Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Sensing Signal in ISAC: A NOMA Inspired Scheme

A non-orthogonal multiple access (NOMA)-inspired integrated sensing and communication (ISAC) framework is proposed, where a dual-functional base station (BS) transmits the composite communication and sensing signals. In contrast to treating the sensing signal as a harmful interference to communication, in this work, multiple beams of the sensing signal are exploited to convey extra information streams based on the concept of NOMA. Then, each communication user detects the extra information streams and the existing legacy information streams with the aid of successive interference cancellation (SIC). Based on the proposed framework, a multiple-objective optimization problem (MOOP) is formulated to characterize the trade-off between the communication throughput and sensing beampattern accuracy. For the general multiple-user scenario, the formulated MOOP is firstly converted to a single-objective optimization problem via the e-constraint method. Then, a double-layer block coordinate descent (BCD) algorithm is proposed by employing fractional programming and successive convex approximation to find a high-quality sub-optimal solution. For the special single-user scenario, the globally optimal solution can be obtained by transforming the MOOP into a convex quadratic semidefinite program. Moreover, it is rigorously proved that 1) in the multiple-user scenario, the proposed NOMA-inspired ISAC framework always outperforms the state-of-the-art sensing-interference-cancellation (SenIC) ISAC frameworks by further exploiting sensing signals for delivering extra information streams; 2) in the special single-user scenario, the proposed NOMA-inspired ISAC framework achieves the same performance as the existing SenIC ISAC frameworks, which reveals that the coordination of sensing interference is not necessarily required in this case. Numerical results verify the theoretical results.
Consequences of the compatibility of skein algebra and cluster algebra on surfaces

We investigate two algebra of curves on a topological surface with punctures - the cluster algebra of surfaces defined by Fomin, Shapiro, and Thurston, and the generalized skein algebra constructed by Roger and Yang. By establishing their compatibility, we resolve Roger-Yang's conjecture on the deformation quantization of the decorated Teichmuller space. We also obtain several structural results on the cluster algebra of surfaces. The cluster algebra of a positive genus surface is not finitely generated, and it differs from its upper cluster algebra.
Muons in showers with energy $E_{0} \geq$ 5 EeV and QGSjetII-04 and EPOS LHC models of hadronic interactions. Is there a muon deficit in the models?

The paper presents data on the muon component with a threshold \(\varepsilon_{thr} \geq\) 1 GeV. Air showers were registered at the Yakutsk array during almost 50 years of continuous air shower observations. The characteristics of muons are compared with calculations of QGSjetII-04 and EPOS LHC models for a proton and an iron nucleus. There is a muon deficit in the models, at energies greater than 5 EeV. To make an agreement between experimental data and simulations on muons, further tuning of the models is required.
giMLPs: Gate with Inhibition Mechanism in MLPs

This paper presents a new model architecture, gate with inhibition MLP (giMLP). The gate with inhibition on CycleMLP (gi-CycleMLP) can produce equal performance on the ImageNet classification task, and it also improves the BERT, Roberta, and DeBERTaV3 models depending on two novel techniques. The first is the gating MLP, where matrix multiplications between the MLP and the trunk Attention input in further adjust models' adaptation. The second is inhibition which inhibits or enhances the branch adjustment, and with the inhibition levels increasing, it offers models more muscular features restriction. We show that the giCycleMLP with a lower inhibition level can be competitive with the original CycleMLP in terms of ImageNet classification accuracy. In addition, we also show through a comprehensive empirical study that these techniques significantly improve the performance of fine-tuning NLU downstream tasks. As for the gate with inhibition MLPs on DeBERTa (giDeBERTa) fine-tuning, we find it can achieve appealing results on most parts of NLU tasks without any extra pretraining again. We also find that with the use of Gate With Inhibition, the activation function should have a short and smooth negative tail, with which the unimportant features or the features that hurt models can be moderately inhibited. The experiments on ImageNet and twelve language downstream tasks demonstrate the effectiveness of Gate With Inhibition, both for image classification and for enhancing the capacity of nature language fine-tuning without any extra pretraining.
Modeling the Role that Habitat Overlap Shape has on the Spread of Brucellosis in the Greater Yellowstone Ecosystem

Brucellosis is a zoonotic bacterial infectious disease that affects livestock and wildlife. The Greater Yellowstone Ecosystem is the last area in the United States where cattle are regularly infected with brucellosis. Even though livestock are vaccinated, interactions with reservoir species still result in spill-over cases to cattle. The National Academy of Sciences, Engineering, and Medicine has indicated that modeling efforts should focus on the transmission between elk and cattle and should be conducted to better understand the effects of land-use change and landscape configuration on disease risk. This chapter determines how the landscape's configuration, using the shape and amount of habitat overlap between elk and cattle, contributes to cross-species brucellosis transmission, and how land-use change translates to disease prevalence. A mathematical-epidemiological model is combined with landscape ecology metrics to estimate transmission rates between the species and model disease spread. The results of this study can ultimately help stakeholders develop policy for controlling brucellosis transmission between livestock and elk in the Greater Yellowstone Area. In turn, this could lead to less disease prevalence, reduce associated costs, and assist in population management.
Robust Trajectory Prediction against Adversarial Attacks

Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving (AD) systems. However, these methods are vulnerable to adversarial attacks, leading to serious consequences such as collisions. In this work, we identify two key ingredients to defend trajectory prediction models against adversarial attacks including (1) designing effective adversarial training methods and (2) adding domain-specific data augmentation to mitigate the performance degradation on clean data. We demonstrate that our method is able to improve the performance by 46% on adversarial data and at the cost of only 3% performance degradation on clean data, compared to the model trained with clean data. Additionally, compared to existing robust methods, our method can improve performance by 21% on adversarial examples and 9% on clean data. Our robust model is evaluated with a planner to study its downstream impacts. We demonstrate that our model can significantly reduce the severe accident rates (e.g., collisions and off-road driving).
Electron heating in a current-driven turbulence as a result of nonlinear interaction of electron- and ion-acoustic waves

We study electron heating in collisionless current-driven turbulence due to the nonlinear interactions between electron- and ion-acoustic waves. PIC simulation results show that due to a large difference between the electron and ion mean velocities the Buneman instability excites large-amplitude ion-acoustic waves, which strongly modifies the electron velocity distribution function, leading to a secondary instability that generates fast electron-acoustic waves; and in this process, a giant electron hole is ultimately created. This giant electron hole is responsible for strong electron heating due to phase mixing. The numerical simulation results are consistent with the previous observations and provide insight into the key processes responsible for electron heating and the generation of nonlinear waves in a collisionless current-driven instability. waves and the resulting electron heating in current-driven turbulence. We show that once v D / v Te,  crosses the threshold 1.22-1.48, a slow, large-amplitude ion accoustic wave with a hole in electron phase space (we called it IAW-EH) is generated due to the Buneman instability and creates a bump on the left tail of EVDF. The modified EVDF supports the growth of EAWs and generates fast electron accoustic waves also with holes in the electron phase space. These fast electron waves have large potential amplitude and slow down some high-energy electrons and that leads to electron heating. In the meantime, the IAW-EH grows by the coalescence with other waves by due exchange of trapped electrons in these waves and ultimately becomes large enough to trap the main body of the drifted electron population. In this IAW-EH, electrons are rapidly heated via a strong phase mixing
On the Impact of Serial Dependence on Penalized Regression Methods

This paper characterizes the impact of covariate serial dependence on the non-asymptotic estimation error bound of penalized regressions (PRs). Focusing on the direct relationship between the degree of cross-correlation between covariates and the estimation error bound of PRs, we show that orthogonal or weakly cross-correlated stationary AR processes can exhibit high spurious correlations caused by serial dependence. We provide analytical results on the distribution of the sample cross-correlation in the case of two orthogonal Gaussian AR(1) processes, and extend and validate them through an extensive simulation study. Furthermore, we introduce a new procedure to mitigate spurious correlations in a time series setting, applying PRs to pre-whitened (ARMA filtered) time series. We show that under mild assumptions our procedure allows both to reduce the estimation error and to develop an effective forecasting strategy. The estimation accuracy of our proposal is validated through additional simulations, as well as an empirical application to a large set of monthly macroeconomic time series relative to the Euro Area.
A point to set principle for finite-state dimension

Eﬀective dimension has proven very useful in geometric measure theory through the point-to-set principle [7] that characterizes Hausdorﬀ dimension by relativized eﬀective dimension. Finite-state dimension is the least demanding eﬀectivization in this context [2] that among other results can be used to characterize Borel normality [1]. In this paper we prove a characterization of ﬁnite-state dimension in terms of information content of a real number at a certain precision. We then use this characterization to prove a ﬁnite-state dimension point-to-set principle.
Decentralized Intermittent Feedback Adaptive Control of Non-Triangular Nonlinear Time-Varying Systems

This note investigates the decentralized stabilization problem for a class of interconnected systems in the presence of non-triangular structural uncertainties and time-varying parameters, where each subsystem exchanges information only with its neighbors and only intermittent (rather than continuous) states and input are to be utilized. Thus far to the best of authors' knowledge, no solution exists priori to this work, despite its high prevalence in practice. Two globally decentralized adaptive control schemes are presented based on the backstepping technique: the first one is developed in a continuous fashion by combining the philosophy of the modified congelation of variables-based approach with the special treatment of non-triangular structural uncertainties, which avoids the derivative of time-varying parameters and eliminates the limitation of the triangular condition, thus largely broadens the scope of application. By making use of the important property that the partial derivatives of the constructed virtual controllers in each subsystem are all constants, the second scheme is developed through directly replacing the states in the preceding scheme with the triggered ones. Consequently, the non-differentiability issue of the virtual controllers stemming from intermittent state feedback is completely obviated. The internal signals under both schemes are rigorously shown to be globally uniformly bounded with the aid of several novel lemmas, while the stabilization performance can be enhanced by appropriately adjusting design parameters. Moreover, the interevent intervals are ensured to be lower bounded by a positive constant. Finally, numerical simulation verifies the benefits and efficiency of the proposed method.
Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding

This paper proposes a 4D backbone for long-term point cloud video understanding. A typical way to capture spatial-temporal context is using 4Dconv or transformer without hierarchy. However, those methods are neither effective nor efficient enough due to camera motion, scene changes, sampling patterns, and the complexity of 4D data. To address those issues, we leverage the primitive plane as a mid-level representation to capture the long-term spatial-temporal context in 4D point cloud videos and propose a novel hierarchical backbone named Point Primitive Transformer(PPTr), which is mainly composed of intra-primitive point transformers and primitive transformers. Extensive experiments show that PPTr outperforms the previous state of the arts on different tasks.
An Adaptive Rank Continuation Algorithm for General Weighted Low-rank Recovery

This paper is devoted to proposing a general weighted low-rank recovery model and designing a fast SVD-free computational scheme to solve it. First, our generic weighted low-rank recovery model uniﬁes several existing approaches in the literature. Moreover, our model readily extends to the non-convex setting. Algorithm-wise, most ﬁrst-order proximal algorithms in the literature for low-rank recoveries require computing singular value decomposition (SVD). As SVD does not scale appropriately with the dimension of the matrices, these algorithms become slower when the problem size becomes larger. By incorporating the variational formulation of the nuclear norm into the sub-problem of proximal gradient descent, we avoid computing SVD, which results in signiﬁcant speed-up. Moreover, our algorithm preserves the rank identiﬁcation property of nuclear norm [34] which further allows us to design a rank continuation scheme that asymptotically achieves the minimal iteration complexity. Numerical experiments on both toy examples and real-world problems, including structure from motion (SfM) and photometric stereo, background estimation, and matrix completion, demonstrate the superiority of our proposed algorithm.
Joint Precoding and Phase Shift Design in Reconfigurable Intelligent Surfaces-Assisted Secret Key Generation

Physical layer key generation (PLKG) is a promising technique to establish symmetric keys between resource-constrained legitimate users. However, PLKG suffers from a low key rate in harsh environments where channel randomness is limited. To address the problem, reconfigurable intelligent surfaces (RISs) are introduced to reshape the channels by controlling massive reflecting elements, which can provide more channel diversity. In this paper, we design a channel probing protocol to fully extract the randomness from the cascaded channel, i.e., the channels through reflecting elements. We derive the analytical expressions of the key rate and design a water-filling algorithm based on the Karush-Kuhn-Tucker (KKT) conditions to find the upper bound. To find the optimal precoding and phase shift matrices, we propose an algorithm based on the Grassmann manifold optimization methods. The system is evaluated in terms of the key rate, bit disagreement rate (BDR) and randomness. Simulation results show that our protocols significantly improve the key rate as compared to existing protocols. Compared to multiple-antennas systems without a RIS, our proposed method achieves an average 9.51 dB performance gain when the side length of an element is 1/4 wavelength and the Rician factor is 0 dB.
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods

Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiographs. Recently, several works have indicated that these state-of-the-art classifiers can be biased with respect to sensitive patient attributes, such as race or gender, leading to growing concerns about demographic disparities and discrimination resulting from algorithmic and model-based decision-making in healthcare. Fair machine learning has focused on mitigating such biases against disadvantaged or marginalised groups, mainly concentrating on tabular data or natural images. This work presents two novel intra-processing techniques based on fine-tuning and pruning an already-trained neural network. These methods are simple yet effective and can be readily applied post hoc in a setting where the protected attribute is unknown during the model development and test time. In addition, we compare several intra- and post-processing approaches applied to debiasing deep chest X-ray classifiers. To the best of our knowledge, this is one of the first efforts studying debiasing methods on chest radiographs. Our results suggest that the considered approaches successfully mitigate biases in fully connected and convolutional neural networks offering stable performance under various settings. The discussed methods can help achieve group fairness of deep medical image classifiers when deploying them in domains with different fairness considerations and constraints.
Studying the chemical and kinematical structures of dense cores TMC-1C, L1544, and TMC-1 in the Taurus molecular cloud using CCS and NH3 observations

The measurement of chemical and kinematic structures in pre-stellar cores is essential for better understanding of the star-formation process. Here, we study three pre-stellar cores (TMC-1C, L1544, and TMC-1) of the Taurus molecular cloud by means of the thioxoethenylidene (CCS) radical and ammonia (NH3) molecule observed with the Karl G. Jansky Very Large Array telescope in the D, C, and CNB configurations. Our main results are based on the CCS observation of the TMC-1C core, showing that complex structures are present. A spatial offset relative to dust emission is observed in the CCS radical. Across a wide region around the dust peak, inward motion is found through the CCS radical. We have calculated the infall velocity and measured the turbulence inside the core. The turbulence is found to be subsonic. We obtain that the virial parameter α is < 1. Thus, thermal and non-thermal motions cannot prevent the collapse. Spatial incoherence of the CCS and NH3 is observed from the integrated intensity maps in these cores, suggesting that these molecules trace different environments in the cores. We compare the integrated flux densities of CCS with previous single-dish data and find that a small amount of flux is recovered in the interferometric observations, indicating the presence of significant diffuse emission in favourable conditions for producing CCS.
BACCHUS Analysis of Weak Lines in APOGEE Spectra (BAWLAS)

Elements with weak and blended spectral features in stellar spectra are challenging to measure and require specialized analysis methods to precisely measure their chemical abundances. In this work, we have created a catalog of approximately 120,000 giants with high signal-to-noise Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 17 (DR17) spectra, for which we explore weak and blended species to measure Na, P, S, V, Cu, Ce, and Nd abundances and 12C/13C isotopic ratios. We employ an updated version of the Brussels Automatic Code for Characterizing High-accuracy Spectra (BACCHUS) code to derive these abundances using the stellar parameters measured by APOGEE’s DR17 Stellar Parameters and Chemical Abundances Pipeline, quality flagging to identify suspect spectral lines, and a prescription for upper limits. Combined, these allow us to provide our BACCHUS Analysis of Weak Lines in APOGEE Spectra catalog of precise chemical abundances for these weak and blended species, which agrees well with the literature and improves upon APOGEE abundances for these elements, some of which are unable to be measured with APOGEE’s current, grid-based approach without computationally expensive expansions. This new catalog can be used alongside APOGEE and provides measurements for many scientific applications ranging from nuclear physics to Galactic chemical evolution and Milky Way population studies. To illustrate this we show some examples of uses for this catalog, such as showing that we observe stars with enhanced s-process abundances or that we can use the 12C/13C ratios to explore extra mixing along the red giant branch.
A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies

In this paper, we discuss a framework for teaching bimanual manipulation tasks by imitation. To this end, we present a system and algorithms for learning compliant and contact-rich robot behavior from human demonstrations. The presented system combines insights from admittance control and machine learning to extract control policies that can (a) recover from and adapt to a variety of disturbances in time and space, while also (b) effectively leveraging physical contact with the environment. We demonstrate the effectiveness of our approach using a real-world insertion task involving multiple simultaneous contacts between a manipulated object and insertion pegs. We also investigate efficient means of collecting training data for such bimanual settings. To this end, we conduct a human-subject study and analyze the effort and mental demand as reported by the users. Our experiments show that, while harder to provide, the additional force/torque information available in teleoperated demonstrations is crucial for phase estimation and task success. Ultimately, force/torque data substantially improves manipulation robustness, resulting in a 90% success rate in a multipoint insertion task. Code and videos can be found at https://bimanualmanipulation.com/
Meta-DETR: Image-Level Few-Shot Detection With Inter-Class Correlation Exploitation

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes. Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects. In this work, we design Meta-DETR, which (i) is the first image-level few-shot detector, and (ii) introduces a novel inter-class correlational meta-learning strategy to capture and leverage the correlation among different classes for robust and accurate few-shot object detection. Meta-DETR works entirely at image level without any region proposals, which circumvents the constraint of inaccurate proposals in prevalent few-shot detection frameworks. In addition, the introduced correlational meta-learning enables Meta-DETR to simultaneously attend to multiple support classes within a single feedforward, which allows to capture the inter-class correlation among different classes, thus significantly reducing the misclassification over similar classes and enhancing knowledge generalization to novel classes. Experiments over multiple few-shot object detection benchmarks show that the proposed Meta-DETR outperforms state-of-the-art methods by large margins. The implementation codes are publicly available at https://github.com/ZhangGongjie/Meta-DETR.