Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship Between Mood, Sleepiness, and EEG Functional Connectivity by 40 Hz Monaural Beats

The monaural beat is known that it can modulate brain and personal states. However, which changes in brain waves are related to changes in state is still unclear. Therefore, we aimed to investigate the effects of monaural beats and find the relationship between them. Ten participants took part in five separate random sessions, which included a baseline session and four sessions with monaural beats stimulation: one audible session and three inaudible sessions. Electroencephalogram (EEG) were recorded and participants completed pre- and post-stimulation questionnaires assessing mood and sleepiness. As a result, audible session led to increased arousal and positive mood compared to other conditions. From the neurophysiological analysis, statistical differences in frontal-central, central-central, and central-parietal connectivity were observed only in the audible session. Furthermore, a significant correlation was identified between sleepiness and EEG power in the temporal and occipital regions. These results suggested a more detailed correlation for stimulation to change its personal state. These findings have implications for applications in areas such as cognitive enhancement, mood regulation, and sleep management.
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree transformation

Large-scale language models have made great progress in the field of software engineering in recent years. They can be used for many code-related tasks such as code clone detection, code-to-code search, and method name prediction. However, these large-scale language models based on each code token have several drawbacks: They are usually large in scale, heavily dependent on labels, and require a lot of computing power and time to fine-tune new datasets.Furthermore, code embedding should be performed on the entire code snippet rather than encoding each code token. The main reason for this is that encoding each code token would cause model parameter inflation, resulting in a lot of parameters storing information that we are not very concerned about. In this paper, we propose a novel framework, called TransformCode, that learns about code embeddings in a contrastive learning manner. The framework uses the Transformer encoder as an integral part of the model. We also introduce a novel data augmentation technique called abstract syntax tree transformation: This technique applies syntactic and semantic transformations to the original code snippets to generate more diverse and robust anchor samples. Our proposed framework is both flexible and adaptable: It can be easily extended to other downstream tasks that require code representation such as code clone detection and classification. The framework is also very efficient and scalable: It does not require a large model or a large amount of training data, and can support any programming language.Finally, our framework is not limited to unsupervised learning, but can also be applied to some supervised learning tasks by incorporating task-specific labels or objectives. To explore the effectiveness of our framework, we conducted extensive experiments on different software engineering tasks using different programming languages and multiple datasets.
Revisiting Stylized Facts for Modern Stock Markets

In 2001, Rama Cont introduced a now-widely used set of ‘stylized facts’ to synthesize empirical studies of financial time series, resulting in 11 qualitative properties presumed to be universal to all financial markets. Here, we replicate Cont’s analyses for a convenience sample of stocks drawn from the U.S. stock market following a fundamental shift in market regulation. Our study relies on the same authoritative data as that used by the U.S. regulator. We find conclusive evidence in the modern market for eight of Cont’s original facts, while we find weak support for one additional fact and no support for the remaining two. Our study represents the first test of the original set of 11 stylized facts against a consistent set of stocks, therefore providing insight into how Cont’s stylized facts should be viewed in the context of modern stock markets.
What Holes in the Gas Distribution of Nearly Face-on Galaxies Can Tell Us about the Host Disk Parameters: The Case of the NGC 628 Southeast Superbubble

Here we explore the impact of all major factors, such as the nonhomogeneous gas distribution, galactic rotation, and gravity, on the observational appearance of superbubbles in nearly face-on spiral galaxies. The results of our 3D numerical simulations are compared to the observed gas column density distribution in the largest southeast superbubble in the late-type spiral galaxy NGC 628. We make use of the star formation history inside the bubble derived from the resolved stellar population seen in Hubble Space Telescope images to obtain its energy and demonstrate that the results of numerical simulations are in good agreement with the observed gas surface density distribution. We also show that the observed gas column density distribution constrains the gaseous disk scale height and the midplane gas density if the energy input rate can be obtained from observations. This implies that observations of large holes in the interstellar gas distribution and their stellar populations have the potential power to solve the midplane gas density–gaseous disk scale height degeneracy problem in nearly face-on galaxies. The possible role of superbubbles in driving the secondary star formation in galaxies is also briefly discussed.
Magnetic Fields of the Starless Core L 1512

We present JCMT POL-2 850 μm dust polarization observations and Mimir H-band stellar polarization observations toward the starless core L 1512. We detect the highly ordered core-scale magnetic field traced by the POL-2 data, of which the field orientation is consistent with the parsec-scale magnetic fields traced by Planck data, suggesting the large-scale fields thread from the low-density region to the dense core region in this cloud. The surrounding magnetic field traced by the Mimir data shows a wider variation in the field orientation, suggesting there could be a transition of magnetic field morphology at the envelope-scale. L 1512 was suggested to be presumably older than 1.4 Myr in a previous study via time-dependent chemical analysis, hinting that the magnetic field could be strong enough to slow the collapse of L 1512. In this study, we use the Davis–Chandrasekhar–Fermi method to derive a plane-of-sky magnetic field strength (B pos) of 18 ± 7 μG and an observed mass-to-flux ratio (λ obs) of 3.5 ± 2.4, suggesting that L 1512 is magnetically supercritical. However, the absence of significant infall motion and the presence of an oscillating envelope are inconsistent with the magnetically supercritical condition. Using a virial analysis, we suggest the presence of a hitherto hidden line-of-sight magnetic field strength of ∼27 μG with a mass-to-flux ratio (λ tot) of ∼1.6, in which case both magnetic and kinetic pressures are important in supporting the L 1512 core. On the other hand, L 1512 may have just reached supercriticality and will collapse at any time.
Challenges in molecular dynamics simulations of heat exchange statistics.

We study heat exchange in temperature-biased metal-molecule-metal molecular junctions by employing the molecular dynamics simulator LAMMPS. Generating the nonequilibrium steady state with Langevin thermostats at the boundaries of the junction, we show that the average heat current across a gold-alkanedithiol-gold nanojunction behaves physically, with the thermal conductance value matching the literature. In contrast, the full probability distribution function for heat exchange, as generated by the simulator, violates the fundamental fluctuation symmetry for entropy production. We trace this failure back to the implementation of the thermostats and the expression used to calculate the heat exchange. To rectify this issue and produce the correct statistics, we introduce single-atom thermostats as an alternative to conventional many-atom thermostats. Once averaging heat exchange over the hot and cold thermostats, this approach successfully generates the correct probability distribution function, which we use to study the behavior of both the average heat current and its noise. We further examine the thermodynamic uncertainty relation in the molecular junction and show that it holds, albeit demonstrating nontrivial trends. Our study points to the need to carefully implement nonequilibrium molecular dynamics solvers in atomistic simulation software tools for future investigations of noise phenomena in thermal transport.
Increasing the Efficiency of Cryptoasset Investigations by Connecting the Cases

Law enforcement agencies are confronted with a rapidly growing number of cryptoasset-related cases, often redundantly investigating the same cases without mutual knowledge or shared insights. In this paper, we explore the hypothesis that recognizing and acting upon connections between these cases can significantly streamline investigative processes. Through an analysis of a dataset comprising 34 cyberfraud and 1793 sextortion spam cases, we discovered that 41% of the cyberfraud and 96.9% of the sextortion spam incidents can be interconnected. We introduce a straightforward yet effective tool, which is integrated into a broader cryptoasset forensics workflow and allows investigators to highlight and share case connections. Our research unequivocally demonstrates that recognizing case connections can lead to remarkable efficiencies, especially when extended across crime areas, international borders, and jurisdictions.
Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks

Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cross-lingual sharing. In this work, we investigate (1) the degree to which language-wise modularity naturally arises within models with no special modularity interventions, and (2) how cross-lingual sharing and interference differ between such models and those with explicit SFT-guided subnetwork modularity. To quantify language specialization and cross-lingual interaction, we use a Training Data Attribution method that estimates the degree to which a model's predictions are influenced by in-language or cross-language training examples. Our results show that language-specialized subnetworks do naturally arise, and that SFT, rather than always increasing modularity, can decrease language specialization of subnetworks in favor of more cross-lingual sharing.
$\delta N$ formalism on the past light-cone

We apply the gradient expansion approximation to the light-cone gauge, obtaining a separate universe picture at non-linear order in perturbation theory within this framework. Thereafter, we use it to generalize the $\delta N$ formalism in terms of light-cone perturbations. As a consistency check, we demonstrate the conservation of the gauge invariant curvature perturbation on uniform density hypersurface $\zeta$ at the completely non-linear level. The approach studied provides a self-consistent framework to connect at non-linear level quantities from the primordial universe, such as $\zeta$, written in terms of the light-cone parameters, to late time observables.
Use of Equivalent Relative Utility (ERU) to Evaluate Artificial Intelligence-Enabled Rule-Out Devices

We investigated the use of equivalent relative utility (ERU) to evaluate the effectiveness of artificial intelligence (AI)-enabled rule-out devices that use AI to identify and autonomously remove non-cancer patient images from radiologist review in screening mammography.We reviewed two performance metrics that can be used to compare the diagnostic performance between the radiologist-with-rule-out-device and radiologist-without-device workflows: positive/negative predictive values (PPV/NPV) and equivalent relative utility (ERU). To demonstrate the use of the two evaluation metrics, we applied both methods to a recent US-based study that reported an improved performance of the radiologist-with-device workflow compared to the one without the device by retrospectively applying their AI algorithm to a large mammography dataset. We further applied the ERU method to a European study utilizing their reported recall rates and cancer detection rates at different thresholds of their AI algorithm to compare the potential utility among different thresholds. For the study using US data, neither the PPV/NPV nor the ERU method can conclude a significant improvement in diagnostic performance for any of the algorithm thresholds reported. For the study using European data, ERU values at lower AI thresholds are found to be higher than that at a higher threshold because more false-negative cases would be ruled-out at higher threshold, reducing the overall diagnostic performance. Both PPV/NPV and ERU methods can be used to compare the diagnostic performance between the radiologist-with-device workflow and that without. One limitation of the ERU method is the need to measure the baseline, standard-of-care relative utility (RU) value for mammography screening in the US. Once the baseline value is known, the ERU method can be applied to large US datasets without knowing the true prevalence of the dataset.
Probing clustering in neural network representations

Neural network representations contain structure beyond what was present in the training labels. For instance, representations of images that are visually or semantically similar tend to lie closer to each other than to dissimilar images, regardless of their labels. Clustering these representations can thus provide insights into dataset properties as well as the network internals. In this work, we study how the many design choices involved in neural network training affect the clusters formed in the hidden representations. To do so, we establish an evaluation setup based on the BREEDS hierarchy, for the task of subclass clustering after training models with only superclass information. We isolate the training dataset and architecture as important factors affecting clusterability. Datasets with labeled classes consisting of unrelated subclasses yield much better clusterability than those following a natural hierarchy. When using pretrained models to cluster representations on downstream datasets, models pretrained on subclass labels provide better clusterability than models pretrained on superclass labels, but only when there is a high degree of domain overlap between the pretraining and downstream data. Architecturally, we find that normalization strategies affect which layers yield the best clustering performance, and, surprisingly, Vision Transformers attain lower subclass clusterability than ResNets.
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees

Hybrid RL is the setting where an RL agent has access to both offline data and online data by interacting with the real-world environment. In this work, we propose a new hybrid RL algorithm that combines an on-policy actor-critic method with offline data. On-policy methods such as policy gradient and natural policy gradient (NPG) have shown to be more robust to model misspecification, though sometimes it may not be as sample efficient as methods that rely on off-policy learning. On the other hand, offline methods that depend on off-policy training often require strong assumptions in theory and are less stable to train in practice. Our new approach integrates a procedure of off-policy training on the offline data into an on-policy NPG framework. We show that our approach, in theory, can obtain a best-of-both-worlds type of result -- it achieves the state-of-art theoretical guarantees of offline RL when offline RL-specific assumptions hold, while at the same time maintaining the theoretical guarantees of on-policy NPG regardless of the offline RL assumptions' validity. Experimentally, in challenging rich-observation environments, we show that our approach outperforms a state-of-the-art hybrid RL baseline which only relies on off-policy policy optimization, demonstrating the empirical benefit of combining on-policy and off-policy learning. Our code is publicly available at https://github.com/YifeiZhou02/HNPG.
Test-Time Training for Semantic Segmentation with Output Contrastive Loss

Although deep learning-based segmentation models have achieved impressive performance on public benchmarks, generalizing well to unseen environments remains a major challenge. To improve the model's generalization ability to the new domain during evaluation, the test-time training (TTT) is a challenging paradigm that adapts the source-pretrained model in an online fashion. Early efforts on TTT mainly focus on the image classification task. Directly extending these methods to semantic segmentation easily experiences unstable adaption due to segmentation's inherent characteristics, such as extreme class imbalance and complex decision spaces. To stabilize the adaptation process, we introduce contrastive loss (CL), known for its capability to learn robust and generalized representations. Nevertheless, the traditional CL operates in the representation space and cannot directly enhance predictions. In this paper, we resolve this limitation by adapting the CL to the output space, employing a high temperature, and simplifying the formulation, resulting in a straightforward yet effective loss function called Output Contrastive Loss (OCL). Our comprehensive experiments validate the efficacy of our approach across diverse evaluation scenarios. Notably, our method excels even when applied to models initially pre-trained using domain adaptation methods on test domain data, showcasing its resilience and adaptability. \footnote{Code and more information could be found at~ \url{https://github.com/dazhangyu123/OCL}}
Lite it fly: An All-Deformable-Butterfly Network

Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matrix and a data matrix obtained by arranging feature tensors into columns. Recently proposed deformable butterfly (DeBut) decomposes the filter matrix into generalized, butterfly-like factors, thus achieving network compression orthogonal to the traditional ways of pruning or low-rank decomposition. This work reveals an intimate link between DeBut and a systematic hierarchy of depthwise and pointwise convolutions, which explains the empirically good performance of DeBut layers. By developing an automated DeBut chain generator, we show for the first time the viability of homogenizing a DNN into all DeBut layers, thus achieving extreme sparsity and compression. Various examples and hardware benchmarks verify the advantages of All-DeBut networks. In particular, we show it is possible to compress a PointNet to 5% parameters with 5% accuracy drop, a record not achievable by other compression schemes.
Melting and Phase Relations of Carbonated Eclogite at 9–21 GPa and the Petrogenesis of Alkali-Rich Melts in the Deep Mantle

The melting and phase relations of carbonated MORB eclogite have been investigated using the multi-anvil technique at 9–21 GPa and 1100–1900°C. The starting compositions were two synthetic mixes, GA1 and Volga, with the CO2 component added as CaCO3 (cc): GA1 + 10%cc (GA1cc) models altered oceanic crust recycled into the convecting mantle via subduction, and Volga + 10%cc (Volga-cc) models subducted oceanic crust that has lost some of its siliceous component in the sub-arc regime (GA1 minus 6·5 wt % SiO2). The subsolidus mineral assemblage at 9 and 13 GPa includes garnet, clinopyroxene, magnesite, aragonite, a high-pressure polymorph of TiO2 (only at 9 GPa) and stishovite (only at 13 GPa). At 17–21 GPa clinopyroxene is no longer stable; the mineral assemblage consists predominantly of garnet with subordinate magnesite (only at 17 GPa), Na-rich aragonite, stishovite, Ca-perovskite (mostly at 21 GPa), and K-hollandite (mostly at 17 GPa). Na-carbonate with an inferred composition (Na,K)2(Ca,Mg,Fe)(CO3)2 was present in Volga-cc at 21 GPa and 1200°C. Diamond (or graphite) crystallized in most runs in the GA1cc composition, but it was absent in experiments with the Volga-cc composition. In Volga-cc, the solidus temperatures are nearly constant between 1200 and 1300°C over the entire pressure range investigated. In GA1cc, the solidus is located at similar temperatures at 9–13 GPa, but at higher temperatures of 1300–1500°C at 17–21 GPa. The difference in solidi between the GA1cc and Volga-cc compositions can be explained by a change in Na compatibility between 13 and 17 GPa as omphacitic clinopyroxene disappears, resulting in the formation of Na-carbonate or Na-rich melt in Volga-cc. The solidus temperature in GA1cc also increases with increasing pressure as a consequence of carbonate reduction and diamond precipitation, possibly brought on either via progressive Fe2+–Fe3+ transition in garnet at higher pressures or by a decrease of the activity of the diopside component in clinopyroxene. The low-degree melts are highly alkalic (K-rich at 9–13 GPa and Na-rich at 17–21 GPa) carbonatites, changing towards SiO2-rich melts with increasing temperature at constant pressure. The solidi of both compositions remain higher than typical subduction pressure–temperature (P–T) profiles at 5–10 GPa; however, at higher pressures the flat solidus curve of carbonated eclogite may intersect the subduction P–T profile in the Transition Zone, where carbonated eclogite can produce alkali- and carbonate-rich melts. Such subduction-related alkali-rich melts can be potential analogues of kimberlite and carbonatite melt compositions and important agents of mantle metasomatism and diamond formation in the Transition Zone and in cratonic roots. Melting of carbonated eclogite produces a garnet-bearing refractory residue, which could be stored in the Transition Zone or lower mantle.
Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.