Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximation of Single Scattering Properties of Ice and Snow Particles for High Microwave Frequencies

Abstract As satellite high-frequency passive microwave data have recently become available, there is an increasing demand for an accurate and computationally efficient method to calculate the single scattering properties of nonspherical ice particles, so that it may be used in radiative transfer models for physical retrievals of ice water path and snowfall rate. In this study, two such approximations are presented for calculating the single scattering properties of three types of large ice particles: bullet rosettes, sector snowflakes, and dendrite snowflakes, for the frequency range of 85 to 220 GHz, based on results of discrete-dipole approximation (DDA) modeling. By analyzing the DDA modeling results, it is noted that, for nonspherical ice particles, the scattering and absorption cross sections and the asymmetry parameter have a magnitude between those of the two imaginary equal-mass spheres. One is a solid sphere, and the other is an ice–air mixed soft sphere whose diameter equals the particle's maxim.
Fast non-volatile electric control of antiferromagnetic states

Electrical manipulation of antiferromagnetic states, a cornerstone of antiferromagnetic spintronics, is a great challenge, requiring novel material platforms. Here we report the full control over antiferromagnetic states by voltage pulses in the insulating Co$_3$O$_4$ spinel. We show that the strong linear magnetoelectric effect emerging in its antiferromagnetic state is fully governed by the orientation of the N\'eel vector. As a unique feature of Co$_3$O$_4$, the magnetoelectric energy can easily overcome the weak magnetocrystalline anisotropy, thus, the N\'eel vector can be manipulated on demand, either rotated smoothly or reversed suddenly, by combined electric and magnetic fields. We succeed with switching between antiferromagnetic states of opposite N\'eel vectors by voltage pulses within a few microsecond in macroscopic volumes. These observations render quasi-cubic antiferromagnets, like Co$_3$O$_4$, an ideal platform for the ultrafast (pico- to nanosecond) manipulation of microscopic antiferromagnetic domains and may pave the way for the realization of antiferromagnetic spintronic devices.
Coupled system of electrons and exciton-polaritons: Screening, dynamical effects, and superconductivity

Bose-Fermi systems such as mixtures of electrons with excitons or exciton-polaritons are extensively discussed as candidates to host a variety of intriguing phenomena, including polaron formation, drag effects, supersolidity, and superconductivity. In this work, assuming the strong-coupling regime between the semiconductor excitons and cavity photons, we develop the many-body theory approach addressing the interplay of different types of interaction among various species in such a mixture, wherein we take into account dynamical density responses of both the Bose-condensed exciton-polaritons and the two-dimensional electron gas inside an optical microcavity. As was anticipated previously, at high enough polariton densities the lower hybrid mode of the system's excitation spectrum acquires a roton minimum, making the system prone to superconducting pairing in the vicinity of the roton instability. We analyze the possibility of polariton-BEC-mediated superconductivity in the electron gas taking into account full momentum and frequency dependence of the gap, as well as in the Eliashberg approach where the momentum dependence is neglected, and in the Bardeen-Cooper-Schrieffer approach that discards the frequency dependence and dynamical effects. Considering the interaction screening in Thomas-Fermi and in random-phase approximations, we estimate the critical temperatures of superconductivity to be not larger than 0.1 K in the vicinity of instability. As possible realizations of the coupled polariton-electron system, semiconductor quantum wells and two-dimensional transition metal dichalcogenides are considered.
EdgeRIC: Empowering Realtime Intelligent Optimization and Control in NextG Networks

Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we develop a RIC platform called RICworld, consisting of (i) EdgeRIC, which is colocated, but decoupled from the RAN stack, and can access RAN and application-level information to execute AI-optimized and other policies in realtime (sub-millisecond) and (ii) DigitalTwin, a full-stack, trace-driven emulator for training AI-based policies offline. We demonstrate that realtime EdgeRIC operates as if embedded within the RAN stack and significantly outperforms a cloud-based near-realtime RIC (>15 ms latency) in terms of attained throughput. We train AI-based polices on DigitalTwin, execute them on EdgeRIC, and show that these policies are robust to channel dynamics, and outperform queueing-model based policies by 5% to 25% on throughput and application-level benchmarks in a variety of mobile environments.
Understanding People’s Needs in Viewing Diverse Social Opinions about Controversial Topics

Social media (i.e., Reddit) users are overloaded with people’s opinions when viewing discourses about divisive topics. Traditional user interfaces in such media present those opinions in a linear structure, which can limit users in viewing diverse social opinions at scale. Prior work has recognized this limitation, that the linear structure can reinforce biases, where a certain point of view becomes widespread simply because many viewers seem to believe it. This limitation can make it difficult for users to have a truly conversational mode of mediated discussion. Thus, when designing a user interface for viewing people’s opinions, we should consider ways to mitigate selective exposure to information and polarization of opinions. We conducted a needs-finding study with 11 Reddit users, who follow climate change threads and make posts and comments regularly. In the study, we aimed to understand key limitations in people viewing online controversial discourses and to extract design implications to address these problems. Our findings discuss potential future directions to address these problems.
Learning-enabled multi-modal motion prediction in urban environments

Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to assure safety while navigating through highly interactive complex scenarios. In this work, the framework IAMP (Interaction-Aware Motion Prediction), producing multi-modal probabilistic outputs from the integration of a Dynamic Bayesian Network and Markov Chains, is extended with a learning-based approach. The integration of a machine learning model tackles the limitations of the ruled-based mechanism since it can better adapt to different driving styles and driving situations. The method here introduced generates context-dependent acceleration distributions used in a Markov-chain-based motion prediction. This hybrid approach results in better evaluation metrics when compared with the baseline in the four highly-interactive scenarios obtained from publicly available datasets.
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-tuned GPT

Moreover, GPT-based zero-shot classification models tend to make independent predictions over test instances, which can be sub-optimal as the instance correlations and the decision boundaries in the target space are ignored. To address these difficulties and limitations, we propose a new approach to zero-shot text classification, namely \ourmodelshort, which leverages the strong generative power of GPT to assist in training a smaller, more adaptable, and efficient sentence encoder classifier with contrastive self-training. Specifically, GenCo applies GPT in two ways: firstly, it generates multiple augmented texts for each input instance to enhance the semantic embedding of the instance and improve the mapping to relevant labels; secondly, it generates augmented texts conditioned on the predicted label during self-training, which makes the generative process tailored to the decision boundaries in the target space. In our experiments, GenCo outperforms previous state-of-the-art methods on multiple benchmark datasets, even when only limited in-domain text data is available.
GREED: A Neural Framework for Learning Graph Distance Functions

Among various distance functions for graphs, graph and subgraph edit distances (GED and SED respectively) are two of the most popular and expressive measures. Unfortunately, exact computations for both are NP-hard. To overcome this computational bottleneck, neural approaches to learn and predict edit distance in polynomial time have received much interest. While considerable progress has been made, there exist limitations that need to be addressed. First, the efficacy of an approximate distance function lies not only in its approximation accuracy, but also in the preservation of its properties. To elaborate, although GED is a metric, its neural approximations do not provide such a guarantee. This prohibits their usage in higher order tasks that rely on metric distance functions, such as clustering or indexing. Second, several existing frameworks for GED do not extend to SED due to SED being asymmetric. In this work, we design a novel siamese graph neural network called GREED, which through a carefully crafted inductive bias, learns GED and SED in a property-preserving manner. Through extensive experiments across 10 real graph datasets containing up to 7 million edges, we establish that GREED is not only more accurate than the state of the art, but also up to 3 orders of magnitude faster. Even more significantly, due to preserving the triangle inequality, the generated embeddings are indexable and consequently, even in a CPU-only environment, GREED is up to 50 times faster than GPU-powered baselines for graph / subgraph retrieval.
Optimal Decision Making in Active Queue Management

Active Queue Management (AQM) aims to prevent bufferbloat and serial drops in router and switch FIFO packet buffers that usually employ drop-tail queueing. AQM describes methods to send proactive feedback to TCP flow sources to regulate their rate using selective packet drops or markings. Traditionally, AQM policies relied on heuristics to approximately provide Quality of Service (QoS) such as a target delay for a given flow. These heuristics are usually based on simple network and TCP control models together with the monitored buffer filling. A primary drawback of these heuristics is that their way of accounting flow characteristics into the feedback mechanism and the corresponding effect on the state of congestion are not well understood. In this work, we show that taking a probabilistic model for the flow rates and the dequeueing pattern, a Semi-Markov Decision Process (SMDP) can be formulated to obtain an optimal packet-dropping policy. This policy-based AQM, named PAQMAN, takes into account a steady-state model of TCP and a target delay for the flows. Additionally, we present an inference algorithm that builds on TCP congestion control in order to calibrate the model parameters governing underlying network conditions. Using simulation, we show that the prescribed AQM yields comparable throughput to state-of-the-art AQM algorithms while reducing delays significantly.
Mixed radial-angular bounds for Hardy-type operators on Heisenberg group

In this paper, we study $ n $-dimensional Hardy operator and its dual in mixed radial-angular spaces on Heisenberg group and obtain their sharp bounds by using the rotation method. Furthermore, the sharp bounds of $ n $-dimensional weighted Hardy operator and weighted Cesàro operator are also obtained.
Underwater object classification combining SAS and transferred optical-to-SAS Imagery

Combining synthetic aperture sonar (SAS) imagery with optical images for underwater object classification has the potential to overcome challenges such as water clarity, the stability of the optical image analysis platform, and strong reflections from the seabed for sonar-based classification. In this work, we propose this type of multi-modal combination to discriminate between man-made targets and objects such as rocks or litter. We offer a novel classification algorithm that overcomes the problem of intensity and object formation differences between the two modalities. To this end, we develop a novel set of geometrical shape descriptors that takes into account the geometrical relation between the objects shadow and highlight. Results from 7,052 pairs of SAS and optical images collected during several sea experiments show improved classification performance compared to the state-of-the-art for better discrimination between different types of underwater objects. For reproducibility, we share our database.
Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach

Public opinion is a crucial factor in shaping political decision-making. Nowadays, social media has become an essential platform for individuals to engage in political discussions and express their political views, presenting researchers with an invaluable resource for analyzing public opinion. In this paper, we focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors and convert the task into a Graph Neural Network (GNN)-based node classification problem. Then, we introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors, which are also tweet nodes due to the graph's bipartite nature, effectively leveraging user behavioral information. The experimental results show that our proposed model significantly outperforms several competitive baselines. Further analyses demonstrate the significance of user behavioral information and the effectiveness of skip aggregation.
Hier-RTLMP: A Hierarchical Automatic Macro Placer for Large-scale Complex IP Blocks

In a typical RTL to GDSII flow, floorplanning or macro placement is a critical step in achieving decent quality of results (QoR). Moreover, in today's physical synthesis flows (e.g., Synopsys Fusion Compiler or Cadence Genus iSpatial), a floorplan . def with macro and IO pin placements is typically needed as an input to the front-end physical synthesis. Recently, with the increasing complexity of IP blocks, and in particular with auto-generated RTL for machine learning (ML) accelerators, the number of hard macros in a single RTL block can easily run into the several hundreds. This makes the task of generating an automatic floorplan (.def) with IO pin and macro placements for front-end physical synthesis even more critical and challenging. The so-called peripheral approach of forcing macros to the periphery of the layout is no longer viable when the ratio of the sum of the macro perimeters to the floorplan perimeter is large, since this increases the required stacking depth of macros. In this paper, we develop a novel multilevel physical planning approach that exploits the hierarchy and dataflow inherent in the design RTL, and describe its realization in a new hierarchical macro placer, Hier-RTLMP. Hier-RTLMP borrows from traditional approaches used in manual system-on-chip (SoC) floorplanning to create an automatic macro placement for use with large IP blocks containing very large numbers of hard macros. Empirical studies demonstrate substantial improvements over the previous RTL-MP macro placement approach, and promising post-route improvements relative to a leading commercial place-and-route tool.
Robust and differentially private stochastic linear bandits

In this paper, we study the stochastic linear bandit problem under the additional requirements of differential privacy, robustness and batched observations. In particular, we assume an adversary randomly chooses a constant fraction of the observed rewards in each batch, replacing them with arbitrary numbers. We present differentially private and robust variants of the arm elimination algorithm using logarithmic batch queries under two privacy models and provide regret bounds in both settings. In the first model, every reward in each round is reported by a potentially different client, which reduces to standard local differential privacy (LDP). In the second model, every action is"owned"by a different client, who may aggregate the rewards over multiple queries and privatize the aggregate response instead. To the best of our knowledge, our algorithms are the first simultaneously providing differential privacy and adversarial robustness in the stochastic linear bandits problem.
On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steering

Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base stations (BSs). In conventional cell-centric TS strategies, BSs make TS decisions for all user equipment (UEs) in a centralized manner, which focuses more on the overall performance of the whole cell, disregarding specific requirements of individual UE. The flourishing machine learning technologies and evolving UE-centric 5G network architecture have prompted the emergence of new TS technologies. In this paper, we propose a knowledge transfer and federated learning-enabled UE-centric (KT-FLUC) TS framework for highly dynamic 5G radio access networks (RAN). Specifically, first, we propose an attention-weighted group federated learning scheme. It enables intelligent UEs to make TS decisions autonomously using local models and observations, and a global model is defined to coordinate local TS decisions and share experiences among UEs. Secondly, considering the individual UE's limited computation and energy resources, a growing and pruning-based model compression method is introduced, mitigating the computation burden of UEs and reducing the communication overhead of federated learning. In addition, we propose a Q-value-based knowledge transfer method to initialize newcomer UEs, achieving a jump start for their training efficiency. Finally, the simulations show that our proposed KT-FLUC algorithm can effectively improve the service quality, achieving 65\% and 38\% lower delay and 52% and 57% higher throughput compared with cell-based TS and other UE-centric TS strategies, respectively.
Simulation of Surface Plasmon Excitation in a Plasmonic Nano-Wire Using Surface Integral Equations

In this paper, scattering of a plane and monochromatic electromagnetic wave from a nano-wire is simulated using surface integral equations. First, integral equationsgoverning unknown fields on the surface is obtained based on Stratton-Cho surface integral equations. Then, the interaction of the wave with a non-plasmonic as well as a palsmonic nano-wire is considered. It is shown that in scattering of the wave from the non-plasmonic nano-wire the simple phenomena of refraction and transmission occur. On the other hand, the interaction of TM polarized light with plasmonic nano- wire excites surface plasmon waves. Simulations show that no surface plasmon is excited in interaction of TE polarized light with plasmonioc nano-wire. It is observed that, while increasing the frequency of incident light, the regime of scattering goes from electrostatic limit to simple geometric limit through diffraction region. In continuation, charge distribution induced by surface plasmon is simulated for different times. The simulation shows that a wave-like surface charge is excited and propagates on the surface.There is a very weak charge distribution within the nano-wire indicating that no light penetrates the wire.
Deep Audio-Visual Singing Voice Transcription based on Self-Supervised Learning Models

Singing voice transcription converts recorded singing audio to musical notation. Sound contamination (such as accompaniment) and lack of annotated data make singing voice transcription an extremely difficult task. We take two approaches to tackle the above challenges: 1) introducing multimodal learning for singing voice transcription together with a new multimodal singing dataset, N20EMv2, enhancing noise robustness by utilizing video information (lip movements to predict the onset/offset of notes), and 2) adapting self-supervised learning models from the speech domain to the singing voice transcription task, significantly reducing annotated data requirements while preserving pretrained features. We build a self-supervised learning based audio-only singing voice transcription system, which not only outperforms current state-of-the-art technologies as a strong baseline, but also generalizes well to out-of-domain singing data. We then develop a self-supervised learning based video-only singing voice transcription system that detects note onsets and offsets with an accuracy of about 80\%. Finally, based on the powerful acoustic and visual representations extracted by the above two systems as well as the feature fusion design, we create an audio-visual singing voice transcription system that improves the noise robustness significantly under different acoustic environments compared to the audio-only systems.
Inductive sensing of magnetic microrobots under actuation by rotating magnetic fields

Abstract The engineering space for magnetically manipulated biomedical microrobots is rapidly expanding. This includes synthetic, bioinspired, and biohybrid designs, some of which may eventually assume clinical roles aiding drug delivery or performing other therapeutic functions. Actuating these microrobots with rotating magnetic fields (RMFs) and the magnetic torques they exert offers the advantages of efficient mechanical energy transfer and scalable instrumentation. Nevertheless, closed-loop control still requires a complementary noninvasive imaging modality to reveal position and trajectory, such as ultrasound or X-rays, increasing complexity and posing a barrier to use. Here, we investigate the possibility of combining actuation and sensing via inductive detection of model microrobots under field magnitudes ranging from 100 s of microtesla to 10 s of millitesla rotating at 1 to 100 Hz. A prototype apparatus accomplishes this using adjustment mechanisms for both phase and amplitude to finely balance sense and compensation coils, suppressing the background signal of the driving RMF by 90 dB. Rather than relying on frequency decomposition to analyze signals, we show that, for rotational actuation, phase decomposition is more appropriate. We demonstrate inductive detection of a micromagnet placed in two distinct viscous environments using RMFs with fixed and time-varying frequencies. Finally, we show how magnetostatic selection fields can spatially isolate inductive signals from a micromagnet actuated by an RMF, with the resolution set by the relative magnitude of the selection field and the RMF. The concepts developed here lay a foundation for future closed-loop control schemes for magnetic microrobots based on simultaneous inductive sensing and actuation.