Miracle-dz/UniFAR-FTUs huggingface dataset:
Winos from natural SUSY at the high luminosity LHC

In natural supersymmetric models defined by no worse than a part in thirty electroweak fine-tuning, winos and binos are generically expected to be much heavier than higgsinos. Moreover, the splitting between the higgsinos is expected to be small, so that the visible decay products of the heavier higgsinos are soft, rendering the higgsinos quasi-invisible at the LHC. Within the natural SUSY framwork, heavy electroweak gauginos decay to W, Z or h bosons plus higgsinos in the ratio ~2:1:1, respectively. This is in sharp contrast to models with a bino-like lightest superpartner and very heavy higgsinos, where the charged (neutral) wino essentially always decays to a W (h) boson and an invisible bino. Wino pair production at the LHC, in natural SUSY, thus leads to VV, Vh and hh+MET final states (V=W, Z) where, for TeV scale winos, the vector bosons and h daughters are considerably boosted. We identify eight different channels arising from the leptonic and hadronic decays of the vector bosons and the decay h->b\bar{b}, each of which offers an avenue for wino discovery at the high luminosity LHC (HL-LHC). By combining the signal in all eight channels we find, assuming \sqrt{s}=14 TeV and an integrated luminosity of 3000 fb^{-1}, that the discovery reach for winos extends to m(wino)~1.1~TeV, while the 95% CL exclusion range extends to a wino mass of almost 1.4~TeV. We also identify ``higgsino specific channels'' which could serve to provide 3\sigma evidence that winos lighter than 1.2~TeV decay to light higgsinos rather than to a bino-like LSP, should a wino signal appear at the HL-LHC.
SERENADE: A Model for Human-in-the-loop Automatic Chord Estimation

Computational harmony analysis is important for MIR tasks such as automatic segmentation, corpus analysis and automatic chord label estimation. However, recent research into the ambiguous nature of musical harmony, causing limited inter-rater agreement, has made apparent that there is a glass ceiling for common metrics such as accuracy. Commonly, these issues are addressed either in the training data itself by creating majority-rule annotations or during the training phase by learning soft targets. We propose a novel alternative approach in which a human and an autoregressive model together co-create a harmonic annotation for an audio track. After automatically generating harmony predictions, a human sparsely annotates parts with low model confidence and the model then adjusts its predictions following human guidance. We evaluate our model on a dataset of popular music and we show that, with this human-in-the-loop approach, harmonic analysis performance improves over a model-only approach. The human contribution is amplified by the second, constrained prediction of the model.
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models

Neural networks with wide layers have attracted significant attention due to their equivalence to Gaussian processes, enabling perfect fitting of training data while maintaining generalization performance, known as benign overfitting. However, existing results mainly focus on shallow or finite-depth networks, necessitating a comprehensive analysis of wide neural networks with infinite-depth layers, such as neural ordinary differential equations (ODEs) and deep equilibrium models (DEQs). In this paper, we specifically investigate the deep equilibrium model (DEQ), an infinite-depth neural network with shared weight matrices across layers. Our analysis reveals that as the width of DEQ layers approaches infinity, it converges to a Gaussian process, establishing what is known as the Neural Network and Gaussian Process (NNGP) correspondence. Remarkably, this convergence holds even when the limits of depth and width are interchanged, which is not observed in typical infinite-depth Multilayer Perceptron (MLP) networks. Furthermore, we demonstrate that the associated Gaussian vector remains non-degenerate for any pairwise distinct input data, ensuring a strictly positive smallest eigenvalue of the corresponding kernel matrix using the NNGP kernel. These findings serve as fundamental elements for studying the training and generalization of DEQs, laying the groundwork for future research in this area.
Heterogenous Memory Augmented Neural Networks

It has been shown that semi-parametric methods, which combine standard neural networks with non-parametric components such as external memory modules and data retrieval, are particularly helpful in data scarcity and out-of-distribution (OOD) scenarios. However, existing semi-parametric methods mostly depend on independent raw data points - this strategy is difficult to scale up due to both high computational costs and the incapacity of current attention mechanisms with a large number of tokens. In this paper, we introduce a novel heterogeneous memory augmentation approach for neural networks which, by introducing learnable memory tokens with attention mechanism, can effectively boost performance without huge computational overhead. Our general-purpose method can be seamlessly combined with various backbones (MLP, CNN, GNN, and Transformer) in a plug-and-play manner. We extensively evaluate our approach on various image and graph-based tasks under both in-distribution (ID) and OOD conditions and show its competitive performance against task-specific state-of-the-art methods. Code is available at \url{https://github.com/qiuzh20/HMA}.
Distribution of large‐scale contractional tectonic landforms on Mercury: Implications for the origin of global stresses

The surface of Mercury is dominated by contractional tectonic landforms that are evidence of global‐scale crustal deformation. Using MESSENGER orbital high‐incidence angle imaging and topographic data, large‐scale lobate thrust fault scarps have been mapped globally. The spatial distribution and areal density of the contractional landforms are not uniform; concentrations occur in longitudinal bands and between the north and south hemispheres. Their orientations are generally north‐south at low latitude to midlatitude and east‐west at high latitudes. The spatial distribution and distribution of orientations of these large‐scale contractional features suggest that planet‐wide contraction due to interior cooling cannot be the sole source of global stresses. The nonrandom orientations are best explained by a combination of stresses from global contraction and tidal despinning combined with an equator‐to‐pole variation in lithospheric thickness, while the nonuniform areal density of the contractional features may indicate the influence of mantle downwelling or heterogeneities in lithospheric strength.
A Two-Layer Blockchain Sharding Protocol Leveraging Safety and Liveness for Enhanced Performance

Sharding is essential for improving blockchain scalability. Existing protocols overlook diverse adversarial attacks, limiting transaction throughput. This paper presents Reticulum, a groundbreaking sharding protocol addressing this issue, boosting blockchain scalability. Reticulum employs a two-phase approach, adapting transaction throughput based on runtime adversarial attacks. It comprises"control"and"process"shards in two layers. Process shards contain at least one trustworthy node, while control shards have a majority of trusted nodes. In the first phase, transactions are written to blocks and voted on by nodes in process shards. Unanimously accepted blocks are confirmed. In the second phase, blocks without unanimous acceptance are voted on by control shards. Blocks are accepted if the majority votes in favor, eliminating first-phase opponents and silent voters. Reticulum uses unanimous voting in the first phase, involving fewer nodes, enabling more parallel process shards. Control shards finalize decisions and resolve disputes. Experiments confirm Reticulum's innovative design, providing high transaction throughput and robustness against various network attacks, outperforming existing sharding protocols for blockchain networks.
An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent

The delta-bar-delta algorithm is recognized as a learning rate adaptation technique that enhances the convergence speed of the training process in optimization by dynamically scheduling the learning rate based on the difference between the current and previous weight updates. While this algorithm has demonstrated strong competitiveness in full data optimization when compared to other state-of-the-art algorithms like Adam and SGD, it may encounter convergence issues in mini-batch optimization scenarios due to the presence of noisy gradients. In this study, we thoroughly investigate the convergence behavior of the delta-bar-delta algorithm in real-world neural network optimization. To address any potential convergence challenges, we propose a novel approach called RDBD (Regrettable Delta-Bar-Delta). Our approach allows for prompt correction of biased learning rate adjustments and ensures the convergence of the optimization process. Furthermore, we demonstrate that RDBD can be seamlessly integrated with any optimization algorithm and significantly improve the convergence speed. By conducting extensive experiments and evaluations, we validate the effectiveness and efficiency of our proposed RDBD approach. The results showcase its capability to overcome convergence issues in mini-batch optimization and its potential to enhance the convergence speed of various optimization algorithms. This research contributes to the advancement of optimization techniques in neural network training, providing practitioners with a reliable automatic learning rate scheduler for achieving faster convergence and improved optimization outcomes.
MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games

This paper presents MiniZero, a zero-knowledge learning framework that supports four state-of-the-art algorithms, including AlphaZero, MuZero, Gumbel AlphaZero, and Gumbel MuZero. While these algorithms have demonstrated super-human performance in many games, it remains unclear which among them is most suitable or efficient for specific tasks. Through MiniZero, we systematically evaluate the performance of each algorithm in two board games, 9x9 Go and 8x8 Othello, as well as 57 Atari games. For two board games, using more simulations generally results in higher performance. However, the choice of AlphaZero and MuZero may differ based on game properties. For Atari games, both MuZero and Gumbel MuZero are worth considering. Since each game has unique characteristics, different algorithms and simulations yield varying results. In addition, we introduce an approach, called progressive simulation, which progressively increases the simulation budget during training to allocate computation more efficiently. Our empirical results demonstrate that progressive simulation achieves significantly superior performance in two board games. By making our framework and trained models publicly available, this paper contributes a benchmark for future research on zero-knowledge learning algorithms, assisting researchers in algorithm selection and comparison against these zero-knowledge learning baselines. Our code and data are available at https://rlg.iis. sinica.edu. tw/papers/minizero.
Western Hemlock Ecosystem

We report measurements of rates of sap flow in dominant trees, changes in soil moisture, and evaporation from coarse woody debris in an oldgrowth Douglas-fir‐western hemlock ecosystem at Wind River, Washington, USA, during dry periods in summer. The measurements are compared with eddy-covariance measurements of water-vapor fluxes above the forest (Ee) and at the forest floor (Eu) to examine the components of ecosystem water loss and the factors controlling them. Daily values of Eu were about 10% of Ee. Evaporation from coarse woody debris was only about 2% of Ee. Transpiration (Et), estimated by scaling sap-flow measurements accounted for about 70% of (Ee) 12 Eu); transpiration from subdominant trees may account for the remainder. The daily total change in soil moisture (Es) in the top 30 cm was larger than the net change, probably because of hydraulic redistribution of soil water by roots. Observed differences between Es and Ee were probably because roots also extract water from greater depth, and/or because the measuring systems sample at different spatial scales. The ratio of Et to Es decreased with decreasing soil water content, suggesting that partitioning in water use between understory and overstory changed during the season. The rate of soil drying exceeded Ee early in the day, probably because water vapor was being stored in canopy air space and condensed or adsorbed on tree stems, lichens, and mosses. The daily variation of Ee with vapor-pressure deficit showed strong hysteresis, most likely associated with transpiration of water stored in tree stems and branches.
Rb-Sr and K-Ar geochronometry of Mesozoic granitic rocks and their Sr isotopic composition, Oregon, Washington, and Idaho

Mesozoic orogeny and magmatism began in the northwestern United States soon after deposition of Permian strata, but no rocks have yet been dated from the Permian-Triassic orogenic period. Middle Triassic to Late Jurassic sediment sequences include major unconformities and evidence of several episodes of igneous activity. An early culmination of magmatism occurred in Late Triassic and Early Jurassic time (200–217 m.y. ago) in eugeosynclinal parts of far western Idaho. A widespread and intense culmination in Late Jurassic time was the final major orogenic event in the Oregon eugeosyncline. The Bald Mountain (147 to 158 m.y. old), Wallowa (probably 143 to 160 m.y. old but affected by Cretaceous metamorphism), Deep Creek (at least 137 m.y. old), and many other plutons in the Blue and Klamath Mountain regions in Oregon and in western Idaho were emplaced shortly before the end of Jurassic. The bulk of the Idaho batholith was emplaced during a Cretaceous culmination of igneous activity — the southern (Atlanta) lobe about 75 to 100 m.y. ago and the northern (Bitterroot) lobe about 70 to 80 m.y. ago. Much of the batholith was affected by Eocene magmatism which resulted in widespread resetting of isotopic dates for older rocks to values of 50 m.y. or less. Between 55 and 70 m.y. ago, there was a lull in igneous activity in the northwestern United States. Sr isotope initial ratios change abruptly across a boundary in western Idaho from ∼0.7040 or less, to the west, to ∼0.7060 or greater, to the east. This change marks the boundary between Precambrian crust and Phanerozoic eugeosyncline. The geologic setting of the observed transition and its time independence suggest that it is due to contamination and assimilation processes involving magmas from the mantle and enclosing crustal rocks. Contamination of magmas with radiogenic Sr renders the Sr whole-rock isochron technique useless in dating the Idaho batholith and other intrusive rocks in central and eastern Idaho, areas underlain by Precambrian basement.
Global solutions of 2D non-resistive MHD system under a magnetic vortex background

We investigate the global well-posedness of two-dimensional incompressible viscous and non-resistive MHD system near a non-constant equilibrium. Assuming that the initial data has certain symmetries, we can establish time-weighted lower-order energy estimates and uniform higher-order energy estimates by exploring the dissipation nature of the linearized equations. With these estimates, we can get the global well-posedness result. MSC(2020) 35Q35, 35B65, 76E25.
Tur\'{a}n problem for $C_{2k+1}^{-}$-free signed graph

In this paper, we study the Tur\'{a}n problem for $C_{2k+1}^{-}$. Suppose that $\dot{G}$ is an unbalanced signed graph of order $n$ with $e(\dot{G})$ edges. Let $\lambda_{1} (\dot{G})$ be the largest eigenvalue of $\dot{G}$, and $C_{2k+1}^{-}$ be the set of the negative cycle with length $2k+1$($3 \le k \le \frac{n}{15}$). We prove that if $\dot{G}$ is a $C_{2k+1}^{-}$-free unbalanced signed graph, then $e(\dot{G}) \le e(C_{3}^{-} \cdot K_{n-2})$ and $\lambda_{1}(\dot{G}) \le \lambda_{1}(C_{3}^{-} \cdot K_{n-2})$, with equality holding if and only if $\dot{G}$ is switching equivalent to $C_{3}^{-} \cdot K_{n-2}$.
Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines

This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational on quantum hardware over the Iris dataset. The methodology embraced encapsulates an extensive array of experiments orchestrated through the Qiskit library, alongside hyperparameter optimization. The findings unveil that in particular scenarios, QSVMs extend a level of accuracy that can vie with classical SVMs, albeit the execution times are presently protracted. Moreover, we underscore that augmenting quantum computational capacity and the magnitude of parallelism can markedly ameliorate the performance of quantum machine learning algorithms. This inquiry furnishes invaluable insights regarding the extant scenario and future potentiality of machine learning applications in the quantum epoch. Colab: https://t.ly/QKuz0
Three-sublattice antiferro-type and ferri-type skyrmion crystals in centrosymmetric magnets

We numerically investigate the stability of the skyrmion crystals in a centrosymmetric lattice structure by focusing on the role of magnetic frustration arising from the multi-sublattice degree of freedom. By analyzing an effective three-sublattice spin model with the antiferromagnetic exchange interaction between different sublattices and the momentum-resolved exchange interaction between the same sublattice based on the simulated annealing, we find that various skyrmion crystal phases with sublattice-dependent skyrmion numbers are stabilized with and without an external magnetic field depending on the model parameters: two ferro-type skyrmion crystal phases with the skyrmion numbers of one and two, two ferri-type skyrmion crystal phases, and one antiferro-type skyrmion crystal phase. Our results indicate that the multi-sublattice degree of freedom brings a further intriguing possibility of inducing exotic skyrmion crystal phases.
NICE: Improving Panoptic Narrative Detection and Segmentation with Cascading Collaborative Learning

Panoptic Narrative Detection (PND) and Segmentation (PNS) are two challenging tasks that involve identifying and locating multiple targets in an image according to a long narrative description. In this paper, we propose a unified and effective framework called NICE that can jointly learn these two panoptic narrative recognition tasks. Existing visual grounding tasks use a two-branch paradigm, but applying this directly to PND and PNS can result in prediction conflict due to their intrinsic many-to-many alignment property. To address this, we introduce two cascading modules based on the barycenter of the mask, which are Coordinate Guided Aggregation (CGA) and Barycenter Driven Localization (BDL), responsible for segmentation and detection, respectively. By linking PNS and PND in series with the barycenter of segmentation as the anchor, our approach naturally aligns the two tasks and allows them to complement each other for improved performance. Specifically, CGA provides the barycenter as a reference for detection, reducing BDL's reliance on a large number of candidate boxes. BDL leverages its excellent properties to distinguish different instances, which improves the performance of CGA for segmentation. Extensive experiments demonstrate that NICE surpasses all existing methods by a large margin, achieving 4.1% for PND and 2.9% for PNS over the state-of-the-art. These results validate the effectiveness of our proposed collaborative learning strategy. The project of this work is made publicly available at https://github.com/Mr-Neko/NICE.
Sadness, Anger, or Anxiety: Twitter Users' Emotional Responses to Toxicity in Public Conversations

Cyberbullying and online harassment have serious negative psychological and emotional consequences for the victims, such as decreased life satisfaction, suicidal ideation, self-harming behaviors, depression, anxiety, and others. Most of the prior works assessed people's emotional responses via questionnaires, while social media platforms contain data that could provide valuable insights into users' emotions in real online discussions. Therefore, this data-driven study investigates the effect of toxicity on Twitter users' emotions and other factors associated with expressing anger, anxiety, and sadness in terms of account identifiability, activity, conversation structure, and conversation topic. To achieve this goal, we identified toxic replies in the large dataset consisting of 79,799 random Twitter conversations and obtained the emotions expressed in these conversations. Then, we performed propensity score matching and analyzed causal associations between toxicity and users' emotions. In general, we found that users receiving toxic replies are more likely to express emotions of anger, sadness, and anxiety compared to users who did not receive toxic replies. Finally, analysis results indicate that the conversation topic and users' account characteristics are likely to affect their emotional responses to toxicity. Our findings provide a better understanding of toxic replies' consequences on users' emotional states, which can potentially lead to developing personalized moderation methods that will help users emotionally cope with toxicity on social media.
Single-Shot, Spatio-Temporal Metrology of Relativistic Plasma Optics

Ultrahigh peak power femtosecond laser pulses create extreme states of matter that are currently being probed with great interest. Plasma optics have been proposed for shaping and amplifying high-power pulses, but they are subject to huge modulations and fluctuations due to the very nature of excitation at high intensities. Multidimensional characterization (spatial and temporal) of relativistic plasma dynamics is therefore crucial to understand the spatio-temporal structure of intense femtosecond pulses shaped by plasma optics. This is, however, extremely difficult to achieve, particularly at the low repetition rates typical at 100s terawatt to petawatt powers. Here, we present a single-shot, three-dimensional (3D) spatio-temporal and spatio-spectral measurement of such pulses based on spectral interferometry. We reconstruct the 3D temporal structure of the laser pulse simultaneously resolving the complex plasma dynamics. We demonstrate our method by measuring the sub-picosecond evolution of relativistic solid-density plasmas. Our measurements reveal that different spatial regions of the plasma surface move differently yet exhibit a collective behavior globally. This all-optical measurement technique captures 3D spatio-temporal effects within pulses with ultrahigh peak powers, all in a single shot, enabling further progress in ultrahigh-intensity laser and plasma technologies.
HCI in e-Government and e-Democracy

This chapter introduces the application of HCI design processes and design principles in e-government and e-democracy. We elaborate on HCI design processes and six HCI design principles in the context of e-government and e-democracy, including citizen-centered design, usability, accessibility, access to information, transaction efficiency, and security and privacy. Then, we present two cases to demonstrate the value of applying the HCI processes and design principles in developing and deploying e-government and e-democracy. Finally, we highlight the challenges faced by e-government and e-democracy as well as the future trends. In conclusion, HCI can help the success of e-government and e-democracy and their future growth.