Miracle-dz/UniFAR-FTUs huggingface dataset:
Can AI-Generated Text be Reliably Detected?

The unregulated use of LLMs can potentially lead to malicious consequences such as plagiarism, generating fake news, spamming, etc. Therefore, reliable detection of AI-generated text can be critical to ensure the responsible use of LLMs. Recent works attempt to tackle this problem either using certain model signatures present in the generated text outputs or by applying watermarking techniques that imprint specific patterns onto them. In this paper, we show that these detectors are not reliable in practical scenarios. In particular, we develop a recursive paraphrasing attack to apply on AI text, which can break a whole range of detectors, including the ones using the watermarking schemes as well as neural network-based detectors, zero-shot classifiers, and retrieval-based detectors. Our experiments include passages around 300 tokens in length, showing the sensitivity of the detectors even in the case of relatively long passages. We also observe that our recursive paraphrasing only degrades text quality slightly, measured via human studies, and metrics such as perplexity scores and accuracy on text benchmarks. Additionally, we show that even LLMs protected by watermarking schemes can be vulnerable against spoofing attacks aimed to mislead detectors to classify human-written text as AI-generated, potentially causing reputational damages to the developers. In particular, we show that an adversary can infer hidden AI text signatures of the LLM outputs without having white-box access to the detection method. Finally, we provide a theoretical connection between the AUROC of the best possible detector and the Total Variation distance between human and AI text distributions that can be used to study the fundamental hardness of the reliable detection problem for advanced language models. Our code is publicly available at https://github.com/vinusankars/Reliability-of-AI-text-detectors.
Aerosol mixing over an urban region: radiative effects

Aerosols in urban regions have a distinct character as they can exhibit significant seasonal and interannual variabilities owing to variations in local emissions and long‐range transport. This diversity in aerosol sources can give rise to a complex aerosol mixture over urban regions which can influence resultant aerosol optical properties and their radiative effects. Seasonal variation in aerosol mixing state and aerosol radiative effects over Ahmedabad, an urban region in western India, are deduced from measured aerosol optical properties and a radiative transfer model. Aerosol mixing states for near‐surface single scattering albedo (SSA) at 0.55 μ m are different from those obtained for columnar SSA at 0.50 μ m in each season, emphasizing that aerosol mixing state can vary with altitude determining differences in SSA. In winter, mineral dust coated by water‐soluble aerosols emerges as the mixing state for near‐surface SSA (0.69), while black carbon coated by water‐soluble aerosols emerges as the probable mixing state for columnar SSA (0.94). Aerosol radiative forcing at the surface estimated for probable mixing states of columnar SSA follows observations during all seasons except the monsoon. Heating rate is higher for near‐surface SSA than for column SSA. Heating rates for near‐surface SSA during winter and post‐monsoon are 1 K day−1 below 3 km, but ≤0.5 K day−1 for columnar SSA. A secondary peak in heating rate profile is seen between 2 and 4 km during pre‐monsoon which is due to presence of absorbing aerosols at these altitudes. External mixing emerges as a probable mixing state during pre‐monsoon and monsoon, indicating that mixing depends on aerosol types, their abundance and meteorological conditions. These results can serve as standard regional representatives for aerosol mixing and radiative effects over urban regions. The proposed approach can be applied to other environments to determine extreme bounds of core–shell mixing of aerosols.
Versatile Depth Estimator Based on Common Relative Depth Estimation and Camera-Specific Relative-to-Metric Depth Conversion

A typical monocular depth estimator is trained for a single camera, so its performance drops severely on images taken with different cameras. To address this issue, we propose a versatile depth estimator (VDE), composed of a common relative depth estimator (CRDE) and multiple relative-to-metric converters (R2MCs). The CRDE extracts relative depth information, and each R2MC converts the relative information to predict metric depths for a specific camera. The proposed VDE can cope with diverse scenes, including both indoor and outdoor scenes, with only a 1.12\% parameter increase per camera. Experimental results demonstrate that VDE supports multiple cameras effectively and efficiently and also achieves state-of-the-art performance in the conventional single-camera scenario.
Taming Dyson-Schwinger Equations with Null States.

In quantum field theory, the Dyson-Schwinger equations are an infinite set of coupled equations relating n-point Green's functions in a self-consistent manner. They have found important applications in nonperturbative studies, ranging from quantum chromodynamics and hadron physics to strongly correlated electron systems. However, they are notoriously formidable to solve. One of the main obstacles is that a finite truncation of the infinite system is underdetermined. Recently, Bender et al. [Phys. Rev. Lett. 130, 101602 (2023)PRLTAO0031-900710.1103/PhysRevLett.130. 101602] proposed to make use of the large-n asymptotic behaviors and successfully obtained accurate results in D=0 spacetime. At higher D, it seems more difficult to deduce the large-n behaviors. In this Letter, we propose another avenue in light of the null bootstrap. The underdetermined system is solved by imposing the null state condition. This approach can be extended to D>0 more readily. As concrete examples, we show that the cases of D=0 and D=1 indeed converge to the exact results for several Hermitian and non-Hermitian theories of the gϕ^{n} type, including the complex solutions.
The kinetics of the ice-water interface from ab initio machine learning simulations.

Molecular simulations employing empirical force fields have provided valuable knowledge about the ice growth process in the past decade. The development of novel computational techniques allows us to study this process, which requires long simulations of relatively large systems, with ab initio accuracy. In this work, we use a neural-network potential for water trained on the revised Perdew-Burke-Ernzerhof functional to describe the kinetics of the ice-water interface. We study both ice melting and growth processes. Our results for the ice growth rate are in reasonable agreement with previous experiments and simulations. We find that the kinetics of ice melting presents a different behavior (monotonic) than that of ice growth (non-monotonic). In particular, a maximum ice growth rate of 6.5 Å/ns is found at 14 K of supercooling. The effect of the surface structure is explored by investigating the basal and primary and secondary prismatic facets. We use the Wilson-Frenkel relation to explain these results in terms of the mobility of molecules and the thermodynamic driving force. Moreover, we study the effect of pressure by complementing the standard isobar with simulations at a negative pressure (-1000 bar) and at a high pressure (2000 bar). We find that prismatic facets grow faster than the basal one and that pressure does not play an important role when the speed of the interface is considered as a function of the difference between the melting temperature and the actual one, i.e., to the degree of either supercooling or overheating.
Scaling Anisotropy with Stationary Background Field in Near-Sun Solar Wind Turbulence

The scaling of magnetic fluctuations provides crucial information for the understanding of solar wind turbulence. However, the observed magnetic fluctuations contain not only turbulence but also magnetic structures, leading to the violation of time stationarity. This violation would conceal the true scaling and influence the determination of the sampling angle with respect to the local background magnetic field. Here, to investigate scaling anisotropy, we utilize a simple but effective criterion ϕ < 10° to ensure the time stationarity of the magnetic field, where ϕ is the angle between the two averaged magnetic fields after cutting the interval into two halves. We study the scaling anisotropy using higher-order statistics of structure functions under the condition of stationarity for near-Sun solar wind turbulence for the first time based on measurements obtained from the Parker Solar Probe at 0.17 au. We find that the scaling indices ξ of the magnetic field show a linear dependence on the order p close to ξ(p) = p/4. The multifractal scaling of the magnetic-trace structure functions becomes monoscaling close to ξ(p) = p/3 with the local magnetic field perpendicular to the sampling direction and close to ξ(p) = p/4 with the local magnetic field parallel to the sampling direction when measured with the stationary background magnetic field. The scaling of the velocity-trace structure functions has similar but less significant changes. The near-Sun solar wind turbulence displays different scaling anisotropies from the near-Earth solar wind turbulence, suggesting the evolution of the nonlinear interaction process during solar wind expansion.
Diffusion LMS for Distributed Estimation over Wireless Networks with Inter-Node Interference Perturbation

In this paper, we investigate the diffusion least mean square (DLMS) algorithm over fading channel, where in addition to channel noise and path-loss the inter-node-interference (INI) among neighboring nodes of a host node is also taken into account. We also analyze the mean-square convergence behavior of DLMS algorithm, under such condition. In addition, based on an upper bound of the derived network MSD, an optimization problem is defined to find an optimal combination strategy. Furthermore, the adaptive version of the proposed combination strategy is presented. Simulation results corroborate the theoretical findings and indicate the superiority of the proposed combination methods over some previously reported algorithms.
Time variation of SWS spectra of M-type Mira variables ? I. Z Cyg and its dust optical properties

The M-type Mira variable star, Z Cyg, was observed with the Short-Wavelength Spectrometer (SWS) on board the Infrared Space Observatory (ISO) 7 times at roughly 60 day intervals over one and a half period. The infrared spectrum (2.38{45.2m) of Z Cyg shows prominent silicate emission bands at 10 ma nd 18m and displays quite large variations over the observed period. The variation in the infrared spectrum of Z Cyg is synchronized with the visual light curve. The circumstellar emission and the 10 mt o 18m silicate band ratio increases at maximum and decreases at minimum, indicating a variation in the dust temperature with phase. Apart from minor emission features which may be partly due to oxide dust, the observed spectra can be tted by optically thin dust shell models with one single silicate dust emissivity prole. Thus silicate is the dominant dust component in the circumstellar shell of Z Cyg. The variation in the integrated infrared flux and in the dust temperature derived from the observed spectra can be interpreted in terms of the variation in the luminosity of the central star if proper dust optical properties are adopted. Conversely, the dust emissivity can be estimated from the variations in the infrared spectrum. The derived optical properties are relatively insensitive to the assumptions made in the model analysis because of the optically thin nature of the dust shell. Possible evidence for dust formation near minimum is discussed. The observed variation of the dust shell spectrum of Z Cyg is tted most consistently with a model in which the inner dust shell temperature is 700 100 K at maximum. From the model ts we derive a ratio of the 18 ma nd 10m emission eciencies of Q(18 m)=Q(10 m) = 0:510:08. The Z Cyg dust also has a broad feature in the 20{25m region in addition to the 18m silicate band. The optical properties of dust grains around Z Cyg are compared with those in circumstellar shells of other oxygen-rich late-type stars and it is shown that there are variations in the 20m emissivity of circumstellar dust, possibly related to the presence of another dust component.
A Multitask Deep Learning Approach for Sensor-Based Human Activity Recognition and Segmentation

Deep learning (DL) for sensor-based human activity recognition (HAR) has been a focus of research in recent years. Sensor data stream segmentation is a core element in HAR, which has currently been treated as an independent preprocessing task, usually with a fixed-size window. This has led to two critical problems, namely the multiclass window problem caused by possible multiple activities within a fixed-size window and the fluctuation of prediction results due to noisy data and oversegmentation. To address these research challenges, in this article, we conceive a novel multitask DL approach to segmenting and recognizing human activity simultaneously. Specifically, we propose a multiscale window method based on feature sequence generation to overcome the multiclass window problem. We develop a novel boundary offset prediction algorithm to adjust a window’s boundary to tackle the oversegmentation issue. In addition, we design a multitask framework to streamline and optimize the activity recognition and segmentation tasks simultaneously. We conduct extensive experiments on eight benchmark datasets to evaluate the proposed framework and associated methods. Initial results show that our approach outperforms the performance of current state-of-the-art HAR methods.
Smoothing algorithms for nonsmooth and nonconvex minimization over the stiefel manifold

We consider a class of nonsmooth and nonconvex optimization problems over the Stiefel manifold where the objective function is the summation of a nonconvex smooth function and a nonsmooth Lipschitz continuous convex function composed with an linear mapping. We propose three numerical algorithms for solving this problem, by combining smoothing methods and some existing algorithms for smooth optimization over the Stiefel manifold. In particular, we approximate the aforementioned nonsmooth convex function by its Moreau envelope in our smoothing methods, and prove that the Moreau envelope has many favorable properties. Thanks to this and the scheme for updating the smoothing parameter, we show that any accumulation point of the solution sequence generated by the proposed algorithms is a stationary point of the original optimization problem. Numerical experiments on building graph Fourier basis are conducted to demonstrate the efficiency of the proposed algorithms.
Investigating Topological Order using Recurrent Neural Networks

Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems. Here, we employ 2D RNNs to investigate two prototypical quantum many-body Hamiltonians exhibiting topological order. Specifically, we demonstrate that RNN wave functions can effectively capture the topological order of the toric code and a Bose-Hubbard spin liquid on the kagome lattice by estimating their topological entanglement entropies. We also find that RNNs favor coherent superpositions of minimally-entangled states over minimally-entangled states themselves. Overall, our findings demonstrate that RNN wave functions constitute a powerful tool to study phases of matter beyond Landau's symmetry-breaking paradigm.
Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations

An important line of research attempts to explain CNN image classifier predictions and intermediate layer representations in terms of human understandable concepts. In this work, we expand on previous works in the literature that use annotated concept datasets to extract interpretable feature space directions and propose an unsupervised post-hoc method to extract a disentangling interpretable basis by looking for the rotation of the feature space that explains sparse one-hot thresholded transformed representations of pixel activations. We do experimentation with existing popular CNNs and demonstrate the effectiveness of our method in extracting an interpretable basis across network architectures and training datasets. We make extensions to the existing basis interpretability metrics found in the literature and show that, intermediate layer representations become more interpretable when transformed to the bases extracted with our method. Finally, using the basis interpretability metrics, we compare the bases extracted with our method with the bases derived with a supervised approach and find that, in one aspect, the proposed unsupervised approach has a strength that constitutes a limitation of the supervised one and give potential directions for future research.
Parameter-Free Channel Attention for Image Classification and Super-Resolution

The channel attention mechanism is a useful technique widely employed in deep convolutional neural networks to boost the performance for image processing tasks, eg, image classification and image super-resolution. It is usually designed as a parameterized sub-network and embedded into the convolutional layers of the network to learn more powerful feature representations. However, current channel attention induces more parameters and therefore leads to higher computational costs. To deal with this issue, in this work, we propose a Parameter-Free Channel Attention (PFCA) module to boost the performance of popular image classification and image super-resolution networks, but completely sweep out the parameter growth of channel attention. Experiments on CIFAR-100, ImageNet, and DIV2K validate that our PFCA module improves the performance of ResNet on image classification and improves the performance of MSRResNet on image super-resolution tasks, respectively, while bringing little growth of parameters and FLOPs.
Timely Status Update in Relay-Assisted Cooperative Communications

We investigate the age of information (AoI) of relay-assisted cooperative communication systems. For time-slotted systems without relaying, prior works have shown that the source should generate and send a new packet to the destination every time slot to minimize the average AoI, regardless of whether the destination has successfully decoded the packet in the previous slot. However, when a dedicated relay is involved, whether the relay can improve the AoI performance requires an in-depth study. Depending on whether the source and the relay are allowed to transmit simultaneously, two relay-assisted schemes are investigated: time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes. In TDMA, the source generates and sends a new packet every other time slot to avoid possible simultaneous transmission with the relay. In NOMA, the source generates and sends a new packet every time slot, thus possibly forming simultaneous transmission from the relay and the source. A key challenge in deriving their theoretical average AoI is that the destination has different probabilities of successfully receiving an update packet in different time slots. We model each scheme using a Markov chain to derive the corresponding closed-form average AoI. Interestingly, our theoretical analysis indicates that the relay-assisted schemes can only outperform the non-relay scheme in average AoI when the signal-to-noise ratio of the source-destination link is below $-2\,\text{dB}$ dB. Furthermore, comparing the merits of relay-assisted schemes, simulation results show that the TDMA scheme has a lower energy consumption, while the NOMA counterpart typically achieves a lower average AoI.
Efficient Estimation for Longitudinal Networks via Adaptive Merging

Longitudinal network consists of a sequence of temporal edges among multiple nodes, where the temporal edges are observed in real time. It has become ubiquitous with the rise of online social platform and e-commerce, but largely under-investigated in literature. In this paper, we propose an efficient estimation framework for longitudinal network, leveraging strengths of adaptive network merging, tensor decomposition and point process. It merges neighboring sparse networks so as to enlarge the number of observed edges and reduce estimation variance, whereas the estimation bias introduced by network merging is controlled by exploiting local temporal structures for adaptive network neighborhood. A projected gradient descent algorithm is proposed to facilitate estimation, where the upper bound of the estimation error in each iteration is established. A thorough analysis is conducted to quantify the asymptotic behavior of the proposed method, which shows that it can significantly reduce the estimation error and also provides guideline for network merging under various scenarios. We further demonstrate the advantage of the proposed method through extensive numerical experiments on synthetic datasets and a militarized interstate dispute dataset.
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing

Most of the current literature focused on centralized learning is centered around the celebrated average-consensus paradigm and less attention is devoted to scenarios where the communication between the agents may be imperfect. This letter presents three different algorithms of Decentralized Federated Learning (DFL) in the presence of imperfect information sharing modeled as noisy communication channels. The first algorithm, Federated Noisy Decentralized Learning (FedNDL1) comes from the literature, where the noise is added to the algorithm parameters to simulate the scenario of the presence of noisy communication channels. This algorithm shares parameters to form a consensus with the clients based on a communication graph topology through a noisy communication channel. The proposed second algorithm (FedNDL2) is similar to the first algorithm but with added noise to the parameters and it performs the gossip averaging before the gradient optimization. The proposed third algorithm (FedNDL3), on the other hand, shares the gradients through noisy communication channels instead of the parameters. Theoretical and experimental results show that under imperfect information sharing, the third scheme that mixes gradients is more robust in the presence of a noisy channel compared with the algorithms from the literature that mix the parameters.
AugGPT: Leveraging ChatGPT for Text Data Augmentation

Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning scenario, where the data in the target domain is generally much scarcer and of lowered quality. A natural and widely-used strategy to mitigate such challenges is to perform data augmentation to better capture the data invariance and increase the sample size. However, current text data augmentation methods either can't ensure the correct labeling of the generated data (lacking faithfulness) or can't ensure sufficient diversity in the generated data (lacking compactness), or both. Inspired by the recent success of large language models, especially the development of ChatGPT, which demonstrated improved language comprehension abilities, in this work, we propose a text data augmentation approach based on ChatGPT (named AugGPT). AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. The augmented samples can then be used in downstream model training. Experiment results on few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples.