Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Angle Finding for Generalized Quantum Signal Processing

Quantum Signal Processing (QSP), together with the quantum singular value transformation, is one of the central quantum algorithms due to its efficiency and generality in many fields including quantum simulation, quantum machine learning, and quantum cryptography. The largest bottleneck of QSP and its family is its difficulty in finding the phase angle sequence for signal processing. We find that this is in particular prominent when one employs the generalized formalism of the QSP, or the GQSP, to employ arbitrary single-qubit unitaries for signal processing operator. In this work, we extend the framework of GQSP and propose a robust angle finding algorithm. The proposed angle finding algorithm, based on Prony's method, successfully generates angle sequence of precision $10^{-13}$ up to polynomial degrees of hundreds within a second. By applying our method to Hamiltonian simulation, we find that the number of calls, or queries, to signal operators are essentially halved compared to the ordinary framework of QSP.
Unraveling the Key of Machine Learning Solutions for Android Malware Detection

Android malware detection serves as the front line against malicious apps. With the rapid advancement of machine learning (ML), ML-based Android malware detection has attracted increasing attention due to its capability of automatically capturing malicious patterns from Android APKs. These learning-driven methods have reported promising results in detecting malware. However, the absence of an in-depth analysis of current research progress makes it difficult to gain a holistic picture of the state of the art in this area. This paper presents a comprehensive investigation to date into ML-based Android malware detection with empirical and quantitative analysis. We first survey the literature, categorizing contributions into a taxonomy based on the Android feature engineering and ML modeling pipeline. Then, we design a general-propose framework for ML-based Android malware detection, re-implement 12 representative approaches from different research communities, and evaluate them from three primary dimensions, i.e., effectiveness, robustness, and efficiency. The evaluation reveals that ML-based approaches still face open challenges and provides insightful findings like more powerful ML models are not the silver bullet for designing better malware detectors. We further summarize our findings and put forth recommendations to guide future research.
Review of multimodal machine learning approaches in healthcare

Machine learning methods in healthcare have traditionally focused on using data from a single modality, limiting their ability to effectively replicate the clinical practice of integrating multiple sources of information for improved decision making. Clinicians typically rely on a variety of data sources including patients' demographic information, laboratory data, vital signs and various imaging data modalities to make informed decisions and contextualise their findings. Recent advances in machine learning have facilitated the more efficient incorporation of multimodal data, resulting in applications that better represent the clinician's approach. Here, we provide a review of multimodal machine learning approaches in healthcare, offering a comprehensive overview of recent literature. We discuss the various data modalities used in clinical diagnosis, with a particular emphasis on imaging data. We evaluate fusion techniques, explore existing multimodal datasets and examine common training strategies.
The Patch Topology in Univalent Foundations

Stone locales together with continuous maps form a coreflective subcategory of spectral locales and perfect maps. A proof in the internal language of an elementary topos was previously given by the second-named author. This proof can be easily translated to univalent type theory using resizing axioms. In this work, we show how to achieve such a translation without resizing axioms, by working with large and locally small frames with small bases. This requires predicative reformulations of several fundamental concepts of locale theory in predicative HoTT/UF, which we investigate systematically.
Role of time-varying magnetic field on QGP equation of state

The phase diagram of quantum chromodynamics (QCD) and its associated thermodynamic properties of quark gluon plasma (QGP) are studied in the presence of time dependent magnetic field. The study plays a pivotal role in the field of cosmology, astrophysics, and heavy ion collisions. In order to explore the structure of quark gluon plasma to deal with the dynamics of quarks and gluons, we investigate the equation of state (EoS) not only in the environment of static magnetic field but also in the presence of time-varying magnetic fields. So, for determining the equation of state of QGP at non zero magnetic fields, we revisited our earlier model where the effect of time varying magnetic field was not taken into consideration. Using the phenomenological model, some appealing features are noticed depending upon the three different scales; effective mass of quark, temperature, and time independent as well as time-dependent magnetic field. Earlier the effective mass of quark was incorporated in our calculations and in the current work, it is modified for static and time-varying magnetic fields. Thermodynamic observables including pressure, energy density, entropy, etc. are calculated for a wide range of temperature and time-dependent as well as time-independent magnetic fields. Finally, we claim that the EoS are highly affected in the presence of a magnetic field. Our results are notable compared to other approaches and found to be advantageous for the measurement of QGP equation of state. These crucial findings with and without time-varying magnetic field could have phenomenological implications in various sectors of high energy physics.
Recent Advances in Digital Image and Video Forensics, Anti-forensics and Counter Anti-forensics

Image and video forensics have recently gained increasing attention due to the proliferation of manipulated images and videos, especially on social media platforms, such as Twitter and Instagram, which spread disinformation and fake news. This survey explores image and video identification and forgery detection covering both manipulated digital media and generative media. However, media forgery detection techniques are susceptible to anti-forensics; on the other hand, such anti-forensics techniques can themselves be detected. We therefore further cover both anti-forensics and counter anti-forensics techniques in image and video. Finally, we conclude this survey by highlighting some open problems in this domain.
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees

Active learning (AL) aims to improve model performance within a fixed labeling budget by choosing the most informative data points to label. Existing AL focuses on the single-domain setting, where all data come from the same domain (e.g., the same dataset). However, many real-world tasks often involve multiple domains. For example, in visual recognition, it is often desirable to train an image classifier that works across different environments (e.g., different backgrounds), where images from each environment constitute one domain. Such a multi-domain AL setting is challenging for prior methods because they (1) ignore the similarity among different domains when assigning labeling budget and (2) fail to handle distribution shift of data across different domains. In this paper, we propose the first general method, dubbed composite active learning (CAL), for multi-domain AL. Our approach explicitly considers the domain-level and instance-level information in the problem; CAL first assigns domain-level budgets according to domain-level importance, which is estimated by optimizing an upper error bound that we develop; with the domain-level budgets, CAL then leverages a certain instance-level query strategy to select samples to label from each domain. Our theoretical analysis shows that our method achieves a better error bound compared to current AL methods. Our empirical results demonstrate that our approach significantly outperforms the state-of-the-art AL methods on both synthetic and real-world multi-domain datasets. Code is available at https://github.com/Wang-ML-Lab/multi-domain-active-learning.
How Free is Parameter-Free Stochastic Optimization?

We study the problem of parameter-free stochastic optimization, inquiring whether, and under what conditions, do fully parameter-free methods exist: these are methods that achieve convergence rates competitive with optimally tuned methods, without requiring significant knowledge of the true problem parameters. Existing parameter-free methods can only be considered ``partially'' parameter-free, as they require some non-trivial knowledge of the true problem parameters, such as a bound on the stochastic gradient norms, a bound on the distance to a minimizer, etc. In the non-convex setting, we demonstrate that a simple hyperparameter search technique results in a fully parameter-free method that outperforms more sophisticated state-of-the-art algorithms. We also provide a similar result in the convex setting with access to noisy function values under mild noise assumptions. Finally, assuming only access to stochastic gradients, we establish a lower bound that renders fully parameter-free stochastic convex optimization infeasible, and provide a method which is (partially) parameter-free up to the limit indicated by our lower bound.
A Holistic Approach for Equity-aware Carbon Reduction of Ridesharing Platforms

Ridesharing services have revolutionized personal mobility, offering convenient on-demand transportation anytime. While early proponents of ridesharing suggested that these services would reduce the overall carbon emissions of the transportation sector, recent studies reported a type of rebound effect showing substantial carbon emissions of ridesharing platforms, mainly due to their deadhead miles traveled between two consecutive rides. However, reducing deadhead miles' emissions can incur longer waiting times for riders and starvation of ride assignments for some drivers. Therefore, any efforts towards reducing the carbon emissions from ridesharing platforms must consider the impact on the quality of service, e.g., waiting time, and on the equitable distribution of rides across drivers. This paper proposes a holistic approach to reduce the carbon emissions of ridesharing platforms while minimizing the degradation in user waiting times and equitable ride assignments across drivers. Towards this end, we decompose the global carbon reduction problem into two sub-problems: carbon- and equity-aware ride assignment and fuel-efficient routing. For the ride assignment problem, we consider the trade-off between the amount of carbon reduction and the rider's waiting time and propose simple yet efficient algorithms to handle the conflicting trade-offs. For the routing problem, we analyze the impact of fuel-efficient routing in reducing the carbon footprint, trip duration, and driver efficiency of ridesharing platforms using route data from Google Maps. Our comprehensive trace-driven experimental results show significant emissions reduction with a minor increase in riders' waiting times. Finally, we release E$^2$-RideKit, a toolkit that enables researchers to augment ridesharing datasets with emissions and equity information for further research on emission analysis and platform improvement.
Optimal sampling for stochastic and natural gradient descent

We consider the problem of optimising the expected value of a loss functional over a nonlinear model class of functions, assuming that we have only access to realisations of the gradient of the loss. This is a classical task in statistics, machine learning and physics-informed machine learning. A straightforward solution is to replace the exact objective with a Monte Carlo estimate before employing standard first-order methods like gradient descent, which yields the classical stochastic gradient descent method. But replacing the true objective with an estimate ensues a ``generalisation error''. Rigorous bounds for this error typically require strong compactness and Lipschitz continuity assumptions while providing a very slow decay with sample size. We propose a different optimisation strategy relying on a natural gradient descent in which the true gradient is approximated in local linearisations of the model class via (quasi-)projections based on optimal sampling methods. Under classical assumptions on the loss and the nonlinear model class, we prove that this scheme converges almost surely monotonically to a stationary point of the true objective and we provide convergence rates.
Design Space Exploration for Particle Detector Read-out Implementations in Matlab and Simulink on the Example of the SHiP SBT

On a very fundamental level, particle detectors share similar requirements for their read-out chain. This is reflected in the way that typical read-out solutions are developed, where a previous design is taken and modified to fit some changes in requirements. One of the two common approaches is the current-based read-out, where the waveform of the sensor output is sampled in order to later extract information from there. This approach is used in many detector applications using scintillation based detectors, including PET. With this contribution, we will introduce how we use Matlab in order to simulate the read-out electronics of particle detectors. We developed this simulation approach as a base for our ongoing development of software-defined read-out ASICs that cover the requirements of a variety of particle detector types. Simulink was chosen as a base for our developments as it allows simulation of mixed-signal systems and comes with built-in toolkits to aid in developments of such systems. With our approach, we want to take a new look at how we approach designing such a read-out, with a focus on digital signal processing close to the sensor, making use of known signal characteristics and modern methods of communications engineering. We are taking into account the time profile of an event, the bandwidth-limiting properties of the sensor and attached electronics, digitization stages and finally the parameterization of approaches for digital processing of the signal. We will show how we are applying the design approach to the development of a read-out for the proposed SHiP SBT detector, which is a scintillation based detector relying on SiPMs sensors, using this as an example for our modelling approach and show preliminary results.
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback

Following the success of Proximal Policy Optimization (PPO) for Reinforcement Learning from Human Feedback (RLHF), new techniques such as Sequence Likelihood Calibration (SLiC) and Direct Policy Optimization (DPO) have been proposed that are offline in nature and use rewards in an indirect manner. These techniques, in particular DPO, have recently become the tools of choice for LLM alignment due to their scalability and performance. However, they leave behind important features of the PPO approach. Methods such as SLiC or RRHF make use of the Reward Model (RM) only for ranking/preference, losing fine-grained information and ignoring the parametric form of the RM (eg., Bradley-Terry, Plackett-Luce), while methods such as DPO do not use even a separate reward model. In this work, we propose a novel approach, named BRAIn, that re-introduces the RM as part of a distribution matching approach.BRAIn considers the LLM distribution conditioned on the assumption of output goodness and applies Bayes theorem to derive an intractable posterior distribution where the RM is explicitly represented. BRAIn then distills this posterior into an amortized inference network through self-normalized importance sampling, leading to a scalable offline algorithm that significantly outperforms prior art in summarization and AntropicHH tasks. BRAIn also has interesting connections to PPO and DPO for specific RM choices.
Small inertia limit for coupled kinetic swarming models

We investigate various versions of multi-dimensional systems involving many species, modeling aggregation phenomena through nonlocal interaction terms. We establish a rigorous connection between kinetic and macroscopic descriptions by considering the small-inertia limit at the kinetic level. The results are proven either under smoothness assumptions on all interaction kernels or under singular assumptions for \emph{self-interaction} potentials. Utilizing different techniques in the two cases, we demonstrate the existence of a solution to the kinetic system, provide uniform estimates with respect to the inertia parameter, and show convergence towards the corresponding macroscopic system as the inertia approaches zero.
Loading-effect-based three-dimensional microfabrication empowers on-chip Brillouin optomechanics.

The acousto-optic interaction known as stimulated Brillouin scattering (SBS) has emerged as a fundamental principle for realizing crucial components and functionalities in integrated photonics. However, the main challenge of integrating Brillouin devices is how to effectively confine both optical and acoustic waves. Apart from that, the manufacturing processes for these devices need to be compatible with standard fabrication platforms and streamlined to facilitate their large-scale integration. Here, we demonstrate a novel, to the best of our knowledge, suspended nanowire structure that can tightly confine photons and phonons. Furthermore, tailored for this structure, we introduce a loading-effect-based three-dimensional microfabrication technique, compatible with complementary metal-oxide-semiconductor (CMOS) technology. This innovative technique allows for the fabrication of the entire structure using a single-step lithography exposure, significantly streamlining the fabrication process. Leveraging this structure and fabrication scheme, we have achieved a Brillouin gain coefficient of 1100 W-1m-1 on the silicon-on-insulator platform within a compact footprint. It can support a Brillouin net gain over 4.1 dB with modest pump powers. We believe that this structure can significantly advance the development of SBS on chip, unlocking new opportunities for a large-scale integration of Brillouin-based photonic devices.
SIMPL: A Simple and Efficient Multi-Agent Motion Prediction Baseline for Autonomous Driving

This letter presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles. Unlike conventional agent-centric methods with high accuracy but repetitive computations and scene-centric methods with compromised accuracy and generalizability, SIMPL delivers real-time, accurate motion predictions for all relevant traffic participants. To achieve improvements in both accuracy and inference speed, we propose a compact and efficient global feature fusion module that performs directed message passing in a symmetric manner, enabling the network to forecast future motion for all road users in a single feed-forward pass and mitigating accuracy loss caused by viewpoint shifting. Additionally, we investigate the continuous trajectory parameterization using Bernstein basis polynomials in trajectory decoding, allowing evaluations of states and their higher-order derivatives at any desired time point, which is valuable for downstream planning tasks. As a strong baseline, SIMPL exhibits highly competitive performance on Argoverse 1 & 2 motion forecasting benchmarks compared with other state-of-the-art methods. Furthermore, its lightweight design and low inference latency make SIMPL highly extensible and promising for real-world onboard deployment.
Universal Post-Training Reverse-Engineering Defense Against Backdoors in Deep Neural Networks

A variety of defenses have been proposed against backdoors attacks on deep neural network (DNN) classifiers. Universal methods seek to reliably detect and/or mitigate backdoors irrespective of the incorporation mechanism used by the attacker, while reverse-engineering methods often explicitly assume one. In this paper, we describe a new detector that: relies on internal feature map of the defended DNN to detect and reverse-engineer the backdoor and identify its target class; can operate post-training (without access to the training dataset); is highly effective for various incorporation mechanisms (i.e., is universal); and which has low computational overhead and so is scalable. Our detection approach is evaluated for different attacks on a benchmark CIFAR-10 image classifier.
Vi(E)va LLM! A Conceptual Stack for Evaluating and Interpreting Generative AI-based Visualizations

The automatic generation of visualizations is an old task that, through the years, has shown more and more interest from the research and practitioner communities. Recently, large language models (LLM) have become an interesting option for supporting generative tasks related to visualization, demonstrating initial promising results. At the same time, several pitfalls, like the multiple ways of instructing an LLM to generate the desired result, the different perspectives leading the generation (code-based, image-based, grammar-based), and the presence of hallucinations even for the visualization generation task, make their usage less affordable than expected. Following similar initiatives for benchmarking LLMs, this paper copes with the problem of modeling the evaluation of a generated visualization through an LLM. We propose a theoretical evaluation stack, EvaLLM, that decomposes the evaluation effort in its atomic components, characterizes their nature, and provides an overview of how to implement and interpret them. We also designed and implemented an evaluation platform that provides a benchmarking resource for the visualization generation task. The platform supports automatic and manual scoring conducted by multiple assessors to support a fine-grained and semantic evaluation based on the EvaLLM stack. Two case studies on GPT3.5-turbo with Code Interpreter and Llama2-70-b models show the benefits of EvaLLM and illustrate interesting results on the current state-of-the-art LLM-generated visualizations.
Numerical solution to a Parabolic-ODE Solow model with spatial diffusion and technology-induced motility

This work studies a parabolic-ODE PDE's system which describes the evolution of the physical capital"$k$"and technological progress"$A$", using a meshless in one and two dimensional bounded domain with regular boundary. The well-known Solow model is extended by considering the spatial diffusion of both capital anf technology. Moreover, we study the case in which no spatial diffusion of the technology progress occurs. For such models, we propound schemes based on the Generalized Finite Difference method and proof the convergence of the numerical solution to the continuous one. Several examples show the dynamics of the model for a wide range of parameters. These examples illustrate the accuary of the numerical method.