Miracle-dz/UniFAR-FTUs huggingface dataset:
Dirac series of $GL(n,\mathbb{H})$

We determine the Dirac series of $GL(n,\mathbb{H})$, give the spin lowest $K$-types of the Dirac series, and show that the spin lowest $K$-type of any Dirac series is unique and multiplicity free. In addition, using the same method, we show that the spin lowest $K$-type of any Dirac series in the $GL(n,\mathbb{R})$ case is unique and multiplicity free.
Gradient Descent Ascent in Min-Max Stackelberg Games

Min-max optimization problems (i.e., min-max games) have at-tracted a great deal of attention recently as their applicability to a wide range of machine learning problems has become evident. In this paper, we study min-max games with dependent strategy sets, where the strategy of the first player constrains the behavior of the second. Such games are best understood as sequential, i.e., Stackelberg, games, for which the relevant solution concept is Stackelberg equilibrium, a generalization of Nash. One of the most popular algorithms for solving min-max games is gradient descent ascent (GDA). We present a straightforward generalization of GDA to min-max Stackelberg games with dependent strategy sets, but show that it may not converge to a Stackelberg equilibrium. We then introduce two variants of GDA, which assume access to a solution oracle for the optimal Karush Kuhn Tucker (KKT) multipliers of the games’ constraints. We show that such an oracle exists for a large class of convex-concave min-max Stackelberg games, and provide proof that our GDA variants with such an oracle converge in 𝑂 ( 1 / 𝜀 2 ) iterations to an 𝜀 -Stackelberg equilibrium, improving on the most efficient algorithms currently known which converge in 𝑂 ( 1 / 𝜀 3 ) iterations. We then show that solving Fisher markets, a canonical example of a min-max Stackelberg game, using our novel algorithm, corresponds to buyers and sellers using myopic best-response dynamics in a repeated market, allowing us to prove the convergence of these dynamics in 𝑂 ( 1 / 𝜀 2 ) iterations in Fisher markets. We close by describing experiments on Fisher markets which suggest potential ways to extend our theoretical results, by demonstrating how different properties of the objective function can affect the convergence and convergence rate of our algorithms.
C2IMUFS: Complementary and Consensus Learning-Based Incomplete Multi-View Unsupervised Feature Selection

Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods assume that all of views are complete. However, multi-view data are usually incomplete, i.e., a part of instances are presented on some views but not all views. Besides, learning the complete similarity graph, as an important promising technology in existing MUFS methods, cannot achieve due to the missing views. In this paper, we propose a complementary and consensus learning-based incomplete multi-view unsupervised feature selection method (C<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="huang-ieq3-3266595.gif"/></alternatives></inline-formula>IMUFS) to address the aforementioned issues. Concretely, C<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="huang-ieq4-3266595.gif"/></alternatives></inline-formula>IMUFS integrates feature selection into an extended weighted non-negative matrix factorization model equipped with adaptive learning of view-weights and a sparse <inline-formula><tex-math notation="LaTeX">$\ell _{2,p}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>ℓ</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="huang-ieq5-3266595.gif"/></alternatives></inline-formula>-norm, which can offer better adaptability and flexibility. By the sparse linear combinations of multiple similarity matrices derived from different views, a complementary learning-guided similarity matrix reconstruction model is presented to obtain the complete similarity graph in each view. Furthermore, C<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="huang-ieq6-3266595.gif"/></alternatives></inline-formula>IMUFS learns a consensus clustering indicator matrix across different views and embeds it into a spectral graph term to preserve the local geometric structure. Comprehensive experimental results on real-world datasets demonstrate the effectiveness of C<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="huang-ieq7-3266595.gif"/></alternatives></inline-formula>IMUFS compared with state-of-the-art methods.
Physical LiDAR Simulation in Real-Time Engine

Designing and validating sensor applications and algorithms in simulation is an important step in the modern development process. Furthermore, modern open-source multi-sensor simulation frameworks are moving towards the usage of video-game engines such as the Unreal Engine. Simulation of a sensor such as a LiDAR can prove to be difficult in such real-time software. In this paper we present a GPU-accelerated simulation of LiDAR based on its physical properties and interaction with the environment. We provide a generation of the depth and intensity data based on the properties of the sensor as well as the surface material and incidence angle at which the light beams hit the surface. It is validated against a real LiDAR sensor and shown to be accurate and precise although highly depended on the spectral data used for the material properties.
Scrooge: a fast and memory-frugal genomic sequence aligner for CPUs, GPUs, and ASICs

Abstract Motivation Pairwise sequence alignment is a very time-consuming step in common bioinformatics pipelines. Speeding up this step requires heuristics, efficient implementations, and/or hardware acceleration. A promising candidate for all of the above is the recently proposed GenASM algorithm. We identify and address three inefficiencies in the GenASM algorithm: it has a high amount of data movement, a large memory footprint, and does some unnecessary work. Results We propose Scrooge, a fast and memory-frugal genomic sequence aligner. Scrooge includes three novel algorithmic improvements which reduce the data movement, memory footprint, and the number of operations in the GenASM algorithm. We provide efficient open-source implementations of the Scrooge algorithm for CPUs and GPUs, which demonstrate the significant benefits of our algorithmic improvements. For long reads, the CPU version of Scrooge achieves a 20.1×, 1.7×, and 2.1× speedup over KSW2, Edlib, and a CPU implementation of GenASM, respectively. The GPU version of Scrooge achieves a 4.0×, 80.4×, 6.8×, 12.6×, and 5.9× speedup over the CPU version of Scrooge, KSW2, Edlib, Darwin-GPU, and a GPU implementation of GenASM, respectively. We estimate an ASIC implementation of Scrooge to use 3.6× less chip area and 2.1× less power than a GenASM ASIC while maintaining the same throughput. Further, we systematically analyze the throughput and accuracy behavior of GenASM and Scrooge under various configurations. As the best configuration of Scrooge depends on the computing platform, we make several observations that can help guide future implementations of Scrooge. Availability and implementation https://github.com/CMU-SAFARI/Scrooge.
CycleTrans: Learning Neutral yet Discriminative Features for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a task of matching the same individuals across the visible and infrared modalities. Its main challenge lies in the modality gap caused by cameras operating on different spectra. Existing VI-ReID methods mainly focus on learning general features across modalities, often at the expense of feature discriminability. To address this issue, we present a novel cycle-construction-based network for neutral yet discriminative feature learning, termed CycleTrans. Specifically, CycleTrans uses a lightweight Knowledge Capturing Module (KCM) to capture rich semantics from the modality-relevant feature maps according to pseudo queries. Afterwards, a Discrepancy Modeling Module (DMM) is deployed to transform these features into neutral ones according to the modality-irrelevant prototypes. To ensure feature discriminability, another two KCMs are further deployed for feature cycle constructions. With cycle construction, our method can learn effective neutral features for visible and infrared images while preserving their salient semantics. Extensive experiments on SYSU-MM01 and RegDB datasets validate the merits of CycleTrans against a flurry of state-of-the-art methods, +4.57% on rank-1 in SYSU-MM01 and +2.2% on rank-1 in RegDB.
Multiple Descent in the Multiple Random Feature Model

Recent works have demonstrated a double descent phenomenon in over-parameterized learning. Although this phenomenon has been investigated by recent works, it has not been fully understood in theory. In this paper, we investigate the multiple descent phenomenon in a class of multi-component prediction models. We first consider a ''double random feature model'' (DRFM) concatenating two types of random features, and study the excess risk achieved by the DRFM in ridge regression. We calculate the precise limit of the excess risk under the high dimensional framework where the training sample size, the dimension of data, and the dimension of random features tend to infinity proportionally. Based on the calculation, we further theoretically demonstrate that the risk curves of DRFMs can exhibit triple descent. We then provide a thorough experimental study to verify our theory. At last, we extend our study to the ''multiple random feature model'' (MRFM), and show that MRFMs ensembling $K$ types of random features may exhibit $(K+1)$-fold descent. Our analysis points out that risk curves with a specific number of descent generally exist in learning multi-component prediction models.
Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentation

Albeit with varying degrees of progress in the field of Semi-Supervised Semantic Segmentation, most of its recent successes are involved in unwieldy models and the lightweight solution is still not yet explored. We find that existing knowledge distillation techniques pay more attention to pixel-level concepts from labeled data, which fails to take more informative cues within unlabeled data into account. Consequently, we offer the first attempt to provide lightweight SSSS models via a novel multi-granularity distillation (MGD) scheme, where multi-granularity is captured from three aspects: i) complementary teacher structure; ii) labeled-unlabeled data cooperative distillation; iii) hierarchical and multi-levels loss setting. Specifically, MGD is formulated as a labeled-unlabeled data cooperative distillation scheme, which helps to take full advantage of diverse data characteristics that are essential in the semi-supervised setting. Image-level semantic-sensitive loss, region-level content-aware loss, and pixel-level consistency loss are set up to enrich hierarchical distillation abstraction via structurally complementary teachers. Experimental results on PASCAL VOC2012 and Cityscapes reveal that MGD can outperform the competitive approaches by a large margin under diverse partition protocols. For example, the performance of ResNet-18 and MobileNet-v2 backbone is boosted by 11.5% and 4.6% respectively under 1/16 partition protocol on Cityscapes. Although the FLOPs of the model backbone is compressed by 3.4-5.3x (ResNet-18) and 38.7-59.6x (MobileNetv2), the model manages to achieve satisfactory segmentation results.
Event-Triggered Model Predictive Control With Deep Reinforcement Learning for Autonomous Driving

Event-triggered model predictive control (eMPC) is a popular optimal control method with an aim to alleviate the computation and/or communication burden of MPC. However, it generally requires a priori knowledge of the closed-loop system behavior along with the communication characteristics for designing the event-trigger policy. This paper attempts to solve this challenge by proposing an efficient eMPC framework and demonstrates successful implementation of this framework on the autonomous vehicle path following. First of all, a model-free reinforcement learning (RL) agent is used to learn the optimal event-trigger policy without the need for a complete dynamical system and communication knowledge in this framework. Furthermore, techniques including prioritized experience replay (PER) buffer and long short-term memory (LSTM) are employed to foster exploration and improve training efficiency. In this paper, we use the proposed framework with three deep RL algorithms, i.e., Double Q-learning (DDQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC), to solve this problem. Results show that all three deep RL-based eMPC (deep-RL-eMPC) can achieve better evaluation performance than the conventional threshold-based and previous linear Q-based approach in the autonomous path following. In particular, PPO-eMPC with LSTM and DDQN-eMPC with PER and LSTM obtain a superior balance between the closed-loop control performance and event-trigger frequency.
Ionizing Radiation Fluctuations and Large-Scale Structure in the Lyα Forest

We investigate the large-scale inhomogeneities of the hydrogen-ionizing radiation field in the universe at redshift z = 3. Using a ray-tracing algorithm, we simulate a model in which quasars are the dominant sources of radiation. We make use of large-scale N-body simulations of a Λ cold dark matter universe and include such effects as finite quasar lifetimes and output on the light cone, which affects the shape of quasar light echoes. We create Lyα forest spectra that would be generated in the presence of such a fluctuating radiation field, finding that the power spectrum of the Lyα forest can be suppressed by as much as 15% for modes with k = 0.05-1 h Mpc-1. This relatively small effect may have consequences for high-precision measurements of the Lyα power spectrum on larger scales than have yet been published. We also investigate a second probe of the ionizing radiation fluctuations, the cross-correlation of quasar positions and the Lyα forest. For both quasar lifetimes that we simulate (107 and 108 yr), we expect to see a strong decrease in the Lyα absorption close to other quasars (the "foreground" proximity effect). We then use data from the Sloan Digital Sky Survey First Data Release to make an observational determination of this statistic. We find no sign of our predicted lack of absorption, but instead increased absorption close to quasars. If the bursts of radiation from quasars last on average less than 106 yr, then we would not expect to be able to see the foreground effect. However, the strength of the absorption itself seems to be indicative of rare objects and hence much longer total times of emission per quasar. Variability of quasars in bursts with timescales of between 104 and 106 yr could reconcile these two facts.
IceCube Search for Neutrinos Coincident with Gravitational Wave Events from LIGO/Virgo Run O3

Using data from the IceCube Neutrino Observatory, we searched for high-energy neutrino emission from the gravitational-wave events detected by the advanced LIGO and Virgo detectors during their third observing run. We did a low-latency follow-up on the public candidate events released during the detectors’ third observing run and an archival search on the 80 confident events reported in the GWTC-2.1 and GWTC-3 catalogs. An extended search was also conducted for neutrino emission on longer timescales from neutron star containing mergers. Follow-up searches on the candidate optical counterpart of GW190521 were also conducted. We used two methods; an unbinned maximum likelihood analysis and a Bayesian analysis using astrophysical priors, both of which were previously used to search for high-energy neutrino emission from gravitational-wave events. No significant neutrino emission was observed by any analysis, and upper limits were placed on the time-integrated neutrino flux as well as the total isotropic equivalent energy emitted in high-energy neutrinos.
A Virtual 2D Tactile Array for Soft Actuators Using Acoustic Sensing

We create a virtual 2D tactile array for soft pneumatic actuators using embedded audio components. We detect contact-specific changes in sound modulation to infer tactile information. We evaluate different sound representations and learning methods to detect even small contact variations. We demonstrate the acoustic tactile sensor array by the example of a PneuFlex actuator and use a Braille display to individually control the contact of 29 x 4 pins with the actuator's 90 x 10 mm palmar surface. Evaluating the spatial resolution, the acoustic sensor localizes edges in x- and y-direction with a root-mean-square regression error of 1.67 mm and 0.0 mm, respectively. Even light contacts of a single Braille pin with a lifting force of 0.17 N are measured with high accuracy. Finally, we demonstrate the sensor's sensitivity to complex contact shapes by successfully reading the 26 letters of the Braille alphabet from a single display cell with a classification rate of 88 %.
Multi-task Learning for Monocular Depth and Defocus Estimations with Real Images

—Monocular depth estimation and defocus estimation are two fundamental tasks in computer vision. Most existing methods treat depth estimation and defocus estimation as two separate tasks, ignoring the strong connection between them. In this work, we propose a multi-task learning network consisting of an encoder with two decoders to estimate the depth and defocus map from a single focused image. Through the multi-task network, the depth estimation facilitates the defocus estimation to get better results in the weak texture region and the defocus estimation facilitates the depth estimation by the strong physical connection between the two maps. We set up a dataset (named ALL-in-3D dataset) which is the first all-real image dataset consisting of 100K sets of all-in-focus images, focused images with focus depth, depth maps, and defocus maps. It enables the network to learn features and solid physical connections between the depth and real defocus images. Experiments demonstrate that the network learns more solid features from the real focused images than the synthetic focused images. Benefiting from this multi-task structure where different tasks facilitate each other, our depth and defocus estimations achieve significantly better performance than other state-of-art algorithms. The code and dataset will be publicly available at https://github.com/cubhe/MDDNet. Transmission. His research interests include robotics, machine vision, the optical measurement and its application.
Coupling coordination between higher education and environmental governance: Evidence of western China

Exploring the coupling coordination mechanism between higher education and environmental governance is conducive to understanding the mutual interactions between them and thus enhancing mutual development. This study constructs the coupling coordination mechanism and the aggregated evaluation index system to measure the mutual coordination relations between them, assesses the performances and the coupling coordination degrees between them in the case of western provincial regions in China, and proposes countermeasures based on the prediction results. It is found that the performances of higher education are relatively more stable than those of environmental governance, and the distributions of the average performance grades exist noticeable geographical differences. Besides, the coupling coordination degrees increase with mild fluctuations with relatively small spatial variations, demonstrating the balanced and similar coordinated development status among regions; furthermore, in the future, the gaps in the coordination status among regions will gradually decline. This study highlights the mutual coordination mechanism between the two subsystems, compares the coupling coordination status among regions both temporally and spatially, and proposes specific, generalizable development suggestions that contribute to the academic sector, policymakers, ecology, and sustainability.
The influenza A virus genome packaging network — complex, flexible and yet unsolved

Abstract The genome of influenza A virus (IAV) consists of eight unique viral RNA segments. This genome organization allows genetic reassortment between co-infecting IAV strains, whereby new IAVs with altered genome segment compositions emerge. While it is known that reassortment events can create pandemic IAVs, it remains impossible to anticipate reassortment outcomes with pandemic prospects. Recent research indicates that reassortment is promoted by a viral genome packaging mechanism that delivers the eight genome segments as a supramolecular complex into the virus particle. This finding holds promise of predicting pandemic IAVs by understanding the intermolecular interactions governing this genome packaging mechanism. Here, we critically review the prevailing mechanistic model postulating that IAV genome packaging is orchestrated by a network of intersegmental RNA–RNA interactions. Although we find supporting evidence, including segment-specific packaging signals and experimentally proposed RNA–RNA interaction networks, this mechanistic model remains debatable due to a current shortage of functionally validated intersegmental RNA–RNA interactions. We speculate that identifying such functional intersegmental RNA–RNA contacts might be hampered by limitations of the utilized probing techniques and the inherent complexity of the genome packaging mechanism. Nevertheless, we anticipate that improved probing strategies combined with a mutagenesis-based validation could facilitate their discovery.
Evaluation of the active constituents of Nilavembu Kudineer for viral replication inhibition against SARS‐CoV‐2: An approach to targeting RNA‐dependent RNA polymerase (RdRp)

Abstract The World Health Organization has declared the novel coronavirus (COVID‐19) outbreak a global pandemic and emerging threat to people in the 21st century. SARS‐CoV‐2 constitutes RNA‐Dependent RNA Polymerase (RdRp) viral proteins, a critical target in the viral replication process. No FDA‐approved drug is currently available, and there is a high demand for therapeutic strategies against COVID‐19. In search of the anti‐COVID‐19 compound from traditional medicine, we evaluated the active moieties from Nilavembu Kudineer (NK), a poly‐herbal Siddha formulation recommended by AYUSH against COVID‐19. We conducted a preliminary docking analysis of 355 phytochemicals (retrieved from PubChem and IMPPAT databases) present in NK against RdRp viral protein (PDB ID: 7B3B) using COVID‐19 Docking Server and further with AutoDockTool‐1.5. 6. MD simulation studies confirmed that Orientin (L1), Vitexin (L2), and Kasuagamycin (L3) revealed better binding activity against RdRp (PDB ID: 7B3B) in comparison with Remdesivir. The study suggests a potential scaffold for developing drug candidates against COVID‐19. Practical applications Nilavembu Kudineer is a poly‐herbal Siddha formulation effective against various diseases like cough, fever, breathing problems, etc. This study shows that different phytoconstituents identified from Nilavembu Kudineer were subjected to in silico and ADME analyses. Out of the former 355 phytochemical molecules, Orientin (L1), Vitexin (L2), and Kasuagamycin (L3) showed better binding activity against RdRp viral protein (PDB ID: 7B3B) in comparison with the synthetic repurposed drug. Our work explores the search for an anti‐COVID‐19 compound from traditional medicine like Nilavembu Kudineer, which can be a potential scaffold for developing drug candidates against COVID‐19.