Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Chameleon Algorithm for Cluster Analysis

The Chameleon algorithm is good at discovering arbitrary shape Clusters with high quality among cluster analysis technologies.But this algorithm needs user to give the k-most close neighbor's K value as well as the position to stop Combing sub-Clusters artificially.It is difficult to determinate these parameters without prior knowledge.Besides that,the Chameleon algorithm needs to use graph partitioning technology which is a NP-hard problem in the first step therefore increases the Computation order of complexity greatly.One kind of improved hierarchical clustering algorithm(I-Chameleon algorithm) is proposed which could solve the above many problems very well and find arbitrary shape clusters automatically by introducing the concept of modularity.
Graded triangular bases

This article develops a practical technique for studying representations of $\Bbbk$-linear categories arising in the categorification of quantum groups. We work in terms of locally unital algebras which are $\mathbb{Z}$-graded with graded pieces that are finite-dimensional and bounded below, developing a theory of graded triangular bases for such algebras. The definition is a graded extension of the notion of triangular basis introduced in previous joint work with Stroppel. However, in the general graded setting, finitely generated projective modules often fail to be Noetherian, so that existing results from the study of highest weight categories are not directly applicable. Nevertheless, we show that there is still a good theory of standard modules. In motivating examples arising from Kac-Moody 2-categories, these modules categorify the PBW bases for the modified forms of quantum groups constructed by Wang.
BadCS: A Backdoor Attack Framework for Code search

With the development of deep learning (DL), DL-based code search models have achieved state-of-the-art performance and have been widely used by developers during software development. However, the security issue, e.g., recommending vulnerable code, has not received sufficient attention, which will bring potential harm to software development. Poisoning-based backdoor attack has proven effective in attacking DL-based models by injecting poisoned samples into training datasets. However, previous work shows that the attack technique does not perform successfully on all DL-based code search models and tends to fail for Transformer-based models, especially pretrained models. Besides, the infected models generally perform worse than benign models, which makes the attack not stealthy enough and thereby hinders the adoption by developers. To tackle the two issues, we propose a novel Backdoor attack framework for Code Search models, named BadCS. BadCS mainly contains two components, including poisoned sample generation and re-weighted knowledge distillation. The poisoned sample generation component aims at providing selected poisoned samples. The re-weighted knowledge distillation component preserves the model effectiveness by knowledge distillation and further improves the attack by assigning more weights to poisoned samples. Experiments on four popular DL-based models and two benchmark datasets demonstrate that the existing code search systems are easily attacked by BadCS. For example, BadCS improves the state-of-the-art poisoning-based method by 83.03%-99.98% and 75.98%-99.90% on Python and Java datasets, respectively. Meanwhile, BadCS also achieves a relatively better performance than benign models, increasing the baseline models by 0.49% and 0.46% on average, respectively.
Simplicial Hopfield networks

Hopfield networks are artificial neural networks which store memory patterns on the states of their neurons by choosing recurrent connection weights and update rules such that the energy landscape of the network forms attractors around the memories. How many stable, sufficiently-attracting memory patterns can we store in such a network using $N$ neurons? The answer depends on the choice of weights and update rule. Inspired by setwise connectivity in biology, we extend Hopfield networks by adding setwise connections and embedding these connections in a simplicial complex. Simplicial complexes are higher dimensional analogues of graphs which naturally represent collections of pairwise and setwise relationships. We show that our simplicial Hopfield networks increase memory storage capacity. Surprisingly, even when connections are limited to a small random subset of equivalent size to an all-pairwise network, our networks still outperform their pairwise counterparts. Such scenarios include non-trivial simplicial topology. We also test analogous modern continuous Hopfield networks, offering a potentially promising avenue for improving the attention mechanism in Transformer models.
Learning to Personalize Recommendation based on Customers' Shopping Intents

Understanding the customers' high level shopping intent, such as their desire to go camping or hold a birthday party, is critically important for an E-commerce platform; it can help boost the quality of shopping experience by enabling provision of more relevant, explainable, and diversified recommendations. However, such high level shopping intent has been overlooked in the industry due to practical challenges. In this work, we introduce Amazon's new system that explicitly identifies and utilizes each customer's high level shopping intents for personalizing recommendations. We develop a novel technique that automatically identifies various high level goals being pursued by the Amazon customers, such as"go camping", and"preparing for a beach party". Our solution is in a scalable fashion (in 14 languages across 21 countries). Then a deep learning model maps each customer's online behavior, e.g. product search and individual item engagements, into a subset of high level shopping intents. Finally, a realtime ranker considers both the identified intents as well as the granular engagements to present personalized intent-aware recommendations. Extensive offline analysis ensures accuracy and relevance of the new recommendations and we further observe an 10% improvement in the business metrics. This system is currently serving online traffic at amazon.com, powering several production features, driving significant business impacts
Meshless Lagrangian methods for physics-based animations of solids and fluids

Nature builds very complex Systems of mutuallyinteracting and changing mate¬ rials. Computer graphics attempts to map these real world phenomena onto sim¬ plified physics-based modeis.The objeetive of this dissertation is to developnew methods for physics-based animation of interacting fluids and deformableobjeets, including melting and freezing, based on a unifiedmeshlessLagrangianapproach. Discretizing and solving the equations of motion using a meshless particlebased system has several advantages compared to mesh-based Lagrangian and Eulerian methods. Material properties are simply advected with the particles and might change as a function of time, the particles' position, and properties of neighboring particles. For strong deformations, the spatial discretization can be efficiently adapted without the need for complicatedremeshing. Furthermore, grid-based aliasing artifacts from the alignmentwith boundaries are avoided. A major drawback of meshless methods is the expensive computation of the parti¬ cles' neighborhoodneeded for Computing the shapefunctions.In this dissertation, the benefits of meshlesscollocationmethods are explored for stably animatingflu¬ ids and objeetswith arbitrary deformations. In our framework,the domainof a modelis represented by Volumetrie particles. We explore theSmoothedParticle Hydrodynamicsmethodto stably and efficiently solve the continuum mechanics equations for simulating fluids and elasto-plastic objeets including fracturing and contact handling. Based on a unified particle metaphor, we present a framework that combines solids and fluids, thus enabling to simulate a broad ränge of effects such as viscoelastic materials, melting solids, interactions between solids and fluids, and multiphaseeffects between liquid and air such as bubbles and foam. To improve the performance, we present a new multiresolutionapproachthat adapts the discretization of thedomaindynamically to the characteristics of the Simulation, while reducing the overall complexityof the computations. High quality animations require a high resolution surface embedded into the Volumetrie representation. We present new meshless methods for animatinga point-sampledsurfacealong with the particles. Our surfacemodelexploits the ad¬ vantages of both explicit and implicit surfacerepresentations.Geometricallycom¬ plex surfaces of deformable solids are efficiently animated using a rigid motion invariant free-form deformationapproach. Additionally, surface potentialfields can guide the surface deformationsuch that topological changes are handledim¬ plicitly, making the model suitable also for fluid simulations as well as melting
Deep learning and genetic algorithm framework for tailoring mechanical properties via inverse microstructure optimization

Materials-by-design has been historically challenging due to complex process-microstructure-property relations. Conventional analytical or simulation-based approaches suffer from low accuracy or long computational time and poor transferability, further limiting their applications in solving the inverse material design problem. Here, we establish a deep learning- and genetic algorithm-based framework that combines forward prediction and inverse exploration. Our framework provides an end-to-end solution for microstructure optimization to achieve application-specific mechanical properties of materials. In this study, we select the widely used Ti-6Al-4V to demonstrate the effectiveness of this framework by tailoring its microstructure to achieve various yield strength and elastic modulus across a large design space, while minimizing the stress concentration factor. Compared with conventional methods, our framework is efficient, versatile, and readily transferrable to other materials and properties. Paired with additive manufacturing’s potential in controlling local microstructural features, our method has far-reaching potential for accelerating the development of application-specific, high-performing materials.
Exploring lepton ﬂavor violation phenomena of the Z and Higgs bosons with unprecedented precision at electron-proton colliders

We comprehensively study the potential for discovering lepton ﬂavor violation (LFV) phenomena associated with the Z and Higgs bosons at the LHeC and FCC-he. Our meticulous investigation reveals the remarkable suitability of electron-proton colliders, harnessing advantages such as negligible pileups, minimal QCD backgrounds, electron-beam polarization P e , and the capability of distinguishing the charged-current from neutral-current processes. In our pursuit of LFV of the Z boson, we employ an innovative indirect probe, utilizing the t -channel mediation of the Z boson in the process pe − → jτ − . For LFV in the Higgs sector, we scrutinize direct observations of the on-shell decays of H → e + τ − and H → µ ± τ ∓ through the charged-current production of H . Focusing on H → e + τ − proves highly eﬃcient due to the absence of positron-related backgrounds in the charged-current modes at electron-proton colliders. Through a dedicated signal-to-background analysis with the boosted decision tree algorithm, we demonstrate that the LHeC with the total integrated luminosity of 1 ab − 1 and P e = − 80% can put signiﬁcantly lower 2 σ bounds than the HL-LHC with 3 ab − 1 . Speciﬁcally, we ﬁnd Br( Z → eτ ) < 4 . 8 × 10 − 7 , Br( H → eτ ) < 1 . 72 × 10 − 4 , and Br( H → µτ ) < 1 . 0 × 10 − 4 . Furthermore, our study uncovers the exceptional precision of the FCC-he in measuring the LFV signatures of the Z and Higgs bosons, which indicates the potential for future discoveries in this captivating ﬁeld
Policy Gradient Methods in the Presence of Symmetries and State Abstractions

Reinforcement learning (RL) on high-dimensional and complex problems relies on abstraction for improved efficiency and generalization. In this paper, we study abstraction in the continuous-control setting, and extend the definition of Markov decision process (MDP) homomorphisms to the setting of continuous state and action spaces. We derive a policy gradient theorem on the abstract MDP for both stochastic and deterministic policies. Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization. Based on these theorems, we propose a family of actor-critic algorithms that are able to learn the policy and the MDP homomorphism map simultaneously, using the lax bisimulation metric. Finally, we introduce a series of environments with continuous symmetries to further demonstrate the ability of our algorithm for action abstraction in the presence of such symmetries. We demonstrate the effectiveness of our method on our environments, as well as on challenging visual control tasks from the DeepMind Control Suite. Our method's ability to utilize MDP homomorphisms for representation learning leads to improved performance, and the visualizations of the latent space clearly demonstrate the structure of the learned abstraction.
Tapering spatio temporal models

We consider regression models for multivariate spatio temporal data. We view the data as a time series of spatial processes and work in the setting of dynamic models. In order to add exibility we consider regression models with spatio temporal varying coefficients. Spatial dependence among the di fferent measurements is attained considering the linear model of coregionalization. Since spatio-temporal data are tipically of large dimension we propose to perform estimation both through maximum likelihood by means of the EM algorithm and a modi ed version of it exploiting the covariance tapering likelihood function.
AREPO White Dwarf merger simulations resulting in edge-lit detonation and run-away hypervelocity companion

We present a series of high-resolution simulations generated with the moving-mesh code arepo to model the merger of a 1.1 M⊙ carbon-oxygen primary white dwarf with an outer helium layer and a 0.35 M⊙ secondary helium white dwarf. Our simulations lead to detonations that are consistent with the edge-lit scenario, where a helium detonation is ignited at the base of the helium layer of the primary WD, which triggers an off-centre carbon detonation. This produces an asymmetric ejecta pattern and differences in line-of-sight observables (e.g. mean atomic weight). The ejecta that are flung into space are dominated by 56Ni, 4He, 28Si, and 32S. Our simulations result in a surviving degenerate companion of mass 0.22–0.25 M⊙ moving at >1700 km s−1, consistent with the observational findings of hypervelocity WDs. The secondary’s surface layers are enriched by heavy metals, with 56Ni making up approximately 0.8% of the remaining mass. We also analyse the sensitivity of the outcome on simulation parameters, including the ‘inspiral time’, which defines a period of accelerated angular momentum loss. We find that the choice of ‘inspiral time’ qualitatively influences the simulation result, including the survival of the secondary. We argue that the shorter inspiral cases result in qualitatively and quantitatively similar outcomes. We also investigate the sensitivity of our results on the primary’s chemical profile by comparing simulations using isothermal, constant composition models with the same mass and central composition and characterised by either a bare carbon-oxygen core (no helium) or a carbon-oxygen core enveloped by a thick helium layer.
On an Integral Variant of Incremental Input/Output-to-State Stability and Its Use as a Notion of Nonlinear Detectability

We propose a time-discounted integral variant of incremental input/output-to-state stability (i-iIOSS) together with an equivalent Lyapunov function characterization. Continuity of the i-iIOSS Lyapunov function is ensured if the system satisfies a certain continuity assumption involving the Osgood condition. We show that the proposed i-iIOSS notion is a necessary condition for the existence of a robustly globally asymptotically stable observer mapping in a time-discounted “<inline-formula> <tex-math notation="LaTeX">${L^{2}}$ </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">${L^{\infty }}$ </tex-math></inline-formula>” sense. In combination, our results provide a general framework for a Lyapunov-based robust stability analysis of observers for continuous-time systems, which in particular is crucial for the use of optimization-based state estimators (such as moving horizon estimation).
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation

We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthetic images was evaluated on an unseen dataset of real images and yielded mean Dice scores of 88.6 $\pm 4.91$ , 91.9 $\pm 4.22$, 85.2 $\pm 4.83$ \% for left ventricular endocardium, epicardium and left atrial segmentation respectively. This represents a relative increase of $9.2$, $3.3$ and $13.9$ \% in Dice scores compared to the previous state-of-the-art. The proposed pipeline has potential for application to a wide range of other tasks across various medical imaging modalities.
Well-posedness of the periodic dispersion-generalized Benjamin-Ono equation in the weakly dispersive regime

We study the dispersion-generalized Benjamin-Ono equation in the periodic setting. This equation interpolates between the Benjamin-Ono equation ($\alpha=1$) and the viscous Burgers' equation ($\alpha=0$). We obtain local well-posedness in $H^s$ for $s>3/2-\alpha$ and $\alpha\in(0,1)$ by using the short-time Fourier restriction method.
Deep Learning-based Estimation for Multitarget Radar Detection

Target detection and recognition is a very challenging task in a wireless environment where a multitude of objects are located, whether to effectively determine their positions or to identify them and predict their moves. In this work, we propose a new method based on a convolutional neural network (CNN) to estimate the range and velocity of moving targets directly from the range-Doppler map of the detected signals. We compare the obtained results to the two dimensional (2D) periodogram, and to the similar state of the art methods, 2DResFreq and VGG-19 network and show that the estimation process performed with our model provides better estimation accuracy of range and velocity index in different signal to noise ratio (SNR) regimes along with a reduced prediction time. Afterwards, we assess the performance of our proposed algorithm using the peak signal to noise ratio (PSNR) which is a relevant metric to analyse the quality of an output image obtained from compression or noise reduction. Compared to the 2D-periodogram, 2DResFreq and VGG-19, we gain 33 dB, 21 dB and 10 dB, respectively, in terms of PSNR when SNR = 30 dB.
Accurate electron-recoil ionization factors for dark matter direct detection in xenon, krypton, and argon

While most scintillation-based dark matter experiments search for Weakly Interacting Massive Particles (WIMPs), a sub-GeV WIMP-like particle may also be detectable in these experiments. While dark matter of this type and scale would not leave appreciable nuclear recoil signals, it may instead induce ionization of atomic electrons. Accurate modelling of the atomic wavefunctions is key to investigating this possibility, with incorrect treatment leading to a large suppression in the atomic excitation factors. We have calculated these atomic factors for argon, krypton and xenon and present the tabulated results for use with a range of dark matter models. This is made possible by the separability of the atomic and dark matter form factor, allowing the atomic factors to be calculated for general couplings; we include tables for vector, scalar, pseudovector, and pseudoscalar electron couplings. Additionally, we calculate electron impact total ionization cross sections for xenon using the tabulated results as a test of accuracy. Lastly, we provide an example calculation of the event rate for dark matter scattering on electrons in XENON1T and show that these calculations depend heavily on how the low-energy response of the detector is modelled.
Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue

Goal-directed dialogue systems aim to proactively reach a pre-determined target through multi-turn conversations. The key to achieving this task lies in planning dialogue paths that smoothly and coherently direct conversations towards the target. However, this is a challenging and under-explored task. In this work, we propose a coherent dialogue planning approach that uses a stochastic process to model the temporal dynamics of dialogue paths. We define a latent space that captures the coherence of goal-directed behavior using a Brownian bridge process, which allows us to incorporate user feedback flexibly in dialogue planning. Based on the derived latent trajectories, we generate dialogue paths explicitly using pre-trained language models. We finally employ these paths as natural language prompts to guide dialogue generation. Our experiments show that our approach generates more coherent utterances and achieves the goal with a higher success rate.
Research Reports on Mathematical and Computing Sciences Series B : Operations Research

The positive semidenite constraint for the variable matrix in semidenite pro- gramming (SDP) relaxation is further relaxed by a nite number of second order cone constraints in second order cone programming (SOCP) relaxations. A few types of SOCP relaxations are obtained from dierent ways of expressing the positive semidenite con- straint of the SDP relaxation. We present how such SOCP relaxations can be derived, and show the relationship between the resulting SOCP relaxations.
Towards Understanding Machine Learning Testing in Practise

Visualisations drive all aspects of the Machine Learning (ML) Development Cycle but remain a vastly untapped resource by the research community. ML testing is a highly interactive and cognitive process which demands a human-in-the-loop approach. Besides writing tests for the code base, bulk of the evaluation requires application of domain expertise to generate and interpret visualisations. To gain a deeper insight into the process of testing ML systems, we propose to study visualisations of ML pipelines by mining Jupyter notebooks. We propose a two prong approach in conducting the analysis. First, gather general insights and trends using a qualitative study of a smaller sample of notebooks. And then use the knowledge gained from the qualitative study to design an empirical study using a larger sample of notebooks. Computational notebooks provide a rich source of information in three formats—text, code and images. We hope to utilise existing work in image analysis and Natural Language Processing for text and code, to analyse the information present in notebooks. We hope to gain a new perspective into program comprehension and debugging in the context of ML testing.