Miracle-dz/UniFAR-FTUs huggingface dataset:
GAN-CNMP: An Interactive Generative Drawing Tool

Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, GAN-CNMP, that incorporates a novel adversarial loss on CNMP to increase sketch smoothness and consistency. Through the experiments, we show that our model can be trained with few unlabeled samples, can construct distributions automatically in the latent space, and produces better results than the base model in terms of shape consistency and smoothness.
Route planning of automated guided vehicles

A few basic types of transportation functions of the automated guided vehicles are identified. Frameworks and issues related to the routing problems are then presented. The suitability of several generic topologies to each function is evaluated under the framework. It is believed that the precursory work can be a basis for future research and development in this area.
VLA Observations of the AE Aqr-type Cataclysmic Variable LAMOST J024048.51+195226.9

AE Aqr was until recently the only known magnetic cataclysmic variable (MCV) containing a rapidly spinning (33.08 s) white dwarf (WD). Its radio emission is believed to be a superposition of synchrotron-emitting plasmoids, because it has a positive spectral index spanning three orders of magnitude (≈2–2000 GHz) and is unpolarized. Both characteristics are unusual for MCVs. Recently, Thorstensen has suggested that the cataclysmic variable LAMOST J024048.51+195226.9 (henceforth, J0240+19) is a twin of AE Aqr based on its optical spectra. Optical photometry shows the star to be a high-inclination eclipsing binary with a spin period of 24.93 s, making it the fastest spinning WD. This paper presents three hours of Very Large Array radio observations of J0240+19. These observations show that the persistent radio emission from J0240+19 is dissimilar to that of AE Aqr in that it shows high circular polarization and a negative spectral index. The emission is most similar to that from the nova-like CV V603 Aql. We argue that the radio emission is caused by a superposition of plasmoids emitting plasma radiation or electron cyclotron maser emission from the lower corona of the donor star and not from the magnetosphere near the WD, because the latter site is expected to be modulated at the orbital period of the binary and to show eclipses—of which there is no evidence. The radio source J0240+19, although weak (≲ 1 mJy), is a persistent source in a high-inclination eclipsing binary, making it a good laboratory for studying radio emission from CVs.
Model-Independent Error Mitigation in Parametric Quantum Circuits and Depolarizing Projection of Quantum Noise

Xiaoyang Wang,a,∗ Xu Feng, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, f Georgios Polykratis f ,g and Paolo Stornati School of Physics and Center of High Energy Physics, Peking University, Beijing 100871, China Center for Theoretical Physics, Co-Design Center for Quantum Advantage, and NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, ON N2L 2Y5, Canada Department of Mathematics, King’s College London, Strand, London WC2R 2LS, United Kingdom Deutsches Elektronen-Synchrotron DESY, Platanenallee 6, 15738 Zeuthen, Germany f Computation-Based Science and Technology Research Center, The Cyprus Institute, 20 Kavafi Street, 2121 Nicosia, Cyprus Department of Physics, University of Cyprus, P.O. Box 20537, 1678 Nicosia, Cyprus ICFO, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss 3, 08860 Castelldefels (Barcelona), Spain E-mail: zzwxy@pku.edu. cn, xu.feng@pku.edu. cn, lfuncke@mit.edu,
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction

This paper presents a novel attention-based neural net-workfor structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approach detects corners and classifies edge candidates between corners in an end-to-end manner. Our contribution is a holistic edge clas-sification architecture, which 1) initializes the feature of an edge candidate by a trigonometric positional encoding of its end-points; 2) fuses image feature to each edge candidate by deformable attention; 3) employs two weight-sharing Trans-former decoders to learn holistic structural patterns over the graph edge candidates; and 4) is trained with a masked learning strategy. The corner detector is a variant of the edge classification architecture, adapted to operate on pixels as corner candidates. We conduct experiments on two structured reconstruction tasks: outdoor building architecture and indoor fioorplan planar graph reconstruction. Exten-sive qualitative and quantitative evaluations demonstrate the superiority of our approach over the state of the art. Code and pre-trained models are available at https://heat-structured-reconstruction.github. io/
Donut: Document Understanding Transformer without OCR

Understanding document images (e.g., invoices) has been an important research topic and has many applications in document processing automation. Through the latest advances in deep learning-based Optical Character Recognition (OCR), current Visual Document Understanding (VDU) systems have come to be designed based on OCR. Although such OCR-based approach promise reasonable performance, they suffer from critical problems induced by the OCR, e.g. , (1) expensive computational costs and (2) performance degradation due to the OCR error propagation. In this paper, we propose a novel VDU model that is end-to-end trainable without underpinning OCR framework. To this end, we propose a new task and a synthetic document image generator to pre-train the model to mitigate the dependencies on large-scale real document images. Our approach achieves state-of-the-art performance on various document understanding tasks in public benchmark datasets and private industrial service datasets. Through extensive experiments and analysis, we demonstrate the effectiveness of the proposed model especially with consideration for a real-world application.
Light Curve Analysis and Period Study of Two Eclipsing Binaries UZ Lyr and BR Cyg

Two eclipsing binary systems, UZ Lyr and BR Cyg, are semi-detached types whose secondary component fills its Roche lobe. Although radial velocity and light curves of these systems have already been investigated separately, both their radial velocity and light curves are analyzed simultaneously for the first time in the present study. Also, the orbital period changes of these systems are studied. Our results show that the mass transfers between components have negligible effects on the orbital period changes of these systems, but two light travel time effects are the reasons for the periodic behavior of the O − C curve for UZ Lyr. We could not remark more information about orbital period changes for BR Cyg, but we find a new orbital period for it. By radial velocity and light curve analysis, we find a cold spot on the secondary component of BR Cyg. The new geometrical and physical parameters of both systems are obtained and their positions on the H-R diagram are demonstrated.
A novel data-driven algorithm to predict anomalous prescription based on patient's feature set

Appropriate dosing of radiation is crucial to patient safety in radiotherapy. Current quality assurance depends heavily on a peer-review process, where the physicians' peer review on each patient's treatment plan, including dose and fractionation. However, such a process is manual and laborious. Physicians may not identify errors due to time constraints and caseload. We designed a novel prescription anomaly detection algorithm that utilizes historical data to predict anomalous cases. Such a tool can serve as an electronic peer who will assist the peer-review process providing extra safety to the patients. In our primary model, we created two dissimilarity metrics, R and F. R defining how far a new patient's prescription is from historical prescriptions. F represents how far away a patient's feature set is from the group with an identical or similar prescription. We flag prescription if either metric is greater than specific optimized cut-off values. We used thoracic cancer patients (n=2356) as an example and extracted seven features. Here, we report our testing f1 score, between 75%-94% for different treatment technique groups. We also independently validate our results by conducting a mock peer review with three thoracic specialists. Our model has a lower type 2 error rate compared to manual peer-review physicians. Our model has many advantages over traditional machine learning algorithms, particularly in that it does not suffer from class imbalance. It can also explain why it flags each case and separate prescription and non-prescription-related features without learning from the data.
Synthetic weather radar using hybrid quantum-classical machine learning

The availability of high-resolution weather radar images underpins effective forecasting and decision-making. In regions beyond traditional radar coverage, generative models have emerged as an important synthetic capability, fusing more ubiquitous data sources, such as satellite imagery and numerical weather models, into accurate radar-like products. Here, we demonstrate methods to augment conventional convolutional neural networks with quantum-assisted models for generative tasks in global synthetic weather radar. We show that quantum kernels can, in principle, perform fundamentally more complex tasks than classical learning machines on the relevant underlying data. Our results establish synthetic weather radar as an effective heuristic benchmark for quantum computing capabilities and set the stage for detailed quantum advantage benchmarking on a high-impact operationally relevant problem.
Three-dimensional Gross-Neveu model with two flavors of staggered fermions

We introduce a strongly interacting lattice field theory model containing two flavors of massless staggered fermions with two kinds of interactions: (1) a lattice current-current interaction, and (2) an on-site four-fermion interaction. At weak couplings, we expect a massless fermion phase since our interactions become irrelevant at long distances. At strong couplings, based on previous studies, we argue that our lattice model contains two different massive fermion phases with different mechanisms of fermion mass generation. In one phase, fermions become massive through Spontaneous Symmetry Breaking (SSB) via the formation of a fermion bilinear condensate. In the other phase, fermion mass arises through a more exotic mechanism without the formation of any fermion bilinear condensate. Our lattice model is free of sign problems and can be studied using the fermion bag algorithm. The longer term goal here is to study both these mass generation phenomena in a single model and understand how different phases come together.
A new compressed cover tree guarantees a near linear parameterized complexity for all $k$-nearest neighbors search in metric spaces

This paper studies the classical problem of ﬁnding k nearest neighbors to m query points within a larger set of n reference points in an arbitrary metric space. The well-known work by Beygelzimer, Kakade and Langford in ICML 2006 introduced cover trees and claimed to guarantee a near linear worst-case complexity in the number n of reference points. Section 5.3 of Curtin’s PhD (2015) pointed out that the key step in the above work needs to update an estimate O (log n ) for a required number of iterations. This paper describes typical examples when past cover trees need O ( n ) iterations so that the overall worst-time complexity remains quadratic as for a brute-force search. Despite the same estimate O (log n ) being repeated in numerous subsequent papers, no further analysis was published. A new compressed cover tree is a much smaller structure that resolves the past obstacles for k -nearest neighbor search and guarantees a parameterized complexity that is near linear in the maximum size of both query and reference set for any k ≥ 1.
Organisational learning 'in-use'

In the exceptionally busy, fragmented, daily structure of a school it is difficult to implement improvement consistently, let alone one that is as difficult as learning the skills associated with organisational learning. In a 'Special' school (students with high or very high needs) the situation is even more intense due to the wide-ranging roles of staff and demanding nature of the students. This paper outlines the way that the senior leadership team in one Special school committed to shifting organisational learning from an 'espoused' to 'in-use' level. The goal of the development was to enhance collaboration, effective relationships, openness and trust. As practitioner researchers each team member engaged in an action research approach using evidence-based decision making to examine their own practice, learn and implement new approaches and then evaluate effectiveness of the improved practice. Ongoing feedback from participants suggests that the action research process offered the team high involvement and ownership of a systematic approach to developing and monitoring their attempt to improve relational effectiveness via organisational learning. The evaluation phase findings indicate that areas of improvement had occurred.
Submicron p-channel (Al,Ga)As/(In,Ga)As HIGFETs

Submicron p-channel (Al,Ga)As/(In,Ga)As HIGFETs have been optimized for application to high-performance complementary GaAs circuits. Major issues with submicron and deep submicron (L/sub g//spl les/0.5-/spl mu/m) P-channel HIGFETs have been the severe short-channel effects, such as high subthreshold leakage currents and high output conductances. With optimization of the p-type self-aligned implant schedule, control of impurity contamination at the substrate/buffer interfaces and increase of the resistivity of the unintentionally-doped GaAs buffers, high-performance submicron devices have been realized. Typically, 0.5-/spl mu/m P-HIGFETs yielded room temperature transconductances of 90 mS/mm, drain currents at V/sub gs/=V/sub ds/=-1.5 V of 63 mA/mm, and subthreshold leakage currents near 1 nA. Subthreshold slope of 90 mV/decade and output conductances under 5 mS/mm were realized.
Trust the Critics: Generatorless and Multipurpose WGANs with Initial Convergence Guarantees

Inspired by ideas from optimal transport theory we present Trust the Critics (TTC), a new algorithm for generative modelling. This algorithm eliminates the trainable generator from a Wasserstein GAN; instead, it iteratively modifies the source data using gradient descent on a sequence of trained critic networks. This is motivated in part by the misalignment which we observed between the optimal transport directions provided by the gradients of the critic and the directions in which data points actually move when parametrized by a trainable generator. Previous work has arrived at similar ideas from different viewpoints, but our basis in optimal transport theory motivates the choice of an adaptive step size which greatly accelerates convergence compared to a constant step size. Using this step size rule, we prove an initial geometric convergence rate in the case of source distributions with densities. These convergence rates cease to apply only when a non-negligible set of generated data is essentially indistinguishable from real data. Resolving the misalignment issue improves performance, which we demonstrate in experiments that show that given a fixed number of training epochs, TTC produces higher quality images than a comparable WGAN, albeit at increased memory requirements. In addition, TTC provides an iterative formula for the transformed density, which traditional WGANs do not. Finally, TTC can be applied to map any source distribution onto any target; we demonstrate through experiments that TTC can obtain competitive performance in image generation, translation, and denoising without dedicated algorithms.1
Expressive Communication: A Common Framework for Evaluating Developments in Generative Models and Steering Interfaces

There is an increasing interest from ML and HCI communities in empowering creators with better generative models and more intuitive interfaces with which to control them. In music, ML researchers have focused on training models capable of generating pieces with increasing long-range structure and musical coherence, while HCI researchers have separately focused on designing steering interfaces that support user control and ownership. In this study, we investigate through a common framework how developments in both models and user interfaces are important for empowering co-creation where the goal is to create music that communicates particular imagery or ideas (e.g., as is common for other purposeful tasks in music creation like establishing mood or creating accompanying music for another media). Our study is distinguished in that it measures communication through both composer's self-reported experiences, and how listeners evaluate this communication through the music. In an evaluation study with 26 composers creating 100+ pieces of music and listeners providing 1000+ head-to-head comparisons, we find that more expressive models and more steerable interfaces are important and complementary ways to make a difference in composers communicating through music and supporting their creative empowerment.
PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images

While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output. This paper introduces PolyWorld, a neural network that directly extracts building vertices from an image and connects them correctly to create precise polygons. The model predicts the connection strength between each pair of vertices using a graph neural network and estimates the assignments by solving a differentiable optimal transport problem. Moreover, the vertex positions are optimized by minimizing a combined segmentation and polygonal angle difference loss. PolyWorld significantly outperforms the state of the art in building polygonization and achieves not only notable quantitative results, but also produces visually pleasing building polygons. Code and trained weights are publicly available at https://thub.com/zorzis/yWorl-PoldPretrainedNetwork.
On Certified Randomness from Fourier Sampling or Random Circuit Sampling

Certified randomness has a long history in quantum information, with many potential applications. Recently Aaronson (2018, 2020) proposed a novel public certified randomness protocol based on existing random circuit sampling (RCS) experiments. The security of his protocol, however, relies on non-standard complexity-theoretic conjectures which were not previously studied in the literature. Inspired by Aaronson's work, we study certified randomness in the quantum random oracle model (QROM). We show that quantum Fourier Sampling can be used to define a publicly verifiable certified randomness protocol, with unconditional black-box security. In addition to giving a certified randomness protocol in the QROM, our work can also be seen as supporting Aaronson's conjectures for RCS-based randomness generation, as our protocol is in some sense the"black-box version"of Aaronson's protocol. In further support of Aaronson's proposal, we prove a Fourier Sampling version of Aaronson's conjecture by extending Raz and Tal's separation of BQP vs PH. Our work complements the subsequent certified randomness protocol of Yamakawa and Zhandry (2022) in the QROM. Whereas the security of that protocol relied on the Aaronson-Ambainis conjecture, our protocol is unconditionally secure - at the expense of requiring exponential-time classical verification. Our protocol also has a simple heuristic implementation.
Robust Partial-to-Partial Point Cloud Registration in a Full Range

Registration of 3D objects from point clouds is a challenging task due to sparse and noisy measurements, incomplete observations, and large transformations. In this work, we propose the <bold>G</bold>raph <bold>M</bold>atching <bold>C</bold>onsensus <bold>Net</bold>work (<bold>GMCNet</bold>) to estimate faithful correspondences for full-range Partial-to-Partial point cloud Registration (PPR) in object-level registration scenarios. To encode robust point descriptors, we employ a novel Transformation-robust Point Transformer (<bold>TPT</bold>) module to adaptively aggregate local features with respect to the structural relations, taking advantage of both handcrafted rotation-invariant (<italic>RI</italic>) features and noise-resilient spatial coordinates. Based on the synergy of hierarchical graph networks and graphical modeling, we propose the Hierarchical Graphical Modeling (<bold>HGM</bold>) architecture to encode robust descriptors comprising of i) a unary term learned from <italic>RI</italic> features, and ii) multiple smoothness terms encoded from neighboring point relations at different scales through our TPT modules. Extensive experiments show that GMCNet outperforms previous state-of-the-art methods for PPR.
A Highly Configurable Hardware/Software Stack for DNN Inference Acceleration

This work focuses on an efficient Agile design methodology for domain-specific accelerators. We employ feature-by-feature enhancement of a vertical development stack and apply it to the TVM/VTA inference accelerator. We have enhanced the VTA design space and enabled end-to-end support for additional workloads. This has been accomplished by augmenting the VTA micro-architecture and instruction set architecture (ISA), as well as by enhancing the TVM compilation stack to support a wide range of VTA configs. The VTA tsim implementation (CHISEL-based) has been enhanced with fully pipelined versions of the ALU/GEMM execution units. In tsim, memory width can now range between 8-64 bytes. Field widths have been made more flexible to support larger scratchpads. New instructions have been added: element-wise 8-bit multiplication to support depthwise convolution, and load with a choice of pad values to support max pooling. Support for more layers and better double buffering has also been added. Fully pipelining ALU/GEMM helps significantly: 4.9x fewer cycles with minimal area change to run ResNet-18 under the default config. Configs featuring a further 11.5x decrease in cycle count at a cost of 12x greater area can be instantiated. Many points on the area-performance pareto curve are shown, showcasing the balance of execution unit sizing, memory interface width, and scratchpad sizing. Finally, VTA is now able to run Mobilenet 1.0 and all layers for ResNets, including the previously disabled pooling and fully connected layers. The TVM/VTA architecture has always featured end-to-end workload evaluation on RTL in minutes. With our modifications, it now offers a much greater number of feasible configurations with a wide range of cost vs. performance. All capabilities mentioned are available in opensource forks while a subset of these capabilities have already been upstreamed.