Miracle-dz/UniFAR-FTUs huggingface dataset:
CLIP-VG: Self-Paced Curriculum Adapting of CLIP for Visual Grounding

Visual Grounding (VG) is a crucial topic in the field of vision and language, which involves locating a specific region described by expressions within an image. To reduce the reliance on manually labeled data, unsupervised methods have been developed to locate regions using pseudo-labels. However, the performance of existing unsupervised methods is highly dependent on the quality of pseudo-labels and these methods always encounter issues with limited diversity. In order to utilize vision and language pre-trained models to address the grounding problem, and reasonably take advantage of pseudo-labels, we propose CLIP-VG, a novel method that can conduct self-paced curriculum adapting of CLIP with pseudo-language labels. We propose a simple yet efficient end-to-end network architecture to realize the transfer of CLIP to the visual grounding. Based on the CLIP-based architecture, we further propose single-source and multi-source curriculum adapting algorithms, which can progressively find more reliable pseudo-labels to learn an optimal model, thereby achieving a balance between reliability and diversity for the pseudo-language labels. Our method outperforms the current state-of-the-art unsupervised method by a significant margin on RefCOCO/+/g datasets in both single-source and multi-source scenarios, with improvements ranging from 6.78% to 10.67% and 11.39% to 14.87%, respectively. Furthermore, our approach even outperforms existing weakly supervised methods.
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models

Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods gets entangled with the learning of the model hyperparameters. We improve hyperparameter learning in GP models and focus on the interplay between variational inference (VI) and the learning target. While VI's lower bound to the marginal likelihood is a suitable objective for inferring the approximate posterior, we show that a direct approximation of the marginal likelihood as in Expectation Propagation (EP) is a better learning objective for hyperparameter optimization. We design a hybrid training procedure to bring the best of both worlds: it leverages conjugate-computation VI for inference and uses an EP-like marginal likelihood approximation for hyperparameter learning. We compare VI, EP, Laplace approximation, and our proposed training procedure and empirically demonstrate the effectiveness of our proposal across a wide range of data sets.
Perturbation of Fiedler vector: interest for graph measures and shape analysis

In this paper we investigate some properties of the Fiedler vector, the so-called first non-trivial eigenvector of the Laplacian matrix of a graph. There are important results about the Fiedler vector to identify spectral cuts in graphs but far less is known about its extreme values and points. We propose a few results and conjectures in this direction. We also bring two concrete contributions, i) by defining a new measure for graphs that can be interpreted in terms of extremality (inverse of centrality), ii) by applying a small perturbation to the Fiedler vector of cerebral shapes such as the corpus callosum to robustify their parameterization.
A Unified One-Step Solution for Aspect Sentiment Quad Prediction

Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspect-based sentiment analysis as it provides a complete aspect-level sentiment structure. However, existing ASQP datasets are usually small and low-density, hindering technical advancement. To expand the capacity, in this paper, we release two new datasets for ASQP, which contain the following characteristics: larger size, more words per sample, and higher density. With such datasets, we unveil the shortcomings of existing strong ASQP baselines and therefore propose a unified one-step solution for ASQP, namely One-ASQP, to detect the aspect categories and to identify the aspect-opinion-sentiment (AOS) triplets simultaneously. Our One-ASQP holds several unique advantages: (1) by separating ASQP into two subtasks and solving them independently and simultaneously, we can avoid error propagation in pipeline-based methods and overcome slow training and inference in generation-based methods; (2) by introducing sentiment-specific horns tagging schema in a token-pair-based two-dimensional matrix, we can exploit deeper interactions between sentiment elements and efficiently decode the AOS triplets; (3) we design ``[NULL]'' token can help us effectively identify the implicit aspects or opinions. Experiments on two benchmark datasets and our released two datasets demonstrate the advantages of our One-ASQP. The two new datasets are publicly released at \url{https://www.github. com/Datastory-CN/ASQP-Datasets}.
Unraveling the Complexity of Dwarf Galaxy Dynamics: A Study of Binary Orbital Motions

We investigate the impact of binary orbital motions on the dynamical modeling of dwarf galaxies with intrinsic line-of-sight velocity dispersions ( σvr ) of 1–9 km s−1. Using dwarf galaxies from the auriga level-2 and level-3 simulations, we apply the Jeans Anisotropic Multi-Gaussian Expansion modeling to tracer stars before and after including binaries to recover the dynamical masses. The recovered total masses within the half-mass radius of tracers, M(< r half), are always inflated due to binary motions, with greater inflations occurring for smaller σvr . However, many dwarf galaxies experience central density deflated due to binary motions, with little dependence on σvr . This is due to the negative radial gradients in the velocity dispersion profiles, with the fractional inflation in σvr due to binaries more significant in outskirts. An extreme binary fraction of 70% can lead to central density deflation of up to 10%–20% at 3 km s−1 < σvr < 8 km s−1, with M( < r half) inflated by 4% at 9 km s−1 and up to 15% at 3 km s−1. A lower binary fraction of 36% leads to similar deflations, with the inflations decreasing to approximately 10% at 3 km s−1 and becoming statistically insignificant. The choice of binary orbit distribution models does not result in significant differences, and observational errors tend to slightly weaken the deflations in the recovered central density. Two observations separated by 1 yr to exclude binaries lead to almost zero inflations/deflations for a binary fraction of 36% over 3 km s−1 < σvr<9 km s−1. For σvr∼1 km s−1 to 3 km s−1, a binary fraction of 70% (36%) still results in 60% (30%) to 10% (1%) of inflations in M( < r half), even with two-epoch observation.
Hydrophobic and breathable nanomembrane for food package material by mimicking cocoon’s structure

The aim of this study was to investigate the cross-linked poly(vinyl alcohol) nanofibrous mats treated by flourinated alkane in supercritical carbon dioxide medium. The surface morphology and chemical structure of electrospun mats were analyzed by scanning electron microscopy and Fourier transform infrared. The results showed that the treated mats could maintain their integrity and fibrous morphology as well as their porous structure after being treated. However, the wettability was changed greatly, the average contact angle of treated nanofibrous mats increased from its original value of 28° to 134°, revealing that the treated mats had good water repellent properties. The paper concluded that the hydrophobic and breathable mats with porous structure might be an excellent candidate for food package materials.
MobileNMT: Enabling Translation in 15MB and 30ms

Deploying NMT models on mobile devices is essential for privacy, low latency, and offline scenarios. For high model capacity, NMT models are rather large. Running these models on devices is challenging with limited storage, memory, computation, and power consumption. Existing work either only focuses on a single metric such as FLOPs or general engine which is not good at auto-regressive decoding. In this paper, we present MobileNMT, a system that can translate in 15MB and 30ms on devices. We propose a series of principles for model compression when combined with quantization. Further, we implement an engine that is friendly to INT8 and decoding. With the co-design of model and engine, compared with the existing system, we speed up 47.0x and save 99.5% of memory with only 11.6% loss of BLEU. Our code will be publicly available after the anonymity period.
Nonclassicality versus quantum non-Gaussianity of photon subtracted displaced Fock state

In this paper, a quantitative investigation of the non-classical and quantum non-Gaussian characters of the photon-subtracted displaced Fock state \ket{\psi}=a^kD(\alpha)\ket{n}, where k is the number of photons subtracted, n is Fock parameter, is performed by using a collection of measures like Wigner logarithmic negativity, linear entropy potential, skew information based measure, and relative entropy of quantum non-Gaussianity. It is noticed that the number of photons subtracted (k) changes the nonclassicality and quantum non-Gaussianity in a significant amount in the regime of small values of the displacement parameter whereas the Fock parameter (n) presents a notable change in the large regime of the displacement parameter. In this respect, the role of the Fock parameter is found to be stronger as compared to the photon subtraction number. Finally, the Wigner function dynamics considering the effects of photon loss channel is used to show that the Wigner negativity can only be detected by highly efficient detectors.
On the Generalization of Multi-modal Contrastive Learning

Multi-modal contrastive learning (MMCL) has recently garnered considerable interest due to its superior performance in visual tasks, achieved by embedding multi-modal data, such as visual-language pairs. However, there still lack theoretical understandings of how MMCL extracts useful visual representation from multi-modal pairs, and particularly, how MMCL outperforms previous approaches like self-supervised contrastive learning (SSCL). In this paper, by drawing an intrinsic connection between MMCL and asymmetric matrix factorization, we establish the first generalization guarantees of MMCL for visual downstream tasks. Based on this framework, we further unify MMCL and SSCL by showing that MMCL implicitly performs SSCL with (pseudo) positive pairs induced by text pairs. Through this unified perspective, we characterize the advantage of MMCL by showing that text pairs induce more semantically consistent and diverse positive pairs, which, according to our analysis, provably benefit downstream generalization. Inspired by this finding, we propose CLIP-guided resampling methods to significantly improve the downstream performance of SSCL on ImageNet by leveraging multi-modal information. Code is available at https://github.com/PKU-ML/CLIP-Help-SimCLR.
The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions

The eccentricity of a substellar companion is an important tracer of its formation history. Directly imaged companions often present poorly constrained eccentricities. A recently developed prior framework for orbit fitting called “observable-based priors” has the advantage of improving biases in derived orbit parameters for objects with minimal phase coverage, which is the case for the majority of directly imaged companions. We use observable-based priors to fit the orbits of 21 exoplanets and brown dwarfs in an effort to obtain the eccentricity distributions with minimized biases. We present the objects’ individual posteriors compared to their previously derived distributions, showing in many cases a shift toward lower eccentricities. We analyze the companions’ eccentricity distribution at a population level, and compare this to the distributions obtained with the traditional uniform priors. We fit a Beta distribution to our posteriors using observable-based priors, obtaining shape parameters α = 1.09−0.22+0.30 and β = 1.42−0.25+0.33 . This represents an approximately flat distribution of eccentricities. The derived α and β parameters are consistent with the values obtained using uniform priors, though uniform priors lead to a tail at high eccentricities. We find that separating the population into high- and low-mass companions yields different distributions depending on the classification of intermediate-mass objects. We also determine via simulation that the minimal orbit coverage needed to give meaningful posteriors under the assumptions made for directly imaged planets is ≈15% of the inferred period of the orbit.
Adaptive image restoration using constrained deconvolution

Algorithme de restauration d'image degradee a cause du flou et du bruit additif en utilisant le changement de forme de la reponse impulsionnelle d'un filtre adaptatif de deconvolution de contraintes pour la partition d'images. On montre l'efficacite d'un tel algorithme mais il necessite des calculs complexes
Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short Term Memory and Autoencoder

Currently, the widespread of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspection (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real-time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay, and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the abovementioned methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmission requirements to many Internet users using SDN capability and the potential of deep learning. Specifically, DNN, CNN, LSTM, and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that do not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13. Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.
PhenoBench - A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain

The production of food, feed, fiber, and fuel is a key task of agriculture. Especially crop production has to cope with a multitude of challenges in the upcoming decades caused by a growing world population, climate change, the need for sustainable production, lack of skilled workers, and generally the limited availability of arable land. Vision systems could help cope with these challenges by offering tools to make better and more sustainable field management decisions and support the breeding of new varieties of crops by allowing temporally dense and reproducible measurements. Recently, tackling perception tasks in the agricultural domain got increasing interest in the computer vision and robotics community since agricultural robotics are one promising solution for coping with the lack of workers and enable a more sustainable agricultural production at the same time. While large datasets and benchmarks in other domains are readily available and have enabled significant progress toward more reliable vision systems, agricultural datasets and benchmarks are comparably rare. In this paper, we present a large dataset and benchmarks for the semantic interpretation of images of real agricultural fields. Our dataset recorded with a UAV provides high-quality, dense annotations of crops and weeds, but also fine-grained labels of crop leaves at the same time, which enable the development of novel algorithms for visual perception in the agricultural domain. Together with the labeled data, we provide novel benchmarks for evaluating different visual perception tasks on a hidden test set comprised of different fields: known fields covered by the training data and a completely unseen field. The tasks cover semantic segmentation, panoptic segmentation of plants, leaf instance segmentation, detection of plants and leaves, and hierarchical panoptic segmentation for jointly identifying plants and leaves.
Energy-Based Models for Cross-Modal Localization using Convolutional Transformers

We present a novel framework using Energy-Based Models (EBMs) for localizing a ground vehicle mounted with a range sensor against satellite imagery in the absence of GPS. Lidar sensors have become ubiquitous on autonomous vehicles for describing its surrounding environment. Map priors are typically built using the same sensor modality for localization purposes. However, these map building endeavors using range sensors are often expensive and time-consuming. Alternatively, we leverage the use of satellite images as map priors, which are widely available, easily accessible, and pro-vide comprehensive coverage. We propose a method using convolutional transformers that performs accurate metric-level localization in a cross-modal manner, which is challenging due to the drastic difference in appearance between the sparse range sensor readings and the rich satellite imagery. We train our model end-to-end and demonstrate our approach achieving higher accuracy than the state-of-the-art on KITTI, Pandaset, and a custom dataset.
Constraining Nuclear Parameters Using Gravitational Waves from f-mode Oscillations in Neutron Stars

Gravitational waves (GWs) emanating from unstable quasi-normal modes in neutron stars (NSs) could be accessible with the improved sensitivity of the current GW detectors or with the next-generation GW detectors and, therefore, can be employed to study the NS interior. Assuming f-mode excitation in isolated pulsars with typical energy of pulsar glitches and considering potential f-mode GW candidates for A+ (upgraded LIGO detectors operating at fifth observing run design sensitivity) and Einstein Telescope (ET), we demonstrate the inverse problem of NS asteroseismology within a Bayesian formalism to constrain the nuclear parameters and NS equation of state (EOS). We describe the NS interior within relativistic mean-field formalism. Taking the example of glitching pulsars, we find that for a single event in A+ and ET, among the nuclear parameters, the nucleon effective mass (m*) within 90% credible interval can be restricted within 10% and 5%, respectively. At the same time, the incompressibility (K) and the slope of the symmetry energy (L) are only loosely constrained. Considering multiple (10) events in A+ and ET, all the nuclear parameters are well constrained, especially m*, which can be constrained to 3% and 2% in A+ and ET, respectively. Uncertainty in the observables of a 1.4 M ⊙ NS such as radius ( R1.4M⊙ ), f-mode frequency ( f1.4M⊙ ), damping time ( τ1.4M⊙ ), and a few EOS properties including squared speed of sound (c s 2) are also estimated.
Differentially Private Selection from Secure Distributed Computing

Given a collection of vectors $x^{(1)},\dots,x^{(n)} \in \{0,1\}^d$, the selection problem asks to report the index of an"approximately largest"entry in $x=\sum_{j=1}^n x^{(j)}$. Selection abstracts a host of problems--in machine learning it can be used for hyperparameter tuning, feature selection, or to model empirical risk minimization. We study selection under differential privacy, where a released index guarantees privacy for each vectors. Though selection can be solved with an excellent utility guarantee in the central model of differential privacy, the distributed setting lacks solutions. Specifically, strong privacy guarantees with high utility are offered in high trust settings, but not in low trust settings. For example, in the popular shuffle model of distributed differential privacy, there are strong lower bounds suggesting that the utility of the central model cannot be obtained. In this paper we design a protocol for differentially private selection in a trust setting similar to the shuffle model--with the crucial difference that our protocol tolerates corrupted servers while maintaining privacy. Our protocol uses techniques from secure multi-party computation (MPC) to implement a protocol that: (i) has utility on par with the best mechanisms in the central model, (ii) scales to large, distributed collections of high-dimensional vectors, and (iii) uses $k\geq 3$ servers that collaborate to compute the result, where the differential privacy holds assuming an honest majority. Since general-purpose MPC techniques are not sufficiently scalable, we propose a novel application of integer secret sharing, and evaluate the utility and efficiency of our protocol theoretically and empirically. Our protocol is the first to demonstrate that large-scale differentially private selection is possible in a distributed setting.
Fully Automated Verification of Linear Time-Invariant Systems against Signal Temporal Logic Specifications via Reachability Analysis

While reachability analysis is one of the most promising approaches for the formal verification of dynamic systems, a major disadvantage preventing a more widespread application is the requirement to manually tune algorithm parameters such as the time step size. Manual tuning is especially problematic if one aims to verify that the system satisfies complicated specifications described by signal temporal logic formulas since the effect the tightness of the reachable set has on the satisfaction of the specification is often non-trivial to see for humans. We address this problem with a fully-automated verifier for linear systems, which automatically refines all parameters for reachability analysis until it can either prove or disprove that the system satisfies a signal temporal logic formula for all initial states and all uncertain inputs. Our verifier combines reachset temporal logic with dependency preservation to obtain a model checking approach whose over-approximation error converges to zero for adequately tuned parameters. While we in this work focus on linear systems for simplicity, the general concept we present can equivalently be applied for nonlinear and hybrid systems.
Balancing of competitive two-player Game Levels with Reinforcement Learning

The balancing process for game levels in a competitive two-player context involves a lot of manual work and testing, particularly in non-symmetrical game levels. In this paper, we propose an architecture for automated balancing of tile-based levels within the recently introduced PCGRL framework (procedural content generation via reinforcement learning). Our architecture is divided into three parts: (1) a level generator, (2) a balancing agent and, (3) a reward modeling simulation. By playing the level in a simulation repeatedly, the balancing agent is rewarded for modifying it towards the same win rates for all players. To this end, we introduce a novel family of swap-based representations to increase robustness towards playability. We show that this approach is capable to teach an agent how to alter a level for balancing better and faster than plain PCGRL. In addition, by analyzing the agent’s swapping behavior, we can draw conclusions about which tile types influence the balancing most. We test and show our results using the Neural MMO (NMMO) environment in a competitive two-player setting.