Miracle-dz/UniFAR-FTUs huggingface dataset:
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition

Deep neural networks have empowered accurate device-free human activity recognition, which has wide applications. Deep models can extract robust features from various sensors and generalize well even in challenging situations such as data-insufficient cases. However, these systems could be vulnerable to input perturbations, i.e., adversarial attacks. We empirically demonstrate that both black-box Gaussian attacks and modern adversarial white-box attacks can render their accuracies to plummet. In this paper, we first point out that such phenomenon can bring severe safety hazards to device-free sensing systems, and then propose a novel learning framework, SecureSense, to defend common attacks. SecureSense aims to achieve consistent predictions regardless of whether there exists an attack on its input or not, alleviating the negative effect of distribution perturbation caused by adversarial attacks. Extensive experiments demonstrate that our proposed method can significantly enhance the model robustness of existing deep models, overcoming possible attacks. The results validate that our method works well on wireless human activity recognition and person identification systems. To the best of our knowledge, this is the first work to investigate adversarial attacks and further develop a novel defense framework for wireless human activity recognition in mobile computing research.
UIU-Net: U-Net in U-Net for Infrared Small Object Detection

Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distinguishability limitations as the network depth increases. Furthermore, small objects in infrared images are frequently emerged bright and dark, posing severe demands for obtaining precise object contrast information. For this reason, we in this paper propose a simple and effective “U-Net in U-Net” framework, UIU-Net for short, and detect small objects in infrared images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net backbone, enabling the multi-level and multi-scale representation learning of objects. Moreover, UIU-Net can be trained from scratch, and the learned features can enhance global and local contrast information effectively. More specifically, the UIU-Net model is divided into two modules: the resolution-maintenance deep supervision (RM-DS) module and the interactive-cross attention (IC-A) module. RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information. Further, IC-A encodes the local context information between the low-level details and high-level semantic features. Extensive experiments conducted on two infrared single-frame image datasets, i.e., SIRST and Synthetic datasets, show the effectiveness and superiority of the proposed UIU-Net in comparison with several state-of-the-art infrared small object detection methods. The proposed UIU-Net also produces powerful generalization performance for video sequence infrared small object datasets, e.g., ATR ground/air video sequence dataset. The codes of this work are available openly at https://github.com/danfenghong/IEEE
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks

Many dynamical systems -- from robots interacting with their surroundings to large-scale multiphysics systems -- involve a number of interacting subsystems. Toward the objective of learning composite models of such systems from data, we present i) a framework for compositional neural networks, ii) algorithms to train these models, iii) a method to compose the learned models, iv) theoretical results that bound the error of the resulting composite models, and v) a method to learn the composition itself, when it is not known a priori. The end result is a modular approach to learning: neural network submodels are trained on trajectory data generated by relatively simple subsystems, and the dynamics of more complex composite systems are then predicted without requiring additional data generated by the composite systems themselves. We achieve this compositionality by representing the system of interest, as well as each of its subsystems, as a port-Hamiltonian neural network (PHNN) -- a class of neural ordinary differential equations that uses the port-Hamiltonian systems formulation as inductive bias. We compose collections of PHNNs by using the system's physics-informed interconnection structure, which may be known a priori, or may itself be learned from data. We demonstrate the novel capabilities of the proposed framework through numerical examples involving interacting spring-mass-damper systems. Models of these systems, which include nonlinear energy dissipation and control inputs, are learned independently. Accurate compositions are learned using an amount of training data that is negligible in comparison with that required to train a new model from scratch. Finally, we observe that the composite PHNNs enjoy properties of port-Hamiltonian systems, such as cyclo-passivity -- a property that is useful for control purposes.
A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention Mechanism for Symbolic Music Modeling

Following the success of the transformer architecture in the natural language domain, transformer-like architectures have been widely applied to the domain of symbolic music recently. Symbolic music and text, however, are two different modalities. Symbolic music contains multiple attributes, both absolute attributes (e.g., pitch) and relative attributes (e.g., pitch interval). These relative attributes shape human perception of musical motifs. These important relative attributes, however, are mostly ignored in existing symbolic music modelling methods with the main reason being the lack of a musically-meaningful embedding space where both the absolute and relative embeddings of the symbolic music tokens can be efficiently represented. In this paper, we propose the Fundamental Music Embedding (FME) for symbolic music based on a bias-adjusted sinusoidal encoding within which both the absolute and the relative attributes can be embedded and the fundamental musical properties (e.g., translational invariance) are explicitly preserved. Taking advantage of the proposed FME, we further propose a novel attention mechanism based on the relative index, pitch and onset embeddings (RIPO attention) such that the musical domain knowledge can be fully utilized for symbolic music modelling. Experiment results show that our proposed model: RIPO transformer which utilizes FME and RIPO attention outperforms the state-of-the-art transformers (i.e., music transformer, linear transformer) in a melody completion task. Moreover, using the RIPO transformer in a downstream music generation task, we notice that the notorious degeneration phenomenon no longer exists and the music generated by the RIPO transformer outperforms the music generated by state-of-the-art transformer models in both subjective and objective evaluations. The code of the proposed method is available online: github.com/guozixunnicolas/FundamentalMusicEmbedding.
An autoencoder for heterotic orbifolds with arbitrary geometry

Artificial neural networks can be an important tool to improve the search for admissible string compactifications and characterize them. In this paper we construct the heterotic orbiencoder, a general deep autoencoder to study heterotic orbifold models arising from various Abelian orbifold geometries. Our neural network can be easily trained to successfully encode the large parameter space of many orbifold geometries simultaneously, independently of the statistical dissimilarities of their training features. In particular, we show that our autoencoder is capable of compressing with good accuracy the large parameter space of two promising orbifold geometries in just three parameters. Further, most orbifold models with phenomenologically appealing features appear in bounded regions of this small space. Our results hint towards a possible simplification of the classification of (promising) heterotic orbifold models.
QFF: Quantized Fourier Features for Neural Field Representations

Multilayer perceptrons (MLPs) learn high frequencies slowly. Recent approaches encode features in spatial bins to improve speed of learning details, but at the cost of larger model size and loss of continuity. Instead, we propose to encode features in bins of Fourier features that are commonly used for positional encoding. We call these Quantized Fourier Features (QFF). As a naturally multiresolution and periodic representation, our experiments show that using QFF can result in smaller model size, faster training, and better quality outputs for several applications, including Neural Image Representations (NIR), Neural Radiance Field (NeRF) and Signed Distance Function (SDF) modeling. QFF are easy to code, fast to compute, and serve as a simple drop-in addition to many neural field representations.
Frank-Read Mechanism in Nematic Liquid Crystals

In a crystalline solid under mechanical stress, a Frank-Read source is a pinned dislocation segment that repeatedly bows and detaches, generating concentric dislocation loops. We demonstrate that in nematic liquid crystals, an analogous Frank-Read mechanism can generate concentric disclination loops. Using experiment, simulation, and theory, we study a disclination segment pinned between surface defects on one substrate in a nematic cell. Under applied twist of the nematic director, the pinned segment bows and emits a new disclination loop which expands, leaving the original segment intact; loop emission repeats for each additional 180$^\circ$ of applied twist. We present experimental micrographs showing loop expansion and snap-off, numerical simulations of loop emission under both quasistatic and dynamic loading, and theoretical analysis considering both free energy minimization and the balance of competing forces. We find that the critical stress for disclination loop emission scales as the inverse of segment length, and changes as a function of strain rate and temperature, in close analogy to the Frank-Read source mechanism in crystals. Lastly, we discuss how Frank-Read sources could be used to modify microstructural evolution in both passive and active nematics.
FedCoCo: A Memory Efficient Federated Self-supervised Framework for On-Device Visual Representation Learning

—The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efﬁciently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a Federated on-device Contrastive learning framework with Coreset selection, which we call FedCoCo, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and signiﬁcance of the proposed method in visual representation learning.
Who Controls the Agenda Controls the Polity

This paper models legislative decision-making with an agenda setter who can propose policies sequentially, tailoring each proposal to the status quo that prevails after prior votes. Voters are sophisticated and the agenda setter cannot commit to her future proposals. Nevertheless, the agenda setter obtains her favorite outcome in every equilibrium regardless of the initial default policy. Central to our results is a new condition on preferences, manipulability, that holds in rich policy spaces, including spatial settings and distribution problems. Our results overturn the conventional wisdom that voter sophistication alone constrains an agenda setter’s power. JEL Codes: D72, C78
QC-StyleGAN - Quality Controllable Image Generation and Manipulation

The introduction of high-quality image generation models, particularly the StyleGAN family, provides a powerful tool to synthesize and manipulate images. However, existing models are built upon high-quality (HQ) data as desired outputs, making them unfit for in-the-wild low-quality (LQ) images, which are common inputs for manipulation. In this work, we bridge this gap by proposing a novel GAN structure that allows for generating images with controllable quality. The network can synthesize various image degradation and restore the sharp image via a quality control code. Our proposed QC-StyleGAN can directly edit LQ images without altering their quality by applying GAN inversion and manipulation techniques. It also provides for free an image restoration solution that can handle various degradations, including noise, blur, compression artifacts, and their mixtures. Finally, we demonstrate numerous other applications such as image degradation synthesis, transfer, and interpolation. The code is available at https://github.com/VinAIResearch/QC-StyleGAN.
Electric micro fields in simulated two component plasmas.

The statistical properties of local electric fields in an classical plasma are investigated by molecular dynamics (MD) simulation. Two‐component plasma simulations of neutral hydrogen, protons and electrons for intermediate plasma coupling conditions, typically Ne≈1018cm−3, Te≈1eV, have been carried out. These simulations appear as a possible and very useful way to generate relevant microfield sample‐sets appropriate for ion emitter lineshape simulations for plasma spectroscopy and to provide guidance for line shape modeling.
Spectroscopy of Giants in LMC Clusters. III. Velocities and Abundances for NGC 1841 and Reticulum and the Properties of the Metal-Poor Clusters

Velocities and metallicities for giant star members of the distant globular clusters Reticulum (C0435 -590) and NGC 1841 (C0444-840) based on spectroscopy with the CTIO Argus multi-fiber system show that these two clusters are likely members of the Large Magellanic Cloud (LMC). The velocity, [Fe/H] metallicity, and absolute visual magnitude for Reticulum are 241.5±1.5 km s −1 , −1.71, and −6.0±0,2, and for NGC 1841, 212.5±1.8 km s −1 , −2.11, and −7.9±0.2
Influence of pre-bottleneck diversion devices on pedestrian flow

The existence of bottlenecks often leads to the stagnation of pedestrian gatherings, which seriously affects the efficiency of traffic and reduces the flow of pedestrians. Some studies have shown that setting devices in front of bottlenecks can promote pedestrian evacuation under certain conditions. In this paper, the effect of setting diversion devices in front of the exit on pedestrian flow is studied. From our observation, these diversion devices can form a buffer zone before the exit and affect pedestrian behaviors. The evacuation times are found to decrease as the devices become farther away from the exit. In our experiments, it is found that the effect of shunt piles on evacuation is better than in the case of safety barriers and without device conditions. Under the condition of setting up safety barriers approximately 1 m and 3 m in front of the exit, the evacuation times are extended by 0.88% and 2.67%. For shunt piles, the evacuation times are 11.53% and 14.96% shorter than that of those without a device regarding the different distances to exit (1 m and 3 m, respectively). In addition, setting up shunt piles reduces the time interval between two consecutive pedestrians. To sum up, in our experimental settings, the diversion devices can effectively improve the average speed ahead of the exit and promote evacuation to become more orderly, which reduces the congestion in the later period of evacuation. In other words, this study demonstrates that a reasonable layout of facilities can not only meet the daily functional requirements but also improve the efficient use of space in emergencies, reducing the probability of crowd conventions and jams.
Nearly Toric Schubert Varieties and Dyck Paths

In this article, we consider Ding's partition Schubert varieties whose members are very close to being a toric variety. More precisely, we characterize the spherical partition Schubert varieties in terms of Dyck paths. We introduce a notion of a nearly toric variety. We identify the nearly toric partition Schubert varieties as well as all singular nearly toric Schubert varieties. We find the cardinalities of the sets of these Schubert varieties.
On the Limit of Explaining Black-box Temporal Graph Neural Networks

Temporal Graph Neural Network (TGNN) has been receiving a lot of attention recently due to its capability in modeling time-evolving graph-related tasks. Similar to Graph Neural Networks, it is also non-trivial to interpret predictions made by a TGNN due to its black-box nature. A major approach tackling this problems in GNNs is by analyzing the model' responses on some perturbations of the model's inputs, called perturbation-based explanation methods. While these methods are convenient and flexible since they do not need internal access to the model, does this lack of internal access prevent them from revealing some important information of the predictions? Motivated by that question, this work studies the limit of some classes of perturbation-based explanation methods. Particularly, by constructing some specific instances of TGNNs, we show (i) node-perturbation cannot reliably identify the paths carrying out the prediction, (ii) edge-perturbation is not reliable in determining all nodes contributing to the prediction and (iii) perturbing both nodes and edges does not reliably help us identify the graph's components carrying out the temporal aggregation in TGNNs.
Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?

Turning the weights to zero when training a neural network helps in reducing the computational complexity at inference. To progressively increase the sparsity ratio in the network without causing sharp weight discontinuities during training, our work combines soft-thresholding and straight-through gradient estimation to update the raw, i.e. non-thresholded, version of zeroed weights. Our method, named ST-3 for straight-through/soft-thresholding/sparse-training2, obtains SoA results, both in terms of accuracy/sparsity and accuracy/FLOPS trade-offs, when progressively increasing the sparsity ratio in a single training cycle. In particular, despite its simplicity, ST-3 favorably compares to the most recent methods, adopting differentiable formulations [42] or bio-inspired neuroregeneration principles [25]. This suggests that the key ingredients for effective sparsification primarily lie in the ability to give the weights the freedom to evolve smoothly across the zero state while progressively increasing the sparsity ratio.
The Intrinsic Profile of the 4428 Å Diffuse Interstellar Band

This paper presents high-quality CCD spectra (obtained at spectral resolving power λ/Δλ ≈ 2000) of the region surrounding the diffuse interstellar band (DIB) centered near 4428 Å, in some 35 O and early B stars located in the Cyg OB2 association. The reddenings of the stars range from E(B-V) ≈ 1.0 to 2.5 mag, allowing us to study the properties of the feature at high extinction, as compared with previous studies of DIBs in diffuse clouds. To our knowledge this is the first significant survey of the 4428 Å DIB profile to be based on CCD-quality spectra, allowing us to probe the properties of this feature with high accuracy. Among our conclusions are (1) the profile of λ4428 is symmetric and invariant within the uncertainties; (2) there is no clear-cut dependence of band properties on stellar spectral type; (3) the equivalent width of the 4428 Å DIB as a function of increasing extinction appears to level off at around E(B-V) ≈ 1.0; (4) there is no evidence for emission wings associated with λ4428; and (5) within the uncertainties the profile of the feature is fitted perfectly by a Lorentzian function, strongly suggesting that the band is broadened by natural damping. We conclude that the feature is created by rapid internal conversion in a molecular carrier.
On the detection of Reynolds stress as a driving and damping mechanism of geodesic acoustic modes and zonal flows

Bispectral analysis has been proposed as a diagnostic for Reynolds stress (RS) as a driving mechanism of zonal flows (ZF) in toroidally confined plasmas. A turbulence simulation code was used to test this technique on a well-defined system. It turned out that the geodesic acoustic mode dominates the poloidal flow spectrum and that it reduces radial transport in the same way as does a low-frequency ZF. Using the total cross-bicoherence, a correlation between RS and large-scale poloidal flows could be detected. The experimentally more accessible auto-bicoherences did not prove to be a useful quantity for studying this interaction. RS was not observed as a precursor of the flow; rather it appears simultaneously in the region of radial flow shear.
Interannual Climate Simulation and Predictability in a Coupled TOGA GCM

Abstract A Pacific Ocean–global atmosphere general circulation model is used to simulate the climatic mean state and variability in the Tropics, up to interannual timescales. For this model no long-term trend in climate occurs, but there are systematic differences between the model mean state and observations: in particular, the east equatorial Pacific sea surface temperature is too high by several degrees. Along the equator the seasonal variability in sea surface temperature is good although some features of the seasonal cycle are unrealistic: for example, the east Pacific convergence zone crosses the equator twice a year, residing in the summer hemisphere. Despite some deficiencies in the simulation of the mean state, there is substantial interannual variability, with irregular oscillations dominated by a 2-yr cycle. A principal oscillation pattern analysis shows that the interannual anomalies are typically generated in the west Pacific and move eastward along the equator, with closely connected oceanic.
The Preferential Formation of High-Mass Stars in Shocked Interstellar Gas Layers

Gravitationally unstable, shocked layers of interstellar gas are produced by cloud-cloud collisions and by expanding nebulae around massive stars. We show that the resulting fragments are likely to be of high mass, and initially well separated (i.e. weakly bound to one another, if at all). This result may explain why dynamically active regions tend to have a high efficiency of massive star formation, and why they tend to relax quickly into a self-propagating mode which generates sequences of OB subgroups. These tendencies are manifested on many scales, from local star-forming regions like Orion, through regions like 30 Doradus in the LMC, to the most IR-luminous starburst galaxies. We also show that, for a wide range of input parameters, gravitational fragmentation of a shocked layer occurs when the column density of hydrogen nuclei through the accumulating layer reaches a value. This may be one reason for the mass-radius relation for molecular cloud clumps first noted by Larson.