . Thermally activated conductivity dominates at higher temperature. This diagram may be universal for the whole family of undoped high Tc related cuprates.