Miracle-dz/UniFAR-FTUs huggingface dataset:
Flag Polymatroids

We describe a natural geometric relationship between matroids and flag matroids by relating the geometry of the greedy algorithm to monotone path polytopes. This perspective allows us to generalize the construction of (underlying) flag matroids to polymatroids. We show that flag polymatroid polytopes are simple by proving that they are normally equivalent to certain nestohedra. For polymatroids realized by subspace arrangements, we show that flag polymatroid polytopes correspond to smooth toric varieties in flag varieties and we interpret our construction in terms of toric quotients. We give various examples that illustrate the rich combinatorial structure of flag polymatroids. Finally, we study general monotone paths on polymatroid polytopes, that relate to the enumeration of certain Young tableaux.
Evaluating the prevalence of spurious correlations in pulsar timing array datasets

Pulsar timing array collaborations have recently reported evidence for a noise process with a common spectrum among the millisecond pulsars in the arrays. The spectral properties of this common-noise process are consistent with expectations for an isotropic gravitational-wave background (GWB) from inspiralling supermassive black hole binaries. However, recent simulation analyses based on Parkes Pulsar Timing Array data indicate that such a detection may arise spuriously. In this paper, we use simulated pulsar timing array datasets to further test the robustness of the inference methods for spectral and spatial correlations from a GWB. Expanding on our previous results, we find strong support (Bayes factors exceeding 105) for the presence of a common-spectrum noise process in datasets where no common process is present, under a wide range of timing noise prescriptions per pulsar. We show that these results are highly sensitive to the choice of Bayesian priors on timing noise parameters, with priors that more closely match the injected distributions of timing noise parameters resulting in diminished support for a common-spectrum noise process. These results emphasize shortcomings in current methods for inferring the presence of a common-spectrum process, and imply that the detection of a common process is not a reliable precursor to detection of the GWB. Future searches for the nanohertz GWB should remain focused on detecting spatial correlations, and make use of more tailored specifications for a common-spectrum noise process.
SAVCHOI: Detecting Suspicious Activities using Dense Video Captioning with Human Object Interactions

Detecting suspicious activities in surveillance videos is a longstanding problem in real-time surveillance that leads to difficulties in detecting crimes. Hence, we propose a novel approach for detecting and summarizing suspicious activities in surveillance videos. We have also created ground truth summaries for the UCF-Crime video dataset. We modify a pre-existing approach for this task by leveraging the Human-Object Interaction (HOI) model for the Visual features in the Bi-Modal Transformer. Further, we validate our approach against the existing state-of-the-art algorithms for the Dense Video Captioning task for the ActivityNet Captions dataset. We observe that this formulation for Dense Captioning performs significantly better than other discussed BMT-based approaches for BLEU@1, BLEU@2, BLEU@3, BLEU@4, and METEOR. We further perform a comparative analysis of the dataset and the model to report the findings based on different NMS thresholds (searched using Genetic Algorithms). Here, our formulation outperforms all the models for BLEU@1, BLEU@2, BLEU@3, and most models for BLEU@4 and METEOR falling short of only ADV-INF Global by 25% and 0.5%, respectively.
How Criticality Meets Bifurcation in Compressive Failure of Disordered Solids

Continuum mechanics describes compressive failure as a standard bifurcation in the response of a material to an increasing load: damage, which initially grows uniformly in the material, localizes within a thin band at failure. Yet, experiments recording the acoustic activity preceding localization evidence power-law distributed failure precursors of increasing size, suggesting that compressive failure is a critical phenomenon. We examine here this apparent contradiction by probing the spatial organization of the damage activity and its evolution until localization during compression experiments of 2D cellular solids. The intermittent damage evolution measured in our experiments is adequately described by a non-stationary depinning equation derived from damage mechanics and reminiscent of critical phenomena. In this description, precursors are damage cascades emerging from the interplay between the material's disorder and the long-range stress redistributions following individual damage events. Yet, the divergence of their characteristic size close to failure, which we observe in our experiments, is not the signature of a transition towards criticality. Instead, the system remains at a fixed distance to the critical point at all stages of the damage evolution. The divergence results from the progressive loss of stability of the material as it is driven towards localization. Thus, our study shows that compressive failure is a standard bifurcation for which the material disorder plays a marginal role. It also shows that precursory activity constitute by-products of the evolution towards localization and can serve to build a predictive method to assess the residual lifetime of structures.
Bandwidth Parameterized by Cluster Vertex Deletion Number

Given a graph $G$ and an integer $b$, Bandwidth asks whether there exists a bijection $\pi$ from $V(G)$ to $\{1, \ldots, |V(G)|\}$ such that $\max_{\{u, v \} \in E(G)} | \pi(u) - \pi(v) | \leq b$. This is a classical NP-complete problem, known to remain NP-complete even on very restricted classes of graphs, such as trees of maximum degree 3 and caterpillars of hair length 3. In the realm of parameterized complexity, these results imply that the problem remains NP-hard on graphs of bounded pathwidth, while it is additionally known to be W[1]-hard when parameterized by the treedepth of the input graph. In contrast, the problem does become FPT when parameterized by the vertex cover number of the input graph. In this paper, we make progress towards the parameterized (in)tractability of Bandwidth. We first show that it is FPT when parameterized by the cluster vertex deletion number cvd plus the clique number $\omega$ of the input graph, thus generalizing the previously mentioned result for vertex cover. On the other hand, we show that Bandwidth is W[1]-hard when parameterized only by cvd. Our results generalize some of the previous results and narrow some of the complexity gaps.
A Compact Dual-Band Rectenna for GSM900 and GSM1800 Energy Harvesting

This paper presents a compact dual-band rectenna for GSM900 and GSM1800 energy harvesting. The monopole antenna consists of a longer bent Koch fractal element for GSM900 band and a shorter radiation element for GSM1800. The rectifier is composed of a multisection dual-band matching network, two rectifying branches, and filter networks. Measured peak efficiency of the proposed rectenna is 62% at 0.88 GHz 15.9 μW/cm2 and 50% at 1.85 GHz 19.1 μW/cm2, respectively. When the rectenna is 25 m away from a cellular base station, measurement result shows that the harvested power is able to power a batteryless LCD watch and achieve 1.275 V output voltage. The proposed rectenna is compact, efficient, low cost, and easy to fabricate, and it is suitable for RF energy harvesting and various wireless communication scenarios.
Improved Regularization of Event-based Learning by Reversing and Drifting

Event camera has an enormous potential in challenging scenes for its advantages of high temporal resolution, high dynamic range, low power consumption, and no motion blur. However, event-based learning is hindered by insufﬁcient generalization ability. In this paper, we ﬁrst analyze the inﬂuence of different brightness variations on event data. Then we propose two novel augmentation methods: EventReverse and EventDrift . By reversing and drifting events to their corresponding positions in the spatiotemporal or polarity domain, the proposed methods generate samples affected by different brightness variations, which improves the robustness of event-based learning and results in a better generalization. Extensive experiments on N-CARS, N-Caltech101 and CIFAR10-DVS datasets demonstrate that our method is general and remarkably effective.
Nível de instrucción, socioeconómico y evaluación de algunas dimensiones de la calidad de vida de octogenarios

Objetivos: describir el perfil de una muestra de octogenarios (n=80) atendidos en la red municipal desalud de una ciudad del interior del Estado de Sao Paulo, Brasil; evaluar su percepcion sobre las dimensionesde la calidad de vida (CV); e identificar correlaciones entre el status socioeconomico, nivel de instruccion y CV.Se trata de un estudio exploratorio descriptivo de aproximacion cualitativa de los datos. Los resultados revelaronque esta poblacion es predominantemente femenina, viuda, analfabeta, sedentaria y pobre, que le faltanservicios de salud y oportunidades de ocio, admitiendo la religion como el mayor apoyo. El status socioeconomicono interfirio en la percepcion de la CV. Sin embargo, observamos tendencia de que mayores niveles deinstruccion y participacion en actividades fisicas resultan en mayor satisfaccion.DESCRIPTORES: anciano; envejecimiento; salud del anciano; calidad de vida; pobreza
Effective Overloaded Pilot Assignment with Pilot Decontamination for Cell-Free Systems

—The pilot contamination in cell-free massive multiple-input-multiple-output (CF-mMIMO) must be addressed for accommodating a large number of users. We have investigated a decontamination method called subspace projection (SP). The SP separates interference from co-pilot users by using the orthog-onality of subspaces of each users’ principal components. The SP based decontamination has a potential to further improve spectral efﬁciency (SE), which is limited by a non-overloaded pilot assignment (PA) rule where each radio unit (RU) does not assign the same pilot to different users. Motivated by this limitation, this paper introduces semi-overloaded and overloaded PA methods adjusted for the decontamination in order to improve the sum SE of CF systems. Numerical simulations show that the overloaded and semi-overloaded PA give higher SE than that of non-overloaded PA at a high user density scenario.
Sparse-Based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolution is important for the quantification and analysis of retinal vasculature. However, the resolution of OCTA images is inversely proportional to the field of view at the same sampling frequency, which is not conducive to clinicians for analyzing larger vascular areas. In this paper, we propose a novel <bold>S</bold>parse-based domain <bold>A</bold>daptation <bold>S</bold>uper-<bold>R</bold>esolution network (SASR) for the reconstruction of realistic <inline-formula><tex-math notation="LaTeX">$6\times \text{6}{\rm{mm}}^{2}$</tex-math></inline-formula>/low-resolution (LR) OCTA images to high-resolution (HR) representations. To be more specific, we first perform a simple degradation of the <inline-formula><tex-math notation="LaTeX">$3\times \text{3}\,{\rm{mm}}^{2}$</tex-math></inline-formula>/high-resolution (HR) image to obtain the synthetic LR image. An efficient registration method is then employed to register the synthetic LR with its corresponding <inline-formula><tex-math notation="LaTeX">$3\times \text{3}\,{\rm{mm}}^{2}$</tex-math></inline-formula> image region within the <inline-formula><tex-math notation="LaTeX">$6\times \text{6}\,{\rm{mm}}^{2}$</tex-math></inline-formula> image to obtain the cropped realistic LR image. We then propose a multi-level super-resolution model for the fully-supervised reconstruction of the synthetic data, guiding the reconstruction of the realistic LR images through a generative-adversarial strategy that allows the synthetic and realistic LR images to be unified in the feature domain. Finally, a novel sparse edge-aware loss is designed to dynamically optimize the vessel edge structure. Extensive experiments on two OCTA sets have shown that our method performs better than state-of-the-art super-resolution reconstruction methods. In addition, we have investigated the performance of the reconstruction results on retina structure segmentations, which further validate the effectiveness of our approach.
Hybrid Classifiers for Spatio-temporal Real-time Abnormal Behaviors Detection, Tracking, and Recognition in Massive Hajj Crowds

Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, large-number abnormal behavior, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormal behaviors. In this paper, our contribution is twofold. First, we introduce an annotated and labeled large-scale crowd abnormal behaviors Hajj dataset (HAJJv2). Second, we propose two methods of hybrid Convolutional Neural Networks (CNNs) and Random Forests (RFs) to detect and recognize Spatio-temporal abnormal behaviors in small and large-scales crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individuals detection method based on the magnitudes and orientations of Horn-Schunck optical flow is used to locate and track individuals with abnormal behaviors. A Kalman filter is employed in large-scale crowd videos to predict and track the detected individuals in the subsequent frames. Then, means, variances, and standard deviations statistical features are computed and fed to the RF to classify individuals with abnormal behaviors in the temporal domain. In large-scale crowds, we fine-tune the ResNet-50 model using YOLOv2 object detection technique to detect individuals with abnormal behaviors in the spatial domain.
Anomalous relocation of topological states

I show that a single embedded non-Hermitian defect in a one-dimensional topological system at certain degrees of non-Hermiticity can remove the topological mode from the edge and restore it inside the lattice at the same place where the non-Hermitian defect is placed. I relate this unexpected phenomenon to the wave matching condition and continuity of the wave function at different sites in the lattice. These findings pave the way for controlling the position of topological states at will.
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo

Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural inverse rendering method for MVPS based on implicit representation. Given multi-view images of a non-Lambertian object illuminated by multiple unknown directional lights, our method jointly estimates the geometry, materials, and lights. Our method first employs multi-light images to estimate per-view surface normal maps, which are used to regularize the normals derived from the neural radiance field. It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer. After optimization, the reconstructed object can be used for novel-view rendering, relighting, and material editing. Experiments on both synthetic and real datasets demonstrate that our method achieves far more accurate shape reconstruction than existing MVPS and neural rendering methods. Our code and model can be found at https://ywq.github. io/psnerf.
Towards Open Set 3D Learning: A Benchmark on Object Point Clouds

In the last years, there has been signiﬁcant progress in the ﬁeld of 3D learning on classiﬁcation, detection and segmentation problems. The vast majority of the existing studies focus on canonical closed-set conditions, neglecting the intrinsic open nature of the real-world. This limits the abilities of autonomous systems involved in safety-critical applications that require managing novel and unknown signals. In this context exploiting 3D data can be a valuable asset since it conveys rich information about the geometry of sensed objects and scenes. This paper provides the ﬁrst broad study on Open Set 3D learning. We introduce a novel testbed with settings of increasing difﬁculty in terms of category semantic shift and cover both in-domain (synthetic-to-synthetic) and cross-domain (synthetic-to-real) scenarios. Moreover, we investigate the related out-of-distribution and Open Set 2D literature to understand if and how their most recent approaches are effective on 3D data. Our extensive benchmark positions several algorithms in the same coherent picture, revealing their strengths and limitations. The results of our analysis may serve as a reliable foothold for future tailored Open Set 3D models.
Analytical synthesis of elliptic voltage-mode even/odd-nth-order filter structures using DDCCs, FDCCIIs, and grounded capacitors and resistors

Despite the recent publication of the analytical synthesis of voltage-mode even/odd- n th-order differential difference current conveyor (DDCC) and fully differential current conveyor II (FDCCII)-grounded resistor and capacitor universal Butterworth/Chebyshev filter structures, an elliptic voltage-mode even/odd- n th-order DDCC and FDCCII-based filter structure is yet to be presented in the literature. Under the restriction of a finite order, the elliptic filter is better at meeting the stringent cut-off rate of a very narrow transition band as compared to other kinds of filters, thus making the synthesis of such an elliptic filter to be extremely useful. In this study, one even- n th-order and two odd- n th-order elliptic filter structures are analytically synthesised using DDCCs and FDCCIIs. The feasibility of such structures is validated through H-spice simulations on the proposed elliptic third-order low-pass and elliptic fourth-order low-pass, high-pass, band-pass, and band-reject filters.
Zonotopal Algebras, Orbit Harmonics, and Donaldson–Thomas Invariants of Symmetric Quivers

We apply the method of orbit harmonics to the set of break divisors and orientable divisors on graphs to obtain the central and external zonotopal algebras, respectively. We then relate a construction of Efimov in the context of cohomological Hall algebras to the central zonotopal algebra of a graph $G_{Q,\gamma }$ constructed from a symmetric quiver $Q$ with enough loops and a dimension vector $\gamma $. This provides a concrete combinatorial perspective on the former work, allowing us to identify the quantum Donaldson–Thomas (DT) invariants as the Hilbert series of the space of $S_{\gamma }$-invariants of the Postnikov–Shapiro slim subgraph space attached to $G_{Q,\gamma }$. The connection with orbit harmonics in turn allows us to give a manifestly nonnegative combinatorial interpretation to numerical DT invariants as the number of $S_{\gamma }$-orbits under the permutation action on the set of break divisors on $G$. We conclude with several representation-theoretic consequences, whose combinatorial ramifications may be of independent interest.
Investigation of gear pitting defect using vibration characteristics in a single-stage gearbox

Gearboxes are the important components in process industries such as cement, food processing and rubber. Pitting is the removal of the material in certain part of gear by the application of repeated stresses. Tooth pitting and surface failure occur due to excessive stress conditions. When a number of pits join together, a larger pit is formed, referred as a spall, which may lead to tooth breakage. The early detection and diagnosis of excessive wear tooth pitting and spalling in gear transmission systems are becoming significant concerns in high-speed, large power applications. The early detection of pitting in gears is important from condition monitoring point of view, which is still to be addressed by an experimental approach. In this study, vibration characteristics of a single-stage spur gearbox with pitting as seeded faults are investigated.
Large-Scale Deep Learning for Multi-Jet Event Classification

We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level information as images considering the geometry of the CMS detector and use this model to discriminate \textit{R}-parity violating super symmetry (RPV SUSY) events from the background events with inelastic quantum process from the Standard Model (QCD multi-jet). We compare the classification performance of the CNN method with that of the widely used cut-based method. The signal efficiency (and expected significance) of the CNN method is 1.85 (1.2) times higher than that of the cut-based method. To speed-up the training, the model training is conducted using the Nurion HPC system at the Korea Institute of Science and Technology Information, which is equipped with thousands of parallel \texttt{Xeon Phi} CPUs. Notably, our CNN model shows scalability up to 1024 nodes.
Contrastive Learning for Interactive Recommendation in Fashion

Recommender systems and search are both indispensable in fa-cilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate independently, failing to combine the strengths of recommender systems to accu-rately capture user tastes with search systems’ ability to process user queries. We propose a novel remedy to this problem by au-tomatically recommending personalized fashion items based on a user-provided text request. Our proposed model, WhisperLite, uses contrastive learning to capture user intent from natural language text and improves the recommendation quality of fashion products. WhisperLite combines the strength of CLIP embeddings with additional neural network layers for personalization, and is trained using a composite loss function based on binary cross entropy and contrastive loss. The model demonstrates a significant improve-ment in offline recommendation retrieval metrics when tested on a real-world dataset collected from an online retail fashion store, as well as widely used open-source datasets in different e-commerce domains, such as restaurants, movies and TV shows, clothing and shoe reviews. We additionally conduct a user study that captures user judgements on the relevance of the model’s recommended items, confirming the relevancy of WhisperLite’s recommendations in an online setting.