Miracle-dz/UniFAR-FTUs huggingface dataset:
Vectorial Bent Functions in Characteristic Two

We study Dillon-type vectorial bent functions of the monomial and multinomial varieties. We also study Kloosterman sums, which relate to the construction of Dillon-type monomial bent functions. For Dillon-type monomial functions we give sufficient conditions for vectorial bentness, leading to the construction of several new examples. We give useful necessary conditions for functions from GF (24m) to GF (4). We give new restrictions on the maximum output dimension of Dillon-type monomial bent functions on GF (24m). We subsequently show that certain Dillon-type multinomial bent functions do not meet the Nyberg bound. We give computational results regarding Dillon-type functions from GF (24m) to GF (4), suggesting that while bent monomials of this type appear to be rare, their binomial counterparts seem to be relatively abundant. Finally, we give divisibility results on Kloosterman sums valued on cosets of certain subfields of GF (2m), leading to explicit constructions of Kloosterman zeros and Dillon-type monomial bent functions.
An SVM fall recognition algorithm based on a gravity acceleration sensor

ABSTRACT To address the increasing health care needs for an ageing population, in this paper, a method of detecting human movements using smartphones is proposed to decrease the risk of accidents in the elderly. The method proposed in this paper uses a mobile phone that has an embedded acceleration sensor to record human motion information that are divided into daily activities (walking, running, going up stairs, going down stairs, and standing still) and falling down. In the process of data acquisition, motion noise contains some interference, and thus the median filter is employed to de-noise and smooth the motion data. Moreover, we extract representative multi-group features and analyse the features by principal component analysis and singular value decomposition to reduce dimensions. Through experimental comparisons with various classifiers, the support vector machine classifier is selected to classify the extracted features. The accuracy of fall detection reached 96.072%, which proved the accuracy of our proposed method.
ON THE USE OF GEOMETRIC DEEP LEARNING FOR THE ITERATIVE CLASSIFICATION AND DOWN-SELECTION OF ANALOG ELECTRIC CIRCUITS

Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing solution effectively and efficiently with lower computational costs. This case study used here is the synthesis of analog electrical circuits that attempt to match a specific frequency response within a particular frequency range. Previous studies utilized an enumeration technique to generate 43,249 unique undirected graphs presenting valid potential circuits. Unfortunately, determining the sizing and performance of many circuits can be too expensive. To reduce computational costs with a quantified trade-off in accuracy, the fraction of the circuit graphs and their performance are used as input data to a classification-focused GDL model. Then, the GDL model can be used to predict the remainder cheaply, thus, aiding decision-makers in the search for the best graph solutions. The results discussed in this paper show that additional graph-based features are useful, favorable total set classification accuracy of 80% in using only 10% of the graphs, and iteratively-built GDL models can further subdivide the graphs into targeted groups with medians significantly closer to the best and containing 88.2 of the top 100 best-performing graphs on average using 25% of the graphs.
Systematic Features and Progenitor Dependence of Core-Collapse Supernovae

We present our latest results of two-dimensional core-collapse supernova simulations for about 400 progenitors. Our self-consistent supernova models reveal the systematic features of core-collapse supernova properties such as neutrino luminosity and energy spectrum, explosion energy, remnant mass, and yield of radioactive 56 Ni. We ﬁnd that these explosion characteristics tend to show a mono-tonic increase as a function of mass accretion rate onto a shock. The accretion rate depends on the structure of the progenitor core and its envelope, which is well described by the compactness parameter.
Global Hierarchical Neural Networks using Hierarchical Softmax

This paper presents a framework in which hierarchical softmax is used to create a global hierarchical classifier. The approach is applicable for any classification task where there is a natural hierarchy among classes. We show empirical results on four text classification datasets. In all datasets the hierarchical softmax improved on the regular softmax used in a flat classifier in terms of macro-F1 and macro-recall. In three out of four datasets hierarchical softmax achieved a higher micro-accuracy and macro-precision.
The Bayesian Context Trees State Space Model for time series modelling and forecasting

A hierarchical Bayesian framework is introduced for developing rich mixture models for real-valued time series, partly motivated by important applications in financial time series analysis. At the top level, meaningful discrete states are identified as appropriately quantised values of some of the most recent samples. These observable states are described as a discrete context-tree model. At the bottom level, a different, arbitrary model for real-valued time series -- a base model -- is associated with each state. This defines a very general framework that can be used in conjunction with any existing model class to build flexible and interpretable mixture models. We call this the Bayesian Context Trees State Space Model, or the BCT-X framework. Efficient algorithms are introduced that allow for effective, exact Bayesian inference and learning in this setting; in particular, the maximum a posteriori probability (MAP) context-tree model can be identified. These algorithms can be updated sequentially, facilitating efficient online forecasting. The utility of the general framework is illustrated in two particular instances: When autoregressive (AR) models are used as base models, resulting in a nonlinear AR mixture model, and when conditional heteroscedastic (ARCH) models are used, resulting in a mixture model that offers a powerful and systematic way of modelling the well-known volatility asymmetries in financial data. In forecasting, the BCT-X methods are found to outperform state-of-the-art techniques on simulated and real-world data, both in terms of accuracy and computational requirements. In modelling, the BCT-X structure finds natural structure present in the data. In particular, the BCT-ARCH model reveals a novel, important feature of stock market index data, in the form of an enhanced leverage effect.
A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles

This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is approached by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.
One Fits All: A Unified Synchrotron Model Explains GRBs with FRED-shape Pulses

The analysis of gamma-ray burst (GRB) spectra often relies on empirical models lacking a distinct physical explanation. Previous attempts to couple physical models with observed data focus on individual burst studies, fitting models to segmented spectra with independent physical parameters. However, these approaches typically neglect to explain the time evolution of observed spectra. In this study, we propose a novel approach by incorporating the synchrotron radiation model to provide a self-consistent explanation for a selection of single-pulse GRBs. Our study comprehensively tests the synchrotron model under a unified physical condition, such as a single injection event of electrons. By tracing the evolution of cooling electrons in a decaying magnetic field, our model predicts time-dependent observed spectra that align well with the data. Using a single set of physical parameters, our model successfully fits all time-resolved spectra within each burst. Our model suggests that the rising phase of the GRB light curve results from the increasing number of radiating electrons, while the declining phase is attributed to the curvature effect, electron cooling, and the decaying magnetic field. Our model provides a straightforward interpretation of the peak energy’s evolution, linked to the decline of the magnetic field and electron cooling due to the expansion of the GRB emission region. Our findings strongly support the notion that spectral and temporal evolution in GRB pulses originates from the expansion of the GRB emission region, with an initial radius of approximately 1015 cm, and synchrotron radiation as the underlying emission mechanism.
The Rhie-Chow stabilized Box Method for the Stokes problem

The Finite Volume method (FVM) is widely adopted in many different applications because of its built-in conservation properties, its ability to deal with arbitrary mesh and its computational efficiency. In this work, we consider the Rhie-Chow stabilized Box Method (RCBM) for the approximation of the Stokes problem. The Box Method (BM) is a piecewise linear Petrov-Galerkin formulation on the Voronoi dual mesh of a Delaunay triangulation, whereas the Rhie-Chow (RC) stabilization is a well known stabilization technique for FVM. The first part of the paper provides a variational formulation of the RC stabilization and discusses the validity of crucial properties relevant for the well-posedeness and convergence of RCBM. Moreover, a numerical exploration of the convergence properties of the method on 2D and 3D test cases is presented. The last part of the paper considers the theoretically justification of the well-posedeness of RCBM and the experimentally observed convergence rates. This latter justification hinges upon suitable assumptions, whose validity is numerically explored.
Radiative forcing of climate change

Our current understanding of mechanisms that are, or may be, acting to cause climate change over the past century is briefly reviewed, with an emphasis on those due to human activity. The paper discusses the general level of confidence in these estimates and areas of remaining uncertainty. The effects of increases in the so-called well-mixed greenhouse gases, and in particular carbon dioxide, appear to be the dominant mechanism. However, there are considerable uncertainties in our estimates of many other forcing mechanisms; those associated with the so-called indirect aerosol forcing (whereby changes in aerosols can impact on cloud properties) may be the most serious, as its climatic effect may be of a similar size as, but opposite sign to, that due to carbon dioxide. The possible role of volcanic eruptions as a natural climate change mechanism is also highlighted.
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology

Processing-in-memory (PIM) has been explored for decades by computer architects, yet it has never seen the light of day in real-world products due to their high design overheads and lack of a killer application. With the advent of critical memory-intensive workloads, several commercial PIM technologies have been introduced to the market ranging from domain-specific PIM architectures to more general-purpose PIM architectures. In this work, we deepdive into UPMEM's commercial PIM technology, a general-purpose PIM-enabled parallel architecture that is highly programmable. Our first key contribution is the development of a flexible simulation framework for PIM. The simulator we developed (aka PIMulator) enables the compilation of UPMEM-PIM source codes into its compiled machine-level instructions, which are subsequently consumed by our cycle-level performance simulator. Using PIMulator, we demystify UPMEM's PIM design through a detailed characterization study. Building on top of our characterization, we conduct a series of case studies to pathfind important architectural features that we deem will be critical for future PIM architectures to support
More Context, Less Distraction: Visual Classification by Inferring and Conditioning on Contextual Attributes

CLIP, as a foundational vision language model, is widely used in zero-shot image classification due to its ability to understandvariousvisualconceptsandnaturallanguagedescriptions. However,howtofullyleverageCLIP’sunprecedented human-like understanding capabilities to achieve better zero-shot classification is still an open question. This paper draws inspiration from the human visual perception process: a modern neuroscience view suggests that in classifying an object, humans first infer its class-independent attributes (e.g., background and orientation) which help separate the foreground object from the background, and then make decisions based on this information. Inspired by this, we observe that providing CLIP with contextual attributes improves zero-shot classification and mitigates reliance on spurious features. We also observe that CLIP itself can reasonably infer the attributes from an image. With these observations, we propose a training-free, two-step zero-shot classification method named PerceptionCLIP . Given an image, it first infers contextual attributes (e.g., background) and then performs object classification conditioning on them. Our experiments show that PerceptionCLIP achieves better generalization, group robustness, and better interpretability. For example, PerceptionCLIP with ViT-L/14 improves the worst group accuracy by 16 . 5% on the Waterbirds dataset and by 3 . 5% on CelebA.
Contact formalism for dissipative mechanical systems on Lie algebroids

In this paper, we introduce a geometric description of contact Lagrangian and Hamiltonian systems on Lie algebroids in the framework of contact geometry, using the theory of prolongations. We discuss the relation between Lagrangian and Hamiltonian settings through a convenient notion of Legendre transformation. We also discuss the Hamilton-Jacobi problem in this framework and introduce the notion of a Legendrian Lie subalgebroid of a contact Lie algebroid.
Collisional relaxation of two-dimensional gravitational systems

Systems with long range interactions present generically the formation of quasi-stationary long-lived non-equilibrium states. These non-Boltzmann states relax to Boltzmann equilibrium following a dynamic which is not well understood. In this letter we derive a simple approximate kinetic equation for the relaxation process in a two-dimensional inhomogeneous self-gravitating particle system, obtaining a Fokker -- Planck equation for the velocity distribution with explicit analytical diffusion coefficients. Performing molecular dynamics simulations using the full dynamics and comparing them with the evolution predicted by the Fokker -- Planck equation, we observe a good agreement with the model for all the duration of the relaxation, from the formation of the Quasi-Stationary state to thermal equilibrium. During all this process we observe a scaling of the relaxation time proportional to the number of particles in the system.
Shaping Online Dialogue: Examining How Community Rules Affect Discussion Structures on Reddit

Community rules play a key part in enabling or constraining the behaviors of members in online communities. However, little is unknown regarding whether and to what degree changing rules actually affects community dynamics. In this paper, we seek to understand how these behavior-governing rules shape the interactions between users, as well as the structure of their discussion. Using the top communities on Reddit (i.e. subreddits), we first contribute a taxonomy of behavior-based rule categories across Reddit. Then, we use a network analysis perspective to discover how changing implementation of different rule categories affects subreddits' user interaction and discussion networks over a 1.5 year period. Our study find several significant effects, including greater clustering among users when subreddits increase rules focused on structural regulation and how restricting allowable content surprisingly leads to more interactions between users. Our findings contribute to research in proactive moderation through rule setting, as well as lend valuable insights for online community designers and moderators to achieve desired community dynamics.
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction

Deep generative models have gained recent traction in accelerated MRI reconstruction. Diffusion priors are particularly promising given their representational fidelity. Instead of the target transformation from undersampled to fully-sampled data required for MRI reconstruction, common diffusion priors are trained to learn a task-agnostic transformation from an asymptotic start-point of Gaussian noise onto the finite end-point of fully-sampled data. During inference, data-consistency projections are injected in between reverse diffusion steps to reach a compromise solution within the span of both the trained diffusion prior and the imaging operator for an accelerated MRI acquisition. Unfortunately, performance losses can occur due to the discrepancy between target and learned transformations given the asymptotic normality assumption in diffusion priors. To address this discrepancy, here we introduce a novel Fourier-constrained diffusion bridge (FDB) for MRI reconstruction that transforms between a finite start-point of moderately undersampled data and an end-point of fully-sampled data. We derive the theoretical formulation of FDB as a generalized diffusion process based on a stochastic degradation operator that performs random spatial-frequency removal. We propose an enhanced sampling algorithm with a learned correction term for soft dealiasing across reverse diffusion steps. Demonstrations on brain MRI indicate that FDB outperforms state-of-the-art methods including non-diffusion and diffusion priors.
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach

Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishing individual entities (nodes or graphs) and overlook the possibility of anomalous groups within the graph. To address this limitation, this paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. Subsequently, group sampling is employed to sample candidate groups, which are then fed into the proposed Topology Pattern-based Graph Contrastive Learning (TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to generate embeddings for each candidate group and thus distinct anomaly groups. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups, highlighting it as a promising solution for Gr-GAD. Datasets and codes of the proposed framework are at the github repository https://anonymous.4open. science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection.
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations

Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at \url{https://github.com/chrisjtan/ucr}.
On how neural networks enhance quantum state tomography with constrained measurements

Quantum state tomography aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Inspired by the intuition that machine learning has favorable robustness and generalization, we propose a deep neural networks based quantum state tomography (DNN-QST) approach, which are applied to three measurement-constrained cases, including few measurement copies and incomplete measurements as well as noisy measurements. Numerical results demonstrate that DNN-QST exhibits a great potential to achieve high fidelity for quantum state tomography with limited measurement resources and can achieve improved estimation when tomographic measurements suffer from noise. In addition, the results for 2-qubit states from quantum optical devices demonstrate the generalization of DNN-QST and its robustness against possible error in the experimental devices.
Domain-adapted Learning and Interpretability: DRL for Gas Trading

Deep Reinforcement Learning (Deep RL) has been explored for a number of applications in finance and stock trading. In this paper, we present a practical implementation of Deep RL for trading natural gas futures contracts. The Sharpe Ratio obtained exceeds benchmarks given by trend following and mean reversion strategies as well as results reported in literature. Moreover, we propose a simple but effective ensemble learning scheme for trading, which significantly improves performance through enhanced model stability and robustness as well as lower turnover and hence lower transaction cost. We discuss the resulting Deep RL strategy in terms of model explainability, trading frequency and risk measures.