Miracle-dz/UniFAR-FTUs huggingface dataset:
Supersymmetry at the crossroads: Higgs, dark matter and collider signals

Supersymmetry (SUSY) is one of the most elegant extensions of the standard model (SM) of particle physics. It solves several problems in the SM, supports the idea of grand unification and also provides a candidate for dark matter. The recent discovery of the Higgs boson is consistent with the lightest Higgs boson in Minimal Supersymmetric Standard Model. However, to date, no signals of supersymmetry were found at the LHC. The presence of the Higgs boson and the absence of supersymmetric particles at the LHC can significantly impact various SUSY models. The topic of this dissertation is to demonstrate how supersymmetry is being tested at various fronts in the era of the Large Hadron Collider (LHC). We will employ SUSY searches from the LHC to derive a model dependent limit on the supersymmetric top quark mass when it is next to the lightest to the SUSY dark matter candidate. We will also address the impact of these observations on the SUSY dark matter candidate. Furthermore, if SUSY is present at LHC accessible energies it can effect the rate at which the Higgs boson decays to some final states. We will discuss how the decay rate of the Higgs boson to two photons can be affected in the presence of SUSY and show that the supersymmetric partner of the tau particle, the stau, can account for an enhancement in this channel. One of the popular mechanisms of SUSY breaking is the minimal gauge mediated SUSY breaking model. We will show that a 125 GeV Higgs severely constrains the minimal version of this model. It implies that, with the exception of the stau, all the SUSY particles are heavy enough to evade detection at the LHC. Finally, we demonstrate that a 125 GeV Higgs boson can also hint to a particular grand unified model. We shall discuss the SO(10) grand unified model with t− b− τ Yukawa unification and show that the Higgs boson mass can be predicted in this model.
BRUSH NUMBERS OF CERTAIN MYCIELSKI GRAPHS

The concept of the brush number was introduced for a simple connected undi- rected graph G. The concept will be applied to the Mycielskian graph of a simple connected graph G to find the brush number in terms of an optimal orientation of G. We also apply the concept to a special family of directed graphs called, finite LinearJaco Graphs and describe a recursive formula for the brush number. Finally the concept is applied to the Mycielski Jaco graph in respect of an optimal orientation. Further to that, the concept of a brush centre of a simple connected graph is introduced. Because brushes themselves may be technology of kind, the technology in real world applications will normally be the subject of maintenance or calibration or virus vetting or alike. Therefore, finding a brush centre of a graph will allow for well located maintenance centres of the brushes prior to a next cycle of cleaning.
Time-resolved investigation of a high-repetition-rate gas-jet target for high-harmonic generation

High repetition rate gas targets constitute an essential component in intense laser matter interaction studies. The technology becomes challenging as the repetition rate approaches kHz regime. In this regime, cantilever based gas valves are employed, which can open and close in tens of microseconds, resulting in a unique kind of gas characteristics in both spatial and temporal domain. Here we characterize piezo cantilever based kHz pulsed gas valves in the low density regime, where it provides sufficient peak gas density for High Harmonic Generation while releasing significantly less amount of gas reducing the vacuum load within the interaction chamber, suitable for high vacuum applications. In order to obtain reliable information of the gas density in the target jet space-time resolved characterization is performed. The gas jet system is validated by conducting interferometric gas density estimations and high harmonic generation measurements at the Extreme Light Infrastructure Attosecond Light Pulse Source (ELI ALPS) facility. Our results demonstrate that while employing such targets for optimal high harmonic generation, the high intensity interaction should be confined to a suitable time window, after the cantilever opening. The measured gas density evolution correlates well with the integrated high harmonic flux and state of the art 3D simulation results, establishing the importance of such metrology.
Efficient Iterative Programs with Distributed Data Collections

Big data programming frameworks have become increasingly important for the development of applications for which performance and scalability are critical. In those complex frameworks, optimizing code by hand is hard and time-consuming, making automated optimization particularly necessary. In order to automate optimization, a prerequisite is to find suitable abstractions to represent programs; for instance, algebras based on monads or monoids to represent distributed data collections. Currently, however, such algebras do not represent recursive programs in a way which allows for analyzing or rewriting them. In this paper, we extend a monoid algebra with a fixpoint operator for representing recursion as a first class citizen and show how it enables new optimizations. Experiments with the Spark platform illustrate performance gains brought by these systematic optimizations.
Harder's conjecture II

Let $f$ be a primitive form of weight $2k+j-2$ for $SL_2(Z)$, and let $\mathfrak p$ be a prime ideal of the Hecke field of $f$. We denote by $SP_m(Z)$ the Siegel modular group of degree $m$. Suppose that $k \equiv 0 \mod 2, \ j \equiv 0 \mod 4$ and that $\mathfrak p$ divides the algebraic part of $L(k+j,f)$. Put ${\bf k}=(k+j/2,k+j/2,j/2+4,j/2+4)$. Then under certain mild conditions, we prove that there exists a Hecke eigenform $F$ in the space of modular forms of weight $(k+j,k)$ for $SP_2(Z)$ such that $[I_2(f)]^{\bf k}$ is congruent to $A^{(I)}_4(F)$ modulo $\mathfrak p$. Here, $[I_2(f)]^{\bf k}$ is the Klingen-Eisenstein lift of the Saito-Kurokawa lift $I_2(f)$ of $f$ to the space of modular forms of weight ${\bf k}$ for $SP_4(Z)$, and $A^{(I)}_4(F)$ is a certain lift of $F$ to the space of cusp forms of weight ${\bf k}$ for $SP_4(Z)$. As an application, we prove Harder's conjecture on the congruence between the Hecke eigenvalues of $F$ and some quantities related to the Hecke eigenvalues of $f$.
Globally optimized Fourier finite-difference operator using simulated annealing algorithm based on multi-parameter

Fourier finite-difference(FFD) method can handle wave propagations in the complex media with large lateral velocity contrasts and wide propagation angles.However,the FFD method has obvious error for wider angles.The optimization method can obviously improve the image accuracy of wider angles without additional computational cost.Based on the rational approximation of the FFD operator,we extend the two coefficients to be optimized to four coefficients,then we optimize them globally using simulated annealing algorithm.Besides the velocity contrast,we consider the effects of both frequency and depth interval.The theoretical analyses and the impulse responses demonstrate that our method significantly extends the accurate propagation angle of the FFD method.Migration results on the 2D SEG/EAGE salt model show that our method resolves the wider angles and the sub-salt weak reflectors more accurately than the unoptimized FFD method.Combination of the FFD method and alternative-direction-implicit plus interpolation method on 3D impulse responses further proved the validity of our method.
Multi-Platform Budget Management in Ad Markets with Non-IC Auctions

In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints. Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation. Additionally, we investigate the online setting where the advertiser must submit bids across platforms while learning about other bidders' bids over time. Our algorithm has $O(T^{3/4})$ regret under the full-information setting. Finally, we demonstrate that our algorithms have superior cumulative regret on both synthetic and real-world datasets of ad placement auctions, compared to existing adaptive pacing algorithms.
Plasmonic hierarchical nanostructures with cascaded field enhancement and their SERS applications

Plasmonic nanostructures with strong near field “hot spots” are highly demanded in many applications such as surface enhanced Raman spectroscopy (SERS). Here, we present some specially designed plasmonic hierarchical nanostructures that combine geometric features of micro- and nanoscales. Owing to the mode coupling and hybridization in these multiscale systems that can produce the cascaded field enhancement (CFE) effect, extremely strong and highly confined field hot spots can be readily generated in nanoscale volumes. Two typical hierarchical nanostructures are presented: an Mshaped grating with 30 nm narrow V-shaped grooves and a nanoparticle-in-cavity (PIC) plasmonic nanoantenna array. A cost-effective, efficient and reliable fabrication technique based on room-temperature nanoimprinting and anisotropic reactive ion etching is developed to fabricate these plasmonic hierarchical nanostructures in large area, during which the nano-features can be finely controlled and tuned. The field distributions and enhancement in the proposed structures are experimentally characterized, which agree very well with the numerical simulations. SERS experiments show the SERS enhancement factor as high as 5×108 by employing these hierarchical nanostructures as SERS substrates, which verify the strong light-matter interaction and show the great potential of these devices as low-cost and highly-active substrates for SERS applications.
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective

Off-policy Learning to Rank (LTR) aims to optimize a ranker from data collected by a deployed logging policy. However, existing off-policy learning to rank methods often make strong assumptions about how users generate the click data, i.e., the click model, and hence need to tailor their methods specifically under different click models. In this paper, we unified the ranking process under general stochastic click models as a Markov Decision Process (MDP), and the optimal ranking could be learned with offline reinforcement learning (RL) directly. Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models. Through a dedicated formulation of the MDP, we show that offline RL algorithms can adapt to various click models without complex debiasing techniques and prior knowledge of the model. Results on various large-scale datasets demonstrate that CUOLR consistently outperforms the state-of-the-art off-policy learning to rank algorithms while maintaining consistency and robustness under different click models.
Unsupervised speech enhancement with deep dynamical generative speech and noise models

This work builds on a previous work on unsupervised speech enhancement using a dynamical variational autoencoder (DVAE) as the clean speech model and non-negative matrix factorization (NMF) as the noise model. We propose to replace the NMF noise model with a deep dynamical generative model (DDGM) depending either on the DVAE latent variables, or on the noisy observations, or on both. This DDGM can be trained in three configurations: noise-agnostic, noise-dependent and noise adaptation after noise-dependent training. Experimental results show that the proposed method achieves competitive performance compared to state-of-the-art unsupervised speech enhancement methods, while the noise-dependent training configuration yields a much more time-efficient inference process.
Kernel Choice Matters for Boundary Inference Using Local Polynomial Density: With Application to Manipulation Testing

The local polynomial density (LPD) estimator has been a useful tool for inference concerning boundary points of density functions. While it is commonly believed that kernel selection is not crucial for the performance of kernel-based estimators, this paper argues that this does not hold true for LPD estimators at boundary points. We find that the commonly used kernels with compact support lead to larger asymptotic and finite-sample variances. Furthermore, we present theoretical and numerical evidence showing that such unfavorable variance properties negatively affect the performance of manipulation testing in regression discontinuity designs, which typically suffer from low power. Notably, we demonstrate that these issues of increased variance and reduced power can be significantly improved just by using a kernel function with unbounded support. We recommend the use of the spline-type kernel (the Laplace density) and illustrate its superior performance.
Temporal Gradient Inversion Attacks with Robust Optimization

Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged during FL have received significant research attention. Gradient Inversion Attacks (GIAs) have been proposed to reconstruct the private data retained by local clients from the exchanged gradients. While recovering private data, the data dimensions and the model complexity increase, which thwart data reconstruction by GIAs. Existing methods adopt prior knowledge about private data to overcome those challenges. In this paper, we first observe that GIAs with gradients from a single iteration fail to reconstruct private data due to insufficient dimensions of leaked gradients, complex model architectures, and invalid gradient information. We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients. To eliminate the negative impacts of outliers, e.g., invalid gradients for collaborative optimization, robust statistics are proposed. Theoretical guarantees on the recovery performance and robustness of TGIAs-RO against invalid gradients are also provided. Extensive empirical results on MNIST, CIFAR10, ImageNet and Reuters 21578 datasets show that the proposed TGIAs-RO with 10 temporal gradients improves reconstruction performance compared to state-of-the-art methods, even for large batch sizes (up to 128), complex models like ResNet18, and large datasets like ImageNet (224*224 pixels). Furthermore, the proposed attack method inspires further exploration of privacy-preserving methods in the context of FL.
Decoding Brain Motor Imagery with various Machine Learning techniques

Motor imagery (MI) is a well-documented technique used by subjects in BCI (Brain Computer Interface) experiments to modulate brain activity within the motor cortex and surrounding areas of the brain. In our term project, we conducted an experiment in which the subjects were instructed to perform motor imagery that would be divided into two classes (Right and Left). Experiments were conducted with two different types of electrodes (Gel and POLiTag) and data for individual subjects was collected. In this paper, we will apply different machine learning (ML) methods to create a decoder based on offline training data that uses evidence accumulation to predict a subject's intent from their modulated brain signals in real-time.
A Piecewise Prony Method for Phase Synchronization Analysis of EEG

In this study,a piecewise Prony method(PPM) was adopted to decompose the nonstationary EEG signals into a summation of sinusoidal components with decaying or growing envelope in order to obtain the magnitude of the frequency components,frequency and phase information.Then,the phase information was used to calculate difference of the phase of two-channel signal.The degree of phase synchronization of two-channel signals was measured according to Shannon entropy.The proposed method was firstly evaluated on simulated transient non-stationary data.The results showed that PPM could estimate the actual frequency and phase with high resolution.With application to three real rat EEG data groups,the phase synchronization entropy of PPM was consistent with that of the classical methods.Compared with Hilbert method,PPM based synchronization analysis was more robust to noise and non-stationary.
Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses

Neurodiversity is an umbrella term that describes variation in brain function among individuals, including conditions such as Attention deficit hyperactivity disorder (ADHD), or dyslexia. Neurodiversity is common in the general population, with an estimated 5.0% to 7.1% and 7% of the world population being diagnosed with ADHD and dyslexia respectively. Neurodivergent (ND) individuals often experience challenges in specific tasks, such as difficulties in communication or a reduced attention span in comparison to neurotypical (NT) individuals. However, they also exhibit specific strengths, such as high creativity or attention to detail. Therefore, improving the inclusion of ND individuals is desirable for economic, ethical, and for talent reasons. In higher education, struggles of ND students are well-documented. Common issues in this area are a lack of awareness among other students and staff, forms of assessment that are particularly challenging for some students, and a lack of offered accommodations. These factors commonly lead to stress, anxiety, and ultimately a risk of dropping out of the studies. Accommodations for ND students can require substantial effort. However, smaller changes in course material can already have major impact. In this chapter, we summarise the lessons learned from an intervention in four courses in undergraduate computer science programmes at Reykjavik University, Iceland, over a period of two terms. Following accessibility guidelines produced by interest groups for different ND conditions, we created course material in the form of slides and assignments specifically tailored to ND audiences. We focused on small, economical changes that could be replicated by educators with a minimal investment of time. We evaluated the success of our intervention through two surveys, showing an overall positive response among ND students and NT students.
Crossed product algebras and generalized entropy for subregions

An early result of algebraic quantum field theory is that the algebra of any subregion in a QFT is a von Neumann factor of type III$_1$, in which entropy cannot be well-defined because such algebras do not admit a trace or density states. However, associated to the algebra is a modular group of automorphisms characterizing the local dynamics of degrees of freedom in the region, and the crossed product of the algebra with its modular group yields a type II$_\infty$ factor, in which traces and hence von Neumann entropy can be well-defined. In this work, we generalize recent constructions of the crossed product algebra for the TFD to, in principle, arbitrary spacetime regions in arbitrary QFTs, paving the way to the study of entanglement entropy without UV divergences. In this sense, the crossed product construction represents a refinement of Haag's assignment of nets of observable algebras to spacetime regions by providing a natural construction of a type II factor. We present several concrete examples: a QFT in Rindler space, a CFT in an open ball of Minkowski space, and arbitrary boundary subregions in AdS/CFT. In the holographic setting, we provide a novel argument for why the bulk dual must be the entanglement wedge, and discuss the distinction arising from boundary modular flow between causal and entanglement wedges for excited states and disjoint regions.
iSLAM: Imperative SLAM

Simultaneous Localization and Mapping (SLAM) stands as one of the critical challenges in robot navigation. A SLAM system often consists of a front-end component for motion estimation and a back-end system for eliminating estimation drifts. Recent advancements suggest that data-driven methods are highly effective for front-end tasks, while geometry-based methods continue to be essential in the back-end processes. However, such a decoupled paradigm between the data-driven front-end and geometry-based back-end can lead to sub-optimal performance, consequently reducing the system's capabilities and generalization potential. To solve this problem, we proposed a novel self-supervised imperative learning framework, named imperative SLAM (iSLAM), which fosters reciprocal correction between the front-end and back-end, thus enhancing performance without necessitating any external supervision. Specifically, we formulate the SLAM problem as a bilevel optimization so that the front-end and back-end are bidirectionally connected. As a result, the front-end model can learn global geometric knowledge obtained through pose graph optimization by back-propagating the residuals from the back-end component. We showcase the effectiveness of this new framework through an application of stereo-inertial SLAM. The experiments show that the iSLAM training strategy achieves an accuracy improvement of 22% on average over a baseline model. To the best of our knowledge, iSLAM is the first SLAM system showing that the front-end and back-end components can mutually correct each other in a self-supervised manner.
Contextual Dictionary Lookup for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples, numbers of knowledge graph embedding (KGE) models have been proposed to perform KGC by learning embeddings. Nevertheless, most existing embedding models map each relation into a unique vector, overlooking the specific fine-grained semantics of them under different entities. Additionally, the few available fine-grained semantic models rely on clustering algorithms, resulting in limited performance and applicability due to the cumbersome two-stage training process. In this paper, we present a novel method utilizing contextual dictionary lookup, enabling conventional embedding models to learn fine-grained semantics of relations in an end-to-end manner. More specifically, we represent each relation using a dictionary that contains multiple latent semantics. The composition of a given entity and the dictionary's central semantics serves as the context for generating a lookup, thus determining the fine-grained semantics of the relation adaptively. The proposed loss function optimizes both the central and fine-grained semantics simultaneously to ensure their semantic consistency. Besides, we introduce two metrics to assess the validity and accuracy of the dictionary lookup operation. We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.