Miracle-dz/UniFAR-FTUs huggingface dataset:
FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing Fourier Transform and Auto-encoder

The success of deep learning-based generative models in producing realistic images, videos, and audios has led to a crucial consideration: how to effectively assess the quality of synthetic samples. While the Fr\'{e}chet Inception Distance (FID) serves as the standard metric for evaluating generative models in image synthesis, a comparable metric for time series data is notably absent. This gap in assessment capabilities stems from the absence of a widely accepted feature vector extractor pre-trained on benchmark time series datasets. In addressing these challenges related to assessing the quality of time series, particularly in the context of Fr\'echet Distance, this work proposes a novel solution leveraging the Fourier transform and Auto-encoder, termed the Fr\'{e}chet Fourier-transform Auto-encoder Distance (FFAD). Through our experimental results, we showcase the potential of FFAD for effectively distinguishing samples from different classes. This novel metric emerges as a fundamental tool for the evaluation of generative time series data, contributing to the ongoing efforts of enhancing assessment methodologies in the realm of deep learning-based generative models.
Unique reconstruction for discretized inverse problems: a random sketching approach

Inverse problem theory is often studied in the ideal infinite-dimensional setting. Through the lens of the PDE-constrained optimization, the well-posedness PDE theory suggests unique reconstruction of the parameter function that attain the zero-loss property of the mismatch function, when infinite amount of data is provided. Unfortunately, this is not the case in practice, when we are limited to finite amount of measurements due to experimental or economical reasons. Consequently, one must compromise the inference goal to a discrete approximation of the unknown smooth function. What is the reconstruction power of a fixed number of data observations? How many parameters can one reconstruct? Here we describe a probabilistic approach, and spell out the interplay of the observation size $(r)$ and the number of parameters to be uniquely identified $(m)$. The technical pillar is the random sketching strategy, in which the matrix concentration inequality and sampling theory are largely employed. By analyzing randomly sub-sampled Hessian matrix, we attain well-conditioned reconstruction problem with high probability. Our main theory is finally validated in numerical experiments. We set tests on both synthetic and the data from an elliptic inverse problem. The empirical performance shows that given suitable sampling quality, the well-conditioning of the sketched Hessian is certified with high probability.
Inception Attacks: Immersive Hijacking in Virtual Reality Systems

Recent advances in virtual reality (VR) system provide fully immersive interactions that connect users with online resources, applications, and each other. Yet these immersive interfaces can make it easier for users to fall prey to a new type of security attacks. We introduce the inception attack, where an attacker controls and manipulates a user's interaction with their VR environment and applications, by trapping them inside a malicious VR application that masquerades as the full VR system. Once trapped in an"inception VR layer", all of the user's interactions with remote servers, network applications, and other VR users can be recorded or modified without their knowledge. This enables traditional attacks (recording passwords and modifying user actions in flight), as well as VR interaction attacks, where (with generative AI tools) two VR users interacting can experience two dramatically different conversations. In this paper, we introduce inception attacks and their design, and describe our implementation that works on all Meta Quest VR headsets. Our implementation of inception attacks includes a cloned version of the Meta Quest browser that can modify data as it's displayed to the user, and alter user input en route to the server (e.g. modify amount of $ transferred in a banking session). Our implementation also includes a cloned VRChat app, where an attacker can eavesdrop and modify live audio between two VR users. We then conduct a study on users with a range of VR experiences, execute the inception attack during their session, and debrief them about their experiences. Only 37% of users noticed the momentary visual"glitch"when the inception attack began, and all but 1 user attributed it to imperfections in the VR platform. Finally, we consider and discuss efficacy and tradeoffs for a wide range of potential inception defenses.
A New Estimate for Neanderthal Energy Expenditure

Past studies exploring Neanderthal total energy expenditure have done so using an additive approach. With this method, higher energy expenditure totals relative to modern day humans have been reported for Neanderthals, thus reinforcing long held beliefs about our closest ancestor’s biology and behavior. Recently, however, explorations into a variety of disciplines within the field of anthropology have provided evidence that is changing some of these perceptions. Supported by energy expenditure and allocation studies suggesting a more complex relationship between total energy expenditure and physical activity than has previously been accounted for, this study presents a new estimate for Neanderthal total energy expenditure through the use of a constrained energy model. The new estimates for Neanderthal are then considered within the context of some recent analyses investigating Neanderthal life history traits, genetics, and the archaeological record. Together, the data supports the TEE estimates shown here and the likelihood that Neanderthals expended more moderate levels of energy similar to that seen in modern day hunter-gatherer groups such as the Hadza or the Yakut.
HILL: A Hallucination Identifier for Large Language Models

Large language models (LLMs) are prone to hallucinations, i.e., nonsensical, unfaithful, and undesirable text. Users tend to overrely on LLMs and corresponding hallucinations which can lead to misinterpretations and errors. To tackle the problem of overreliance, we propose HILL, the"Hallucination Identifier for Large Language Models". First, we identified design features for HILL with a Wizard of Oz approach with nine participants. Subsequently, we implemented HILL based on the identified design features and evaluated HILL's interface design by surveying 17 participants. Further, we investigated HILL's functionality to identify hallucinations based on an existing question-answering dataset and five user interviews. We find that HILL can correctly identify and highlight hallucinations in LLM responses which enables users to handle LLM responses with more caution. With that, we propose an easy-to-implement adaptation to existing LLMs and demonstrate the relevance of user-centered designs of AI artifacts.
Q-CHOP: Quantum constrained Hamiltonian optimization

Combinatorial optimization problems that arise in science and industry typically have constraints. Yet the presence of constraints makes them challenging to tackle using both classical and quantum optimization algorithms. We propose a new quantum algorithm for constrained optimization, which we call quantum constrained Hamiltonian optimization (Q-CHOP). Our algorithm leverages the observation that for many problems, while the best solution is difficult to find, the worst feasible (constraint-satisfying) solution is known. The basic idea is to to enforce a Hamiltonian constraint at all times, thereby restricting evolution to the subspace of feasible states, and slowly"rotate"an objective Hamiltonian to trace an adiabatic path from the worst feasible state to the best feasible state. We additionally propose a version of Q-CHOP that can start in any feasible state. Finally, we benchmark Q-CHOP against the commonly-used adiabatic algorithm with constraints enforced using a penalty term and find that Q-CHOP performs consistently better on a wide range of problems, including textbook problems on graphs, knapsack, combinatorial auction, as well as a real-world financial use case, namely bond exchange-traded fund basket optimization.
Creation of Cold Spell Year in cold climate zones

Climate change not only caused mean land surface temperature rise, but also led to increase in frequency and severity of extreme events, which would have a significant impact on thermal performance of buildings. The risk of overheating and overcooling is dependent upon the outdoor climate and the building design. Overheating risk has received continuous attention under warming climate but overcooling risk. This study aims to create a new Cold Spell Year (CSY) for assessing thermal resilience of buildings taking account of extremely cold events under current climate. Thermal performance of 128 typical residential buildings located in China’s severe cold climate zone were simulated with 72-year long hourly weather data to identify all the historical cold events in terms of their impacts on the risk of overcooling. A new cold spell definition was proposed based on the analysis of characteristics of the cold events. Then, the CSY was created based on the new cold spell definition to ensure the inclusion of the cold events which could have a significant influence on thermal resilience of most buildings. Together with the existing extreme hot years, the CSY would be highly useful in improving thermal resilience of buildings under extreme events.
Electromagnetic Hybrid Beamforming for Holographic Communications

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) superdirective holographic antenna array. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves the real-time adjustment of the radiation pattern to adapt it to the dynamic wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beamforming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation patterns and the mutual coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results demonstrate that our proposed EHB scheme with a 3D holographic array achieves a relatively flat superdirective beamforming gain and allows for programmable focusing directions throughout the entire spatial domain. Furthermore, they also verify that the proposed scheme achieves a sum rate gain of over 150% compared to traditional beamforming algorithms.
Study of dark matter scattering off ${}^2$H and ${}^4$He nuclei within chiral effective field theory

We study dark matter, assumed to be composed by weak interacting massive particles (WIMPs), scattering off ${}^2$H and ${}^4$He nuclei. In order to parameterize the WIMP-nucleon interaction the chiral effective field theory approach is used. Considering only interactions invariant under parity, charge conjugation and time reversal, we examine five interaction types: scalar, pseudoscalar, vector, axial and tensor. Scattering amplitudes between two nucleons and a WIMP are determined up to second order of chiral perturbation theory. We apply this program to calculate the interaction rate as function of the WIMP mass and of the magnitude of the WIMP-quark coupling constants. From our study, we conclude that the scalar nuclear response functions result much greater than the others due to theirs large combination of low energy constants. We verify that the leading order contributions are dominant in this low energy processes. We also provide an estimate for the background due to atmospheric neutrinos.
DNNShield: Embedding Identifiers for Deep Neural Network Ownership Verification

The surge in popularity of machine learning (ML) has driven significant investments in training Deep Neural Networks (DNNs). However, these models that require resource-intensive training are vulnerable to theft and unauthorized use. This paper addresses this challenge by introducing DNNShield, a novel approach for DNN protection that integrates seamlessly before training. DNNShield embeds unique identifiers within the model architecture using specialized protection layers. These layers enable secure training and deployment while offering high resilience against various attacks, including fine-tuning, pruning, and adaptive adversarial attacks. Notably, our approach achieves this security with minimal performance and computational overhead (less than 5\% runtime increase). We validate the effectiveness and efficiency of DNNShield through extensive evaluations across three datasets and four model architectures. This practical solution empowers developers to protect their DNNs and intellectual property rights.
Cluster Before You Hallucinate: Approximating Node-Capacitated Network Design and Energy Efficient Routing

We consider the following node-capacitated network design problem. The input is an undirected graph, set of demands, uniform node capacity and arbitrary node costs. The goal is to find a minimum node-cost subgraph that supports all demands concurrently subject to the node capacities. We consider both single and multi-commodity demands, and provide the first poly-logarithmic approximation guarantees. For single-commodity demands (i.e., all request pairs have the same sink node), we obtain an $O(\log^2 n)$ approximation to the cost with an $O(\log^3 n)$ factor violation in node capacities. For multi-commodity demands, we obtain an $O(\log^4 n)$ approximation to the cost with an $O(\log^{10} n)$ factor violation in node capacities. We use a variety of techniques, including single-sink confluent flows, low-load set cover, random sampling and cut-sparsification. We also develop new techniques for clustering multicommodity demands into (nearly) node-disjoint clusters, which may be of independent interest. Moreover, this network design problem has applications to energy-efficient virtual circuit routing. In this setting, there is a network of routers that are speed scalable, and that may be shutdown when idle. We assume the standard model for power: the power consumed by a router with load (speed) $s$ is $\sigma + s^\alpha$ where $\sigma$ is the static power and the exponent $\alpha>1$. We obtain the first poly-logarithmic approximation algorithms for this problem when speed-scaling occurs on nodes of a network.
Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC). The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses. Specifically, LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses, respectively. Then, LSC explores the distributions of points in the two latent semantic spaces, to remove outliers, generate high-quality model hypotheses, and effectively estimate model instances. Finally, LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting, due to its deterministic fitting nature and efficiency. Compared with several state-of-the-art model fitting methods, our LSC achieves significant superiority for the performance of both accuracy and speed on synthetic data and real images. The code will be available at https://github.com/guobaoxiao/LSC.
Detecting a Proxy for Potential Comorbid ADHD in People Reporting Anxiety Symptoms from Social Media Data

We present a novel task that can elucidate the connection between anxiety and ADHD; use Transformers to make progress toward solving a task that is not solvable by keyword-based classifiers; and discuss a method for visualization of our classifier illuminating the connection between anxiety and ADHD presentations. Up to approximately 50% of adults with ADHD may also have an anxiety disorder and approximately 30% of adults with anxiety may also have ADHD. Patients presenting with anxiety may be treated for anxiety without ADHD ever being considered, possibly affecting treatment. We show how data that bears on ADHD that is comorbid with anxiety can be obtained from social media data, and show that Transformers can be used to detect a proxy for possible comorbid ADHD in people with anxiety symptoms. We collected data from anxiety and ADHD online forums (subreddits). We identified posters who first started posting in the Anxiety subreddit and later started posting in the ADHD subreddit as well. We use this subset of the posters as a proxy for people who presented with anxiety symptoms and then became aware that they might have ADHD. We fine-tune a Transformer architecture-based classifier to classify people who started posting in the Anxiety subreddit and then started posting in the ADHD subreddit vs. people who posted in the Anxiety subreddit without later posting in the ADHD subreddit. We show that a Transformer architecture is capable of achieving reasonable results (76% correct for RoBERTa vs. under 60% correct for the best keyword-based model, both with 50% base rate).
SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data

In the field of data mining and machine learning, commonly used classification models cannot effectively learn in unbalanced data. In order to balance the data distribution before model training, oversampling methods are often used to generate data for a small number of classes to solve the problem of classifying unbalanced data. Most of the classical oversampling methods are based on the SMOTE technique, which only focuses on the local information of the data, and therefore the generated data may have the problem of not being realistic enough. In the current oversampling methods based on generative networks, the methods based on GANs can capture the true distribution of data, but there is the problem of pattern collapse and training instability in training; in the oversampling methods based on denoising diffusion probability models, the neural network of the inverse diffusion process using the U-Net is not applicable to tabular data, and although the MLP can be used to replace the U-Net, the problem exists due to the simplicity of the structure and the poor effect of removing noise. problem of poor noise removal. In order to overcome the above problems, we propose a novel oversampling method SEMRes-DDPM.In the SEMRes-DDPM backward diffusion process, a new neural network structure SEMST-ResNet is used, which is suitable for tabular data and has good noise removal effect, and it can generate tabular data with higher quality. Experiments show that the SEMResNet network removes noise better than MLP; SEMRes-DDPM generates data distributions that are closer to the real data distributions than TabDDPM with CWGAN-GP; on 20 real unbalanced tabular datasets with 9 classification models, SEMRes-DDPM improves the quality of the generated tabular data in terms of three evaluation metrics (F1, G-mean, AUC) with better classification performance than other SOTA oversampling methods.
Fitting Waveform Envelopes to Derive Focal Mechanisms of Moderate Earthquakes

Focal mechanisms of shallow crustal earthquakes with magnitude 4 or less remain unresolved in many regions of the world due to the sparseness of seismic networks. Relatively distant stations must be used, but modeling those waveforms is difficult due to imprecision of seismic-velocity models. The waveform inversions also suffer from instrumental problems, such as flipped channel sign and errors in gain or timing. In such situations, waveforms should therefore be substituted by a robust characteristic which still carries information about focal mechanism and moment magnitude. We propose inversion of waveform envelopes. Three application examples demonstrate the performance of the method for earthquakes in various ranges of epicentral distance and frequency. In the examples, the focal mechanisms of the sample earthquakes are known from the waveform inversion approach based on near-source stations. The near-source stations are then ignored completely. Use of the remaining stations fails to provide the true mechanism through waveform inversion, but a correct estimate of the solution is found using envelopes. This method may be of interest to seismologists who need to compute focal mechanisms for stress-field studies in sparse networks, where traditional waveform inversion and amplitude inversion methods often fail. Electronic Supplement: Figures and text illustrating robustness of envelopes relative to waveforms, problems when increasing frequency, importance of the posterior polarity constraint, and synthetic tests addressing the effects of noise and imprecise
Topological solitons stabilized by a background gauge field and soliton-anti-soliton asymmetry

We study topological lumps supported by the second homotopy group $\pi_2(S^2) \simeq {\mathbb Z}$ in a gauged $O(3)$ model without any potential term coupled with a (non)dynamical $U(1)$ gauge field. It is known that gauged-lumps are stable with an easy-plane potential term but are unstable to expand if the model has no potential term. In this paper, we find that these gauged lumps without a potential term can be made stable by putting them in a uniform magnetic field, irrespective of whether the gauge field is dynamical or not. In the case of the non-dynamical gauge field, only either of lumps or anti-lumps stably exists depending on the sign of the background magnetic field, and the other is unstable to shrink to be singular. We also construct coaxial multiple lumps whose size and mass exhibit a behaviour of droplets. In the case of the dynamical gauge field, both the lumps and anti-lumps stably exist with different masses; the lighter (heavier) one corresponds to the (un)stable one in the case of the nondynamical gauge field. We find that a lump behaves as a superconducting ring and traps magnetic field in its inside, with the total magnetic field reduced from the background magnetic field.
Speech Enhancement using Deep Learning Techniques

In current era of digital technology, speech enhancement has become one of the important challenges in day-to-day life for smoother conversation and understanding. Most of the recorded or live speech audio signals contain some form of noise such as street noise, barking sounds of dogs, conversation between multiple people, construction noise, car honking etc. The key feature of speech enhancement system is to execute in real time on a recorded waveform with minimal possible lag on any localized hardware with minimum computational resource.