Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Precoding and Combining for mmWave Full-Duplex Joint Radar and Communication Systems under Self-Interference

In the context of integrated sensing and communication (ISAC), a full-duplex (FD) transceiver can operate as a monostatic radar while maintaining communication capabilities. This paper investigates the design of precoders and combiners for a joint radar and communication (JRC) system at mmWave frequencies. The primary goals of the design are to minimize self-interference (SI) caused by FD operation, while guaranteeing certain performance in terms of some sensing and communication metrics, as well as taking into account the hardware limitations coming from a hybrid MIMO architecture. Specifically, we introduce a generalized eigenvalue-based precoder that takes into account downlink user rate, radar gain, and SI suppression. Since the hybrid analog/digital architecture degrades the SI suppression capability of the precoder, we further enhance SI suppression with the analog combiner. Our numerical results demonstrate that the proposed architecture achieves the required radar gain and SI mitigation while incurring a small loss in downlink spectral efficiency. Additionally, the numerical experiments also show that the use of orthogonal frequency division multiplexing (OFDM) for radar processing with the proposed beamforming architecture results in highly accurate range and velocity estimates for detected targets.
An Ensemble of 2.5D ResUnet Based Models for Segmentation for Kidney and Masses

The automatic segmentation of kidney, kidney tumor and kidney cyst on Computed Tomography (CT) scans is a challenging task due to the indistinct lesion boundaries and fuzzy texture. Considering the large range and unbalanced distribution of CT scans' thickness, 2.5D ResUnet are adopted to build an efficient coarse-to-fine semantic segmentation framework in this work. A set of 489 CT scans are used for training and validation, and an independent never-before-used CT scans for testing. Finally, we demonstrate the effectiveness of our proposed method. The dice values on test set are 0.954, 0.792, 0.691, the surface dice values are 0.897, 0.591, 0.541 for kidney, tumor and cyst, respectively. The average inference time of each CT scan is 20.65s and the max GPU memory is 3525MB. The results suggest that a better trade-off between model performance and efficiency.
Charged Higgs decay to $W^{\pm}$ and heavy neutral Higgs decaying into $\tau^+\tau^-$ in Georgi Machacek Model at LHC

The CMS collaboration at the Large Hadron Collider (LHC) searched for a charged Higgs boson, in the mass range of $300$ to $700$ GeV, decaying into a $W^{\pm}$ boson and a heavy neutral Higgs boson of mass $200$ GeV, which successively decays into a pair of tau leptons, in proton-proton collisions at $\sqrt{s} = 13$ TeV. In this letter, focussing on the Georgi-Machacek (GM) model, I discuss the parameter space, allowed by the theoretical and experimental constraints, for which the limits on this process obtained by the CMS can be accommodated. The study in this letter also shows that, for the choice of the parameters, the decay of the charged Higgs boson $H_3^{\pm}$ to $W^{\pm}$ and a heavy neutral Higgs boson H is preferred over the decay to any gauge boson and any other neutral or charged Higgs bosons.
Sampling a rare protein transition with a hybrid classical-quantum computing algorithm

Simulating spontaneous structural rearrangements in macromolecules with classical Molecular Dynamics (MD) is an outstanding challenge. Conventional supercomputers can access time intervals up to tens of $\mu$s, while many key events occur on exponentially longer time scales. Transition path sampling techniques have the advantage of focusing the computational power on barrier-crossing trajectories, but generating uncorrelated transition paths that explore diverse conformational regions remains an unsolved problem. We employ a path-sampling paradigm combining machine learning (ML) with quantum computing (QC) to address this issue. We use ML on a classical computer to perform a preliminary uncharted exploration of the conformational space. The data set generated in this exploration is then post-processed to obtain a network representation of the reactive kinetics. Quantum annealing machines can exploit quantum superposition to encode all the transition pathways in this network in the initial quantum state and ensure the generation of completely uncorrelated transition paths. In particular, we resort to the DWAVE quantum computer to perform an all-atom simulation of a protein conformational transition that occurs on the ms timescale. Our results match those of a special purpose supercomputer designed to perform MD simulations. These results highlight the role of biomolecular simulation as a ground for applying, testing, and advancing quantum technologies.
Biomimetic non-ergodic aging by dynamic-to-covalent transitions in physical hydrogels

Hydrogels are soft materials engineered to suit a multitude of applications that exploit their tunable mechanochemical properties. Dynamic hydrogels employing noncovalent, physically crosslinked networks dominated by either enthalpic or entropic interactions enable unique rheological and stimuli-responsive characteristics. In contrast to enthalpy-driven interactions that soften with increasing temperature, entropic interactions result in largely temperature-independent mechanical properties. By engineering interfacial polymer-particle interactions, we can induce a dynamic-to-covalent transition in entropic hydrogels that leads to biomimetic non-ergodic aging in the microstructure without altering the network mesh size. This transition is tuned by varying temperature and formulation conditions such as $p$H, which allows for multivalent tunability in properties. These hydrogels can thus be designed to exhibit either temperature-independent metastable dynamic crosslinking or time-dependent stiffening based on formulation and storage conditions, all while maintaining strucutural features critical for controlling mass transport, akin to many biological tissues. Such robust materials with versatile and adaptable properties can be utilized in applications such as wildfire suppression, surgical adhesives, and depot-forming injectable drug delivery systems.
Gravitational wave fluxes on generic orbits in Kerr spacetime: higher spin and large eccentricity

To obtain the waveform template of gravitational waves (GWs), substantial computational resources and exceedingly high precision are often required. In the previous study [JCAP 11 (2023) 070], we efficiently and accurately calculate GW fluxes of a particle in circular orbits around a Schwarzschild black hole using the confluent Heun function. We extend the previous method to calculate the asymptotic GW fluxes from a particle in generic orbits around a Kerr black hole. Especially when dealing with the computational difficulties in large eccentricity $e=0.9$, higher spin $a=0.999$, higher harmonic modes and strong-field regions, our results are much better than the high-order post-Newtonian results and comparable to the results of the Mano-Suzuki-Takasugi method. Using the grid data of the asymptotic GW fluxes, we give the adiabatic waveform of non-rotating black holes, which agrees well with the waveforms of numerical relativity.
Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles

Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, e.g., route planning for charging. Moreover, understanding why certain predictions are cast is paramount for such a predictive model to gain user trust and be deployed in practice. Since commercial vehicles operate differently as transportation tasks, ambient, and drivers vary, a heterogeneous population is expected when building an AI system for forecasting energy consumption. The dependencies between the input features and the target values are expected to also differ across sub-populations. One well-known example of such a statistical phenomenon is the Simpson paradox. In this paper, we illustrate that such a setting poses a challenge for existing XAI methods that produce global feature statistics, e.g. LIME or SHAP, causing them to yield misleading results. We demonstrate a potential solution by training multiple regression models on subsets of data. It not only leads to superior regression performance but also more relevant and consistent LIME explanations. Given that the employed groupings correspond to relevant sub-populations, the associations between the input features and the target values are consistent within each cluster but different across clusters. Experiments on both synthetic and real-world datasets show that such splitting of a complex problem into simpler ones yields better regression performance and interpretability.
Hypersonic wave wall flow based on gas kinetic method

The transition of hypersonic boundary layer can lead to a several-fold increase in surface heat flux and skin friction for the aircraft, significantly impacting its flight performance. The corrugated wall, as a passive control method for boundary layer flow, also serves as a type of wall microstructure, making its study on the local rarefaction effect of considerable engineering significance. In this study, we employed the conservative discrete unified gas dynamic scheme and utilized a domain-wide numerical simulation method. Initially, we simulated the hypersonic flat plate flow with different depths of corrugated walls under the conditions of incoming flow Mach number of 6 and Reynolds number of ${{10}^{7}}$. Subsequently, we investigated the effects of corrugated walls, including flat plate corrugated walls and wedge corrugated walls, under varying Reynolds numbers for an incoming flow Mach number of 6, and discussed the impact of local rarefaction effect of corrugated walls under different Reynolds numbers. By using the local Knudsen number as the criterion, we found that under these conditions, the occurrence of local rarefaction effect near the corrugated wall due to consecutive failures does not take place when the incoming Reynolds number reaches ${{10}^{7}}$ or ${{10}^{6}}$. However, when the incoming Reynolds number drops to ${{10}^{5}}$, the local rarefaction effect near the corrugated wall becomes evident, with the appearance of non-equilibrium effects in translational and rotational temperatures of molecules. This phenomenon becomes more pronounced as the Reynolds number decreases further.
Global modeling of heterogeneous chemistry on mineral aerosol surfaces: Influence on tropospheric ozone chemistry and comparison to observations

[1] Mineral aerosols can affect gas phase chemistry in the troposphere by providing reactive sites for heterogeneous reactions. We present here a global modeling study of the influence of mineral dust on the tropospheric photochemical cycle. This work is part of the Mineral Dust and Tropospheric Chemistry (MINATROC) project, which focussed on measurement campaigns, laboratory experiments, and integrative modeling. The laboratory experiments provide uptake coefficients for chemical species on mineral aerosol surfaces, which are used to compute the heterogeneous reaction rates in the model. The field measurements at Mount Cimone, northern Italy, provide trace gas and aerosol measurements during a Saharan dust episode and are used to evaluate the model. The simulations include the reactions between mineral dust aerosols and the gas-phase species O3, HNO3, NO3, and N2O5. Under the conditions for the year 2000 the model simulates a decrease in global tropospheric ozone mass by about 5% due to the heterogeneous reactions on dust aerosols. The most important heterogeneous reaction is the uptake of HNO3 on the dust surface, whereby the direct uptake of ozone on dust is not important in atmospheric chemistry. The comparison of the model results to observations indicates that the model simulates well the aerosol mass transported into the Mediterranean during the dust events and the arrival of all major dust events that were observed during a 7 month period. The decrease in ozone concentration during dust events is better simulated by the model when the heterogeneous reactions are included.
Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding

In precision agriculture, the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield. The current machine vision model requires a large volume of data to achieve high performance. However, there are approximately 5.5 million different insect species in the world. None of the existing insect datasets can cover even a fraction of them due to varying geographic locations and acquisition costs. In this paper, we introduce a novel"Insect-1M"dataset, a game-changing resource poised to revolutionize insect-related foundation model training. Covering a vast spectrum of insect species, our dataset, including 1 million images with dense identification labels of taxonomy hierarchy and insect descriptions, offers a panoramic view of entomology, enabling foundation models to comprehend visual and semantic information about insects like never before. Then, to efficiently establish an Insect Foundation Model, we develop a micro-feature self-supervised learning method with a Patch-wise Relevant Attention mechanism capable of discerning the subtle differences among insect images. In addition, we introduce Description Consistency loss to improve micro-feature modeling via insect descriptions. Through our experiments, we illustrate the effectiveness of our proposed approach in insect modeling and achieve State-of-the-Art performance on standard benchmarks of insect-related tasks. Our Insect Foundation Model and Dataset promise to empower the next generation of insect-related vision models, bringing them closer to the ultimate goal of precision agriculture.
Electrical and Thermal Conductivity of Complex-Shaped Contact Spots

This paper explores the electrical and thermal conductivity of complex contact spots on the surface of a half-space. Employing an in-house Fast Boundary Element Method implementation, various complex geometries were studied. Our investigation begins with annulus contact spots to assess the impact of connectedness. We then study shape effects on"multi-petal"spots exhibiting dihedral symmetry, resembling flowers, stars, and gears. The analysis culminates with self-affine shapes, representing a multi-scale generalization of the multi-petal forms. In each case, we introduce appropriate normalizations and develop phenomenological models. For multi-petal shapes, our model relies on a single geometric parameter: the normalized number of"petals". This approach inspired the form of the phenomenological model for self-affine spots, which maintains physical consistency and relies on four geometric characteristics: standard deviation, second spectral moment, Nayak parameter, and Hurst exponent. As a by product, these models enabled us to suggest flux estimations for an infinite number of petals and the fractal limit. This study represents an initial step into understanding the conductivity of complex contact interfaces, which commonly occur in the contact of rough surfaces.
Injecting linguistic knowledge into BERT for Dialogue State Tracking

Dialogue State Tracking (DST) models often employ intricate neural network architectures, necessitating substantial training data, and their inference processes lack transparency. This paper proposes a method that extracts linguistic knowledge via an unsupervised framework and subsequently utilizes this knowledge to augment BERT's performance and interpretability in DST tasks. The knowledge extraction procedure is computationally economical and does not necessitate annotations or additional training data. The injection of the extracted knowledge necessitates the addition of only simple neural modules. We employ the Convex Polytopic Model (CPM) as a feature extraction tool for DST tasks and illustrate that the acquired features correlate with the syntactic and semantic patterns in the dialogues. This correlation facilitates a comprehensive understanding of the linguistic features influencing the DST model's decision-making process. We benchmark this framework on various DST tasks and observe a notable improvement in accuracy.
Bayesian Approach to Linear Bayesian Networks

This study proposes the first Bayesian approach for learning high-dimensional linear Bayesian networks. The proposed approach iteratively estimates each element of the topological ordering from backward and its parent using the inverse of a partial covariance matrix. The proposed method successfully recovers the underlying structure when Bayesian regularization for the inverse covariance matrix with unequal shrinkage is applied. Specifically, it shows that the number of samples $n = \Omega( d_M^2 \log p)$ and $n = \Omega(d_M^2 p^{2/m})$ are sufficient for the proposed algorithm to learn linear Bayesian networks with sub-Gaussian and 4m-th bounded-moment error distributions, respectively, where $p$ is the number of nodes and $d_M$ is the maximum degree of the moralized graph. The theoretical findings are supported by extensive simulation studies including real data analysis. Furthermore the proposed method is demonstrated to outperform state-of-the-art frequentist approaches, such as the BHLSM, LISTEN, and TD algorithms in synthetic data.
OpenPerf: A Benchmarking Framework for the Sustainable Development of the Open-Source Ecosystem

Benchmarking involves designing scientific test methods, tools, and frameworks to quantitatively and comparably assess specific performance indicators of certain test subjects. With the development of artificial intelligence, AI benchmarking datasets such as ImageNet and DataPerf have gradually become consensus standards in both academic and industrial fields. However, constructing a benchmarking framework remains a significant challenge in the open-source domain due to the diverse range of data types, the wide array of research issues, and the intricate nature of collaboration networks. This paper introduces OpenPerf, a benchmarking framework designed for the sustainable development of the open-source ecosystem. This framework defines 9 task benchmarking tasks in the open-source research, encompassing 3 data types: time series, text, and graphics, and addresses 6 research problems including regression, classification, recommendation, ranking, network building, and anomaly detection. Based on the above tasks, we implemented 3 data science task benchmarks, 2 index-based benchmarks, and 1 standard benchmark. Notably, the index-based benchmarks have been adopted by the China Electronics Standardization Institute as evaluation criteria for open-source community governance. Additionally, we have developed a comprehensive toolkit for OpenPerf, which not only offers robust data management, tool integration, and user interface capabilities but also adopts a Benchmarking-as-a-Service (BaaS) model to serve academic institutions, industries, and foundations. Through its application in renowned companies and institutions such as Alibaba, Ant Group, and East China Normal University, we have validated OpenPerf's pivotal role in the healthy evolution of the open-source ecosystem.
High-quality single InGaAs/GaAs quantum dot growth on a CMOS-compatible silicon substrate for quantum photonic applications

We present the direct heteroepitaxial growth of high-quality InGaAs quantum dots on silicon, enabling scalable, cost-effective quantum photonics devices compatible with CMOS technology. GaAs heterostructures are grown on silicon via a GaP buffer and defect-reducing layers. These epitaxial quantum dots exhibit optical properties akin to those on traditional GaAs substrates, promising vast potential for the heteroepitaxy approach. They demonstrate strong multi-photon suppression with $g^{(2)}(\tau)=(3.7\pm 0.2) \times 10^{-2}$ and high photon indistinguishability $V=(66\pm 19)$% under non-resonance excitation. We achieve up to ($18\pm 1$)% photon extraction efficiency with a backside distributed Bragg mirror, marking a crucial step toward silicon-based quantum nanophotonics.
Joint Antenna Selection and Power Allocation in Massive MIMO Systems with Cell Division Technique for MRT and ZF Precoding Schemes

One of the most important challenges in the fifth generation (5G) of telecommunication systems is the efficiency of energy and spectrum. Massive multiple-input multiple-output (MIMO) systems have been proposed by researchers to resolve existing challenges. In the proposed system model of this paper, there is a base station (BS) around which several users and an eavesdropper (EVA) are evenly distributed. The information transmitted between BS and users is disrupted by an EVA, which highlights the importance of secure transfer. This paper analyzes secure energy efficiency (EE) of a massive MIMO system, and its purpose is to maximize the secure EE of the system. Several scenarios are considered to evaluate achieving the desired goal. To maximize the secure EE, selecting optimal number of antennas and cell division methods are employed. Each of these two methods is applied in a system with the maximum ratio transmission (MRT) and the zero forcing (ZF) precodings, and then the problem is solved. Maximum transmission power and minimum secure rate for users insert limitations to the optimization problem. Channel state information (CSI) is generally imperfect for users in any method, while CSI of the EVA is considered perfect as the worst case. Four iterative algorithms are designed to provide numerical assessments. The first algorithm calculates the optimal power of users without utilizing existing methods, the second one is related to the cell division method, the third one is based on the strategy of selecting optimal number of antennas, and forth one is based on a hybrid strategy.
Critiquing Self-report Practices for Human Mental and Wellbeing Computing at Ubicomp

Computing human mental and wellbeing is crucial to various domains, including health, education, and entertainment. However, the reliance on self-reporting in traditional research to establish ground truth often leads to methodological inconsistencies and susceptibility to response biases, thus hindering the effectiveness of modelling. This paper presents the first systematic methodological review of self-reporting practices in Ubicomp within the context of human mental and wellbeing computing. Drawing from existing survey research, we establish guidelines for self-reporting in human wellbeing studies and identify shortcomings in current practices at Ubicomp community. Furthermore, we explore the reliability of self-report as a means of ground truth and propose directions for improving ground truth measurement in this field. Ultimately, we emphasize the urgent need for methodological advancements to enhance human mental and wellbeing computing.