Miracle-dz/UniFAR-FTUs huggingface dataset:
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack of efforts to reflect such behaviors by pathologists in the cell detection models, mainly due to the lack of datasets containing both cell and tissue annotations with overlapping regions. To overcome this limitation, we propose and publicly release OCELOT, a dataset purposely dedicated to the study of cell-tissue relationships for cell detection in histopathology. OCELOT provides overlapping cell and tissue annotations on images acquired from multiple organs. Within this setting, we also propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously. When compared against a model trained only for the cell detection task, our proposed approaches improve cell detection performance on 3 datasets: proposed OCELOT, public TIGER, and internal CARP datasets. On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score. We believe the contributions of this paper, including the release of the OCELOT dataset at https://lunit-io.github. io/research/publications/OCELOT are a crucial starting point toward the important research direction of incorporating cell-tissue relationships in computation pathology.
Active Learning Sensitivity Analysis of $\gamma^\prime$(L1$_2$) Precipitate Morphology of Ternary Co-Based Superalloys

To better understand the equilibrium $\gamma^\prime$(L1$_2$) precipitate morphology in Co-based superalloys, a phase field modeling sensitivity analysis is conducted to examine how four phase-field parameters [initial Co concentration ($c_0$), double-well barrier height ($\omega$), gradient energy density coefficient ($\kappa$), and lattice misfit strain ($\epsilon_{\rm misfit}$)] influence the $\gamma^\prime$(L1$_2$) precipitate size and morphology. Gaussian Process Regression (GPR) models are used to fit the sample points and to generate surrogate models for both precipitate size and morphology. In an Active Learning approach, a Bayesian Optimization algorithm is coupled with the GPR models to suggest new sample points to calculate and efficiently update the models based on a reduction of uncertainty. The algorithm has a user-defined objective, which controls the balance between exploration and exploitation for new suggested points. Our methodology provides a qualitative and quantitative relationship between the $\gamma^\prime$(L1$_2$) precipitate size and morphology and the four phase-field parameters, and concludes that the most sensitive phase-field parameter for precipitate size and morphology is the initial Co concentration ($c_0$) and the double-well barrier height ($\omega$), respectively. We note that the GPR model for precipitate morphology required adding a noise tolerance in order to avoid overfitting due to irregularities in some of the simulated equilibrium $\gamma^\prime$(L1$_2$) precipitate morphology.
Gravitational waves from an eccentric population of primordial black holes orbiting Sgr 
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Primordial black holes (PBH), supposedly formed in the very early Universe, have been proposed as a possible viable dark matter candidate. In this work we characterize the expected gravitational wave (GW) losses from a population of PBHs orbiting Sgr A$^{\star}$, the super-massive black hole at the Galactic center (GC), and assess the signal detectability by the planned space-borne interferometer LISA and by the proposed next generation space-borne interferometer $\mu$Ares. Assuming that PBHs indeed form the entire diffuse mass allowed to reside within the orbit of the S2 star, we compute an upper limit to the expected GW signal both from resolved and non-resolved sources, under the further assumptions of monochromatic mass function and thermally distributed eccentricities. By comparing with our previous work where PBHs on circular orbits were assumed, we show for 1 M$_{\odot}$ PBHs how the GW signal from high harmonics over a 10 year data stream increases by a factor of six the chances of LISA detectability, from the $\approx 10\%$ of the circular case, to $\approx 60\%$, whereas multiple sources can be identified in $20\%$ of our mock populations. The background signal, made by summing up all non resolved sources, should be certainly detectable thanks to the PBHs with higher eccentricity evolving under two body relaxation. In the case of $\mu$Ares, because of its improved sensitivity in the $\mu$Hz band, one third of the entire population of PBHs orbiting Sgr A$^{\star}$ would be resolved. The background noise from the remaining non resolved sources should be detectable as well. Finally we present the results for different PBH masses.
Photogalvanic response in multi-Weyl semimetals

We investigate the dependence of the photogalvanic response of a multi-Weyl semimetal on its topological charge, tilt, and chemical potential. We derive analytical expressions for the shift and injection conductivities for tilted charge-$n$ Weyl points $(n=1,2,3)$ using a low energy two-band effective Hamiltonian. For double-Weyl semimetals, we also compute the response from two-band and four-band tight-binding models with broken time-reversal symmetry to study the effect of band bending and the contributions from higher bands. We find a significant deviation in the responses obtained from the effective low-energy continuum model and more realistic four-band continuum and tight-binding models. We analyze several different limits of these models. We describe the nature of the deviations and provide estimates of their dependence on the frequency and other model parameters. Our analysis provides a simple explanation for the first-principle calculation based frequency dependence of the injection current in SrSi$_2$. Additionally, we find interesting parameter regimes where the frequency dependence of the non-linear optical response can be directly used to probe the type-I/type-II nature of the Weyl cone. We obtain analytical results for the charge-4 Weyl semimetal by reducing the original problem involving a triple $k$-space integral to one with only a double integral. This simplification allows us to extract all relevant information about the nature of its second-order dc response and the precise condition for observing circular photogalvanic effect quantization. The semi-analytical approach presented here can also be extended to a systematic study of second harmonic generation and first-order optical conductivity in charge-4 Weyl semimetals.
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance

Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures. Despite its effectiveness and convenience, the reliability of PTQ methods in the presence of some extrem cases such as distribution shift and data noise remains largely unexplored. This paper first investigates this problem on various commonly-used PTQ methods. We aim to answer several research questions related to the influence of calibration set distribution variations, calibration paradigm selection, and data augmentation or sampling strategies on PTQ reliability. A systematic evaluation process is conducted across a wide range of tasks and commonly-used PTQ paradigms. The results show that most existing PTQ methods are not reliable enough in term of the worst-case group performance, highlighting the need for more robust methods. Our findings provide insights for developing PTQ methods that can effectively handle distribution shift scenarios and enable the deployment of quantized DNNs in real-world applications.
TactoFind: A Tactile Only System for Object Retrieval

We study the problem of object retrieval in scenarios where visual sensing is absent, object shapes are unknown beforehand and objects can move freely, like grabbing objects out of a drawer. Successful solutions require localizing free objects, identifying specific object instances, and then grasping the identified objects, only using touch feedback. Unlike vision, where cameras can observe the entire scene, touch sensors are local and only observe parts of the scene that are in contact with the manipulator. Moreover, information gathering via touch sensors necessitates applying forces on the touched surface which may disturb the scene itself. Reasoning with touch, therefore, requires careful exploration and integration of information over time - a challenge we tackle. We present a system capable of using sparse tactile feedback from fingertip touch sensors on a dexterous hand to localize, identify and grasp novel objects without any visual feedback. Videos are available at https://sites.google. com/view/tactofind.
DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph

In this work we create a question answering dataset over the DBLP scholarly knowledge graph (KG). DBLP is an on-line reference for bibliographic information on major computer science publications that indexes over 4.4 million publications published by more than 2.2 million authors. Our dataset consists of 10,000 question answer pairs with the corresponding SPARQL queries which can be executed over the DBLP KG to fetch the correct answer. DBLP-QuAD is the largest scholarly question answering dataset.
Searching a Tree with Signals: Routing Mobile Sensors for Targets Emitting Radiation, Chemicals or Scents

Adversarial search of a network for an immobile Hider (or target) was introduced and solved for rooted trees by Gal (1979). In this zero-sum game, a Hider picks a point to hide on the tree and a Searcher picks a unit speed trajectory starting at the root. The payoff (to the Hider) is the search time. In Gal's model (and many subsequent investigations), the Searcher receives no additional information after the Hider chooses his location. In reality, the Searcher will often receive such locational information. For homeland security, mobile sensors on vehicles have been used to locate radioactive material stashed in an urban environment. In a military setting, mobile sensors can detect chemical signatures from land mines. In predator-prey search, the predator often has specially attuned senses (hearing for wolves, vision for eagles, smell for dogs, sonar for bats, pressure sensors for sharks) that may help it locate the prey. How can such noisy locational information be used by the Searcher to modify her route? We model such information as signals which indicate which of two branches of a binary tree should be searched first, where the signal has a known accuracy p<1. Our solution calculates which branch (at every branch node) is favored, meaning it should always be searched first when the signal is in that direction. When the signal is in the other direction, we calculate the probability the signal should be followed. Compared to the optimal Hider strategy in the classic search game of Gal, the Hider's optimal distribution for this model is more skewed towards leaf nodes that are further from the root.
Polymer informatics beyond homopolymers

Abstract Polymers are diverse and versatile materials that have met a wide range of material application demands. They come in several flavors and architectures (e.g., homopolymers, copolymers, polymer blends, and polymers with additives). Searching this enormous space for suitable materials with a specific set of property/performance targets is thus nontrivial, painstaking, and expensive. Such a search process can be made effective by the creation of rapid and accurate property predictors. In this article, we present a machine learning framework to predict the thermal properties of homopolymers, copolymers, and polymer blends. A universal fingerprinting scheme capable of handling this entire polymer chemical class has been developed and a multitask deep learning algorithm is trained simultaneously on a large data set of glass-transition, melting, and degradation temperatures. The trained models demonstrate precision and scalability to other properties when relevant data becomes accessible. Impact statement The chemical and structural variations that can be achieved with the polymers is staggering. Such extraordinary and diverse possibilities translate to attractive combinations of physical properties impacting several application spaces, making the polymeric class of materials ubiquitous in our modern society. This chemistry–structure–property diversity is accompanied by a major challenge. Searching the chemo-structural space to identify suitable application-relevant candidates with the right set of target properties is nontrivial, requiring advanced rapid property prediction and search schemes. In this article, we present a data-driven machine learning framework to instantaneously predict the thermal properties (an important property class) of a dizzyingly large class of polymer archetypes, namely, homopolymers, copolymers, and polymer blends. A state-of-the-art machine learning algorithm has been developed and trained on a large data set of glass-transition, melting, and degradation temperatures, to make instantaneous predictions of these properties for any new-to-the-world polymer that falls in this large important polymer chemical class. This prediction scheme paves the way for discovering polymers with unprecedented thermal stability by allowing searches of enormous chemical spaces at scale. Graphical abstract
It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting

With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most spatio-temporal forecasting models typically comprise distinct components that learn spatial and temporal dependencies. A common methodology employs some Graph Neural Network (GNN) to capture relations between spatial locations, while another network, such as a recurrent neural network (RNN), learns temporal correlations. By representing every recorded sample as its own node in a graph, rather than all measurements for a particular location as a single node, temporal and spatial information is encoded in a similar manner. In this setting, GNNs can now directly learn both temporal and spatial dependencies, jointly, while also alleviating the need for additional temporal networks. Furthermore, the framework does not require aligned measurements along the temporal dimension, meaning that it also naturally facilitates irregular time series, different sampling frequencies or missing data, without the need for data imputation. To evaluate the proposed methodology, we consider wind speed forecasting as a case study, where our proposed framework outperformed other spatio-temporal models using GNNs with either Transformer or LSTM networks as temporal update functions.
Reduced-order models for integrated aeroservoelastic optimization

Recent developments of modal-based aeroservoelastic modeling techniques extended the applicability of the modal approach to almost all of the aeroservoelasticity related aspects of aircraft structural and control design. The various techniques are reviewed and combined for an integrated design optimization scheme, where stress, static - aeroelastic, closed-loop flutter, control margins, time response, and continuous gust constraints are treated with a common basic model. The structure is represented in the basic model by a set of low-frequency normal modes of a baseline design. Design changes are adequately addressed without changing the generalized coordinates. Typical difficulties of the modal approach are alleviated by various optional fictitious-mass and modal-perturbation techniques. Static modes can be added during the optimization process for better convergence to the optimal solution. Minimum-state rational approximation of the unsteady aerodynamics leads to an efficient state-space model that can be augmented by any combination of linear-contro l components. A physical weighting algorithm is used to improve the aerodynamic approximations and to select modes for truncation or residualizati on. The reduced-size models and the associated analytic sensitivities to design changes facilitate extremely efficient and adequately accurate on-line optimization sessions. LA] [A0], [A,], [A2] b
Egocentric Audio-Visual Object Localization

Humans naturally perceive surrounding scenes by unifying sound and sight from a first-person view. Likewise, machines are advanced to approach human intelligence by learning with multisensory inputs from an egocentric perspective. In this paper, we explore the challenging egocentric audio-visual object localization task and observe that 1) egomotion commonly exists in first-person recordings, even within a short duration; 2) The out-of-view sound components can be created when wearers shift their attention. To address the first problem, we propose a geometryaware temporal aggregation module that handles the egomotion explicitly. The effect of egomotion is mitigated by estimating the temporal geometry transformation and exploiting it to update visual representations. Moreover, we propose a cascaded feature enhancement module to over-come the second issue. It improves cross-modal localization robustness by disentangling visually-indicated audio representation. During training, we take advantage of the naturally occurring audio-visual temporal synchronization as the “free” self-supervision to avoid costly labeling. We also annotate and create the Epic Sounding Object dataset for evaluation purposes. Extensive experiments show that our method achieves state-of-the-art localization performance in egocentric videos and can be generalized to diverse audio-visual scenes. Code is available at https://github.com/WikiChao/Ego-AV-Loc.
POTTER: Pooling Attention Transformer for Efficient Human Mesh Recovery

Transformer architectures have achieved SOTA performance on the human mesh recovery (HMR) from monocular images. However, the performance gain has come at the cost of substantial memory and computational overhead. A lightweight and efficient model to reconstruct accurate human mesh is needed for real-world applications. In this paper, we propose a pure transformer architecture named POoling aTtention TransformER (POTTER) for the HMR task from single images. Observing that the conventional attention module is memory and computationally expensive, we propose an efficient pooling attention module, which significantly reduces the memory and computational cost without sacrificing performance. Furthermore, we design a new transformer architecture by integrating a High-Resolution (HR) stream for the HMR task. The high-resolution local and global features from the HR stream can be utilized for recovering more accurate human mesh. Our POTTER outperforms the SOTA method METRO by only requiring 7% of total parameters and 14% of the Multiply-Accumulate Operations on the Human3.6M (PA-MPJPE metric) and 3DPW (all three metrics) datasets. The project webpage is https://zczcwh.github. io/potter_page/.
NVA UTO N ET : F AST AND A CCURATE 360 ◦ 3D P ERCEPTION F OR S ELF D RIVING

Robust real-time perception of 3D world is essential to the autonomous vehicle. We introduce an end-to-end surround camera perception system for self-driving. Our perception system is a novel multi-task, multi-camera network which takes a variable set of time-synced camera images as input and produces a rich collection of 3D signals such as sizes, orientations, locations of obstacles, parking spaces and free-spaces, etc. Our perception network is modular and end-to-end: 1) the outputs can be consumed directly by downstream modules without any post-processing such as clustering and fusion — improving speed of model deployment and in-car testing 2) the whole network training is done in one single stage — improving speed of model improvement and iterations. The network is well designed to have high accuracy while running at 53 fps on NVIDIA Orin SoC (system-on-a-chip). The network is robust to sensor mounting variations (within some tolerances) and can be quickly customized for different vehicle types via efﬁcient model ﬁne-tuning thanks of its capability of taking calibration parameters as additional inputs during training and testing. Most importantly, our network has been successfully deployed and being tested on real roads.
Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention

In this paper, we aim to learn a semantic radiance field from multiple scenes that is accurate, efficient and generalizable. While most existing NeRFs target at the tasks of neural scene rendering, image synthesis and multi-view reconstruction, there are a few attempts such as Semantic-NeRF that explore to learn high-level semantic understanding with the NeRF structure. However, Semantic-NeRF simultaneously learns color and semantic label from a single ray with multiple heads, where the single ray fails to provide rich semantic information. As a result, Semantic NeRF relies on positional encoding and needs to train one specific model for each scene. To address this, we propose Semantic Ray (S-Ray) to fully exploit semantic information along the ray direction from its multi-view reprojections. As directly performing dense attention over multi-view reprojected rays would suffer from heavy computational cost, we design a Cross-Reprojection Attention module with consecutive intra-view radial and cross-view sparse attentions, which decomposes contextual information along reprojected rays and cross multiple views and then collects dense connections by stacking the modules. Experiments show that our S-Ray is able to learn from multiple scenes, and it presents strong generalization ability to adapt to unseen scenes. Project page: https://liuff19.github. io/S-Ray/.
Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation

We develop a class of interacting particle systems for implementing a maximum marginal likelihood estimation (MMLE) procedure to estimate the parameters of a latent variable model. We achieve this by formulating a continuous-time interacting particle system which can be seen as a Langevin diffusion over an extended state space of parameters and latent variables. In particular, we prove that the parameter marginal of the stationary measure of this diffusion has the form of a Gibbs measure where number of particles acts as the inverse temperature parameter in classical settings for global optimisation. Using a particular rescaling, we then prove geometric ergodicity of this system and bound the discretisation error in a manner that is uniform in time and does not increase with the number of particles. The discretisation results in an algorithm, termed Interacting Particle Langevin Algorithm (IPLA) which can be used for MMLE. We further prove nonasymptotic bounds for the optimisation error of our estimator in terms of key parameters of the problem, and also extend this result to the case of stochastic gradients covering practical scenarios. We provide numerical experiments to illustrate the empirical behaviour of our algorithm in the context of logistic regression with verifiable assumptions. Our setting provides a straightforward way to implement a diffusion-based optimisation routine compared to more classical approaches such as the Expectation Maximisation (EM) algorithm, and allows for especially explicit nonasymptotic bounds.