Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction

When autonomous vehicles still struggle to solve challenging situations during on-road driving, humans have long mastered the essence of driving with efficient transferable and adaptable driving capability. By mimicking humans' cognition model and semantic understanding during driving, we present HATN, a hierarchical framework to generate high-quality driving behaviors in multi-agent dense-traffic environments. Our method hierarchically consists of a high-level intention identification and low-level action generation policy. With the semantic sub-task definition and generic state representation, the hierarchical framework is transferable across different driving scenarios. Besides, our model is also able to capture variations of driving behaviors among individuals and scenarios by an online adaptation module. We demonstrate our algorithms in the task of trajectory prediction for real traffic data at intersections and roundabouts, where we conducted extensive studies of the proposed method and demonstrated how our method outperformed other methods in terms of prediction accuracy and transferability.
An algorithm taking Kirby diagrams to trisection diagrams

We present an algorithm taking a Kirby diagram of a closed oriented $4$-manifold to a trisection diagram of the same manifold. This algorithm provides us with a large number of examples for trisection diagrams of closed oriented $4$-manifolds since many Kirby-diagrammatic descriptions of closed oriented $4$-manifolds are known. That being said, the algorithm does not necessarily provide particularly efficient trisection diagrams. We also extend this algorithm to work for the non-orientable case.
Fault-Tolerant Performance Enhancement of DC-DC Converters with High-Speed Fault Clearing-unit based Redundant Power Switch Configurations

Fault detection and reconfiguration in fault-tolerant converters may complicated and necessitate using all-purpose microprocessors and high-speed sensors to guarantee the satisfactory performance of power converters. Therefore, providing fault-clearing feature without increasing the processing and sensing burdens and reducing the transition time between faulty to normal state are of great importance. This research proposes a new redundant-switch configuration to address the mentioned challenges. The proposed configuration uses one diode and two fuses to eliminate the faulty switch and replace the reserve one, spontaneously. Open-circuit fault in the proposed configuration is clarified instantly. Moreover, the short-circuit fault is dealt as an open-circuit fault by using a fuse. Thus, the fault-tolerant feature of the proposed switch configuration is achieved without using a complex, versatile and multifaceted fault managing unit. Resultant behaviors of the case studies are derived using MATLAB/SIMULINK. Also, steady-state thermal distribution of power switches which are implemented on a monolith heat sink, analyzed in COMSOL Multi-physics environment. Finally, the viability of the proposed configuration is demonstrated by a laboratory-scaled prototype.
von MISES TAPERING: A NEW CIRCULAR WINDOWING

Discrete and continuous standard windowing are revisited and a a new taper is introduced, which is derived from the normal circular distribution by von Mises. Both the continuous-time and the discrete-time windows are considered, and their spectra obtained. A brief comparison with further classical window families is performed in terms of their properties in the spectral domain. These windows can be used in spectral analysis, and in particular, in the design of FIR (finite impulse response) filters as an alternative to the Kaiser window.
TriVoC: Efficient Voting-Based Consensus Maximization for Robust Point Cloud Registration With Extreme Outlier Ratios

Correspondence-based point cloud registration is a cornerstone in robotics perception and computer vision, which seeks to estimate the best rigid transformation aligning two point clouds from the putative correspondences. However, due to the limited robustness of 3D keypoint matching approaches, outliers, probably in large numbers, are prone to exist among the correspondences, which makes robust registration methods imperative. Unfortunately, existing robust methods have their own limitations (e.g. high computational cost or limited robustness) when facing high or extreme outlier ratios, probably unsuitable for practical use. In this letter, we present a novel, fast, deterministic and guaranteed robust solver, named TriVoC (Triple-layered Voting with Consensus maximization), for the robust registration problem. We decompose the selecting of the minimal 3-point sets into 3 consecutive layers, and in each layer we design an efficient voting and correspondence sorting framework on the basis of the pairwise equal-length constraint. In this manner, the 3-point sets can be selected independently from the reduced correspondence sets according to the sorted sequence, which can significantly lower the computational cost and meanwhile provide a strong guarantee to achieve the largest consensus set (as the final inlier set) as long as a probabilistic termination condition is fulfilled. Varied experiments show that our solver TriVoC is robust against up to 99% outliers, highly accurate, time-efficient even with extreme outlier ratios, and also practical for real-world applications, showing performance superior to other state-of-the-art competitors.
Tracing the Ionization Structure of the Shocked Filaments of NGC 6240

We study the ionization and excitation structure of the interstellar medium in the late-stage gas-rich galaxy merger NGC 6240 using a suite of emission-line maps at ∼25 pc resolution from the Hubble Space Telescope, Keck/NIRC2 with Adaptive Optics, and the Atacama Large Millimeter/submillimeter Array (ALMA). NGC 6240 hosts a superwind driven by intense star formation and/or one or both of two active nuclei; the outflows produce bubbles and filaments seen in shock tracers from warm molecular gas (H2 2.12 μm) to optical ionized gas ([O iii], [N ii], [S ii], and [O i]) and hot plasma (Fe XXV). In the most distinct bubble, we see a clear shock front traced by high [O iii]/Hβ and [O iii]/[O i]. Cool molecular gas (CO(2−1)) is only present near the base of the bubble, toward the nuclei launching the outflow. We interpret the lack of molecular gas outside the bubble to mean that the shock front is not responsible for dissociating molecular gas, and conclude that the molecular clouds are partly shielded and either entrained briefly in the outflow, or left undisturbed while the hot wind flows around them. Elsewhere in the galaxy, shock-excited H2 extends at least ∼4 kpc from the nuclei, tracing molecular gas even warmer than that between the nuclei, where the two galaxies’ interstellar media are colliding. A ridgeline of high [O iii]/Hβ emission along the eastern arm aligns with the southern nucleus’ stellar disk minor axis; optical integral field spectroscopy from WiFeS suggests this highly ionized gas is centered at systemic velocity and likely photoionized by direct line of sight to the southern active galactic nucleus.
Piezoelectric Soft Robot Inchworm Motion by Controlling Ground Friction through Robot Shape

—Electrically driven soft robots enable small and light bodies, as well as environmental compatibility, various locomotion, and safe operation. In particular, electrostatic actuators (for example, piezoelectric actuators) are fast responded. However, scalable ways for seamless integration and untethered operation remain unclear. In addition, soft body nature modeling, including environmental interactions, is a long-standing challenge. Furthermore, more locomotion mechanisms need to be explored. In this paper, we have designed, modeled, and demonstrated a soft robot that, for the ﬁrst time, starts to address all these questions. It has a linear array of ﬁve actuators in a planar structure, opening doors for integration and free operation. A new inchworm-inspired crawling motion mechanism was designed and validated by relying on posture self-adjustment. The ﬁrst analytical soft body model including piezoelectricity, gravity, and ground interactions that well explain robot locomotion was developed and validated by experiments. We demonstrated the robot’s forward and backward motion and explored the effects of payload and driving speed: 1207 µm movement per cycle and up to 200 g payload (16x body weight) can be carried while moving. This work paves the way for fast-responding robots in complicated unknown environments.
Introspective Distillation for Robust Question Answering

Question answering (QA) models are well-known to exploit data bias, e.g., the language prior in visual QA and the position bias in reading comprehension. Recent debiasing methods achieve good out-of-distribution (OOD) generalizability with a considerable sacrifice of the in-distribution (ID) performance. Therefore, they are only applicable in domains where the test distribution is known in advance. In this paper, we present a novel debiasing method called Introspective Distillation (IntroD) to make the best of both worlds for QA. Our key technical contribution is to blend the inductive bias of OOD and ID by introspecting whether a training sample fits in the factual ID world or the counterfactual OOD one. Experiments on visual QA datasets VQA v2, VQA-CP, and reading comprehension dataset SQuAD demonstrate that our proposed IntroD maintains the competitive OOD performance compared to other debiasing methods, while sacrificing little or even achieving better ID performance compared to the non-debiasing ones.
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation

Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks. Due to considerable heterogeneity in shapes, colours and textures, demarcating object boundaries is particularly difficult in biomedical images, resulting in significant inter and intra-rater variability. Approaches, such as soft labelling and distance penalty term, apply a global transformation to the ground truth, redefining the loss function with respect to uncertainty. However, global operations are computationally expensive, and neither approach accurately reflects the uncertainty underlying manual annotation. In this paper, we propose the Boundary Uncertainty, which uses morphological operations to restrict soft labelling to object boundaries, providing an appropriate representation of uncertainty in ground truth labels, and may be adapted to enable robust model training where systematic manual segmentation errors are present. We incorporate Boundary Uncertainty with the Dice loss, achieving consistently improved performance across three well-validated biomedical imaging datasets compared to soft labelling and distance-weighted penalty. Boundary Uncertainty not only more accurately reflects the segmentation process, but it is also efficient, robust to segmentation errors and exhibits better generalisation.
Atomistic study of GaSe/Ge(111) interface formed through van der Waals epitaxy

Layered materials can be grown on various substrates through van der Waals epitaxy (vdWE) regardless of lattice mismatch. The atomistic study of the film‐substrate interface in vdWE is becoming increasingly important due to their expected applications as two‐dimensional (2D) materials. In this contribution, we have grown GaSe thin films on Ge(111) substrates by molecular beam epitaxy and studied the GaSe/Ge(111) interface using high‐angle annular dark‐field scanning transmission electron microscopy (HAADF‐STEM). Cross‐sectional HAADF‐STEM observations revealed that the grown layers adopt predominantly the expected wurtzite‐like structure and stacking, but layers with zinc‐blende‐like structure, similar to Ga2Se3 but apparently different, and other layer stacking sequences, exist locally near the film‐substrate interface. These results demonstrate that even in vdWE, structural changes can occur in the grown layers adjacent to the substrate, highlighting the importance of such interface for synthesizing and applying ultimately thin 2D materials.
A Dynamic Programming Formulation for the Nonlinear Filter

This paper build on our recent work where we presented a dual stochastic optimal control formulation of the nonlinear filtering problem [1]. The constraint for the dual problem is a backward stochastic differential equations (BSDE). The solution is obtained via an application of the maximum principle (MP). In the present paper, a dynamic programming (DP) principle is presented for a special class of BSDE-constrained stochastic optimal control problems. The principle is applied to derive the solution of the nonlinear filtering problem.
Longitudinal Analysis of Mask and No-Mask on Child Face Recognition

Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. To constrain the pandemic, people have started using face mask. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems. However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on face recognition systems. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-the-shelf (COTS) system under child cross-age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended Indian Child Longitudinal Face Dataset) that contains 26,258 face images of 7,473 subjects in the age group of [2, 18] over an average time span of 3.35 years. Due to the combined effects of face mask and face aging, the FaceNet, PFE, ArcFace, and COTS face verification system accuracies decrease by approximately , , , and , respectively.
Love tHy Neighbour: Remeasuring Local Structural Node Similarity in Hypergraph-Derived Networks

The problem of node-similarity in networks has motivated a plethora of such measures between node-pairs, which make use of the underlying graph structure. However, higher-order relations cannot be losslessly captured by mere graphs and hence, extensions thereof viz. hypergraphs are used instead. Measuring proximity between node pairs in such a setting calls for a revision in the topological measures of similarity, lest the hypergraph structure remains under-exploited. We, in this work, propose a multitude of hypergraph-oriented similarity scores between node-pairs, thereby providing novel solutions to the link prediction problem. As a part of our proposition, we provide theoretical formulations to extend graph-topology based scores to hypergraphs. We compare our scores with graph-based scores (over clique-expansions of hypergraphs into graphs) from the state-of-the-art. Using a combination of the existing graph-based and the proposed hypergraph-based similarity scores as features for a classifier predicts links much better than using the former solely. Experiments on several real-world datasets and both quantitative as well as qualitative analyses on the same exhibit the superiority of the proposed similarity scores over the existing ones.
PiDRAM: A Holistic End-to-end FPGA-based Framework for Processing-in-DRAM

Commodity DRAM-based processing-using-memory (PuM) techniques that are supported by off-the-shelf DRAM chips present an opportunity for alleviating the data movement bottleneck at low cost. However, system integration of these techniques imposes non-trivial challenges that are yet to be solved. Potential solutions to the integration challenges require appropriate tools to develop any necessary hardware and software components. Unfortunately, current proprietary computing systems, specialized DRAM-testing platforms, or system simulators do not provide the flexibility and/or the holistic system view that is necessary to properly evaluate and deal with the integration challenges of commodity DRAM-based PuM techniques. We design and develop Processing-in-DRAM (PiDRAM), the first flexible end-to-end framework that enables system integration studies and evaluation of real, commodity DRAM-based PuM techniques. PiDRAM provides software and hardware components to rapidly integrate PuM techniques across the whole system software and hardware stack. We implement PiDRAM on an FPGA-based RISC-V system. To demonstrate the flexibility and ease of use of PiDRAM, we implement and evaluate two state-of-the-art commodity DRAM-based PuM techniques: (i) in-DRAM copy and initialization (RowClone) and (ii) in-DRAM true random number generation (D-RaNGe). We describe how we solve key integration challenges to make such techniques work and be effective on a real-system prototype, including memory allocation, alignment, and coherence. We observe that end-to-end RowClone speeds up bulk copy and initialization operations by 14.6× and 12.6×, respectively, over conventional CPU copy, even when coherence is supported with inefficient cache flush operations. Over PiDRAM’s extensible codebase, integrating both RowClone and D-RaNGe end-to-end on a real RISC-V system prototype takes only 388 lines of Verilog code and 643 lines of C++ code.
Render In-between: Motion Guided Video Synthesis for Action Interpolation

Upsampling videos of human activity is an interesting yet challenging task with many potential applications ranging from gaming to entertainment and sports broadcasting. The main difficulty in synthesizing video frames in this setting stems from the highly complex and non-linear nature of human motion and the complex appearance and texture of the body. We propose to address these issues in a motion-guided frame-upsampling framework that is capable of producing realistic human motion and appearance. A novel motion model is trained to inference the non-linear skeletal motion between frames by leveraging a large-scale motion-capture dataset (AMASS). The high-frame-rate pose predictions are then used by a neural rendering pipeline to produce the full-frame output, taking the pose and background consistency into consideration. Our pipeline only requires low-frame-rate videos and unpaired human motion data but does not require high-frame-rate videos for training. Furthermore, we contribute the first evaluation dataset that consists of high-quality and high-frame-rate videos of human activities for this task. Compared with state-of-the-art video interpolation techniques, our method produces in-between frames with better quality and accuracy, which is evident by state-of-the-art results on pixel-level, distributional metrics and comparative user evaluations. Our code and the collected dataset are available at https://git.io/Render-In-Between.
Periodic Fast Multipole Method

A new scheme is presented for imposing periodic boundary conditions on unit cells with arbitrary source distributions. We restrict our attention here to the Poisson, modified Helmholtz, Stokes and modified Stokes equations. The approach extends to the oscillatory equations of mathematical physics, including the Helmholtz and Maxwell equations, but we will address these in a companion paper, since the nature of the problem is somewhat different and includes the consideration of quasiperiodic boundary conditions and resonances. Unlike lattice sum-based methods, the scheme is insensitive to the unit cell's aspect ratio and is easily coupled to adaptive fast multipole methods (FMMs). Our analysis relies on classical"plane-wave"representations of the fundamental solution, and yields an explicit low-rank representation of the field due to all image sources beyond the first layer of neighboring unit cells. When the aspect ratio of the unit cell is large, our scheme can be coupled with the nonuniform fast Fourier transform (NUFFT) to accelerate the evaluation of the induced field. Its performance is illustrated with several numerial examples.
Quality Estimation Using Round-trip Translation with Sentence Embeddings

Estimating the quality of machine translation systems has been an ongoing challenge for researchers in this field. Many previous attempts at using round-trip translation as a measure of quality have failed, and there is much disagreement as to whether it can be a viable method of quality estimation. In this paper, we revisit round-trip translation, proposing a system which aims to solve the previous pitfalls found with the approach. Our method makes use of recent advances in language representation learning to more accurately gauge the similarity between the original and round-trip translated sentences. Experiments show that while our approach does not reach the performance of current state of the art methods, it may still be an effective approach for some language pairs.
Directed mixed membership stochastic blockmodel

Mixed membership problem for undirected network has been well studied in network analysis recent years. However, the more general case of mixed membership for directed network in which nodes can belong to multiple communities remains a challenge. Here, we propose an interpretable and identiﬁable model: directed mixed membership stochastic blockmodel (DiMMSB) for directed mixed membership networks. DiMMSB allows that row nodes and column nodes of the adjacency matrix can be diﬀerent and these nodes may have distinct community structure in a directed network. We also develop an eﬃcient spectral algorithm called DiSP designed based on simplex structures inherent in the left and right singular vectors of the population adjacency matrix to estimate the mixed memberships for both row nodes and column nodes in a directed network. We show that DiSP is asymptotically consistent under mild conditions by providing error bounds for the inferred membership vectors of each row node and each column node using delicate spectral analysis. Numerical results on computer-generated directed mixed membership networks support our theoretical ﬁndings and show that our DiSP outperforms its competitor in both error rates and run-time. Applications of DiSP to real-world directed networks demonstrate the advantages of DiSP in studying the asymmetric structure of directed networks.