Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Temperature-Dependent Phase Transition in Modern Hopfield Networks

The recent discovery of a connection between Transformers and Modern Hopfield Networks (MHNs) has reignited the study of neural networks from a physical energy-based perspective. This paper focuses on the pivotal effect of the inverse temperature hyperparameter $\beta$ on the distribution of energy minima of the MHN. To achieve this, the distribution of energy minima is tracked in a simplified MHN in which equidistant normalised patterns are stored. This network demonstrates a phase transition at a critical temperature $\beta_{\text{c}}$, from a single global attractor towards highly pattern specific minima as $\beta$ is increased. Importantly, the dynamics are not solely governed by the hyperparameter $\beta$ but are instead determined by an effective inverse temperature $\beta_{\text{eff}}$ which also depends on the distribution and size of the stored patterns. Recognizing the role of hyperparameters in the MHN could, in the future, aid researchers in the domain of Transformers to optimise their initial choices, potentially reducing the necessity for time and energy expensive hyperparameter fine-tuning.
JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation

Part-aware panoptic segmentation is a problem of computer vision that aims to provide a semantic understanding of the scene at multiple levels of granularity. More precisely, semantic areas, object instances, and semantic parts are predicted simultaneously. In this paper, we present our joint panoptic part fusion (JPPF) that combines the three individual segmentations effectively to obtain a panoptic-part segmentation. Two aspects are of utmost importance for this: first, a unified model for the three problems is desired that allows for mutually improved and consistent representation learning. Second, balancing the combination so that it gives equal importance to all individual results during fusion. Our proposed JPPF is parameter-free and dynamically balances its input. The method is evaluated and compared on the Cityscapes panoptic parts (CPP) and Pascal panoptic parts (PPP) datasets in terms of PartPQ and Part-Whole Quality (PWQ). In extensive experiments, we verify the importance of our fair fusion, highlight its most significant impact for areas that can be further segmented into parts, and demonstrate the generalization capabilities of our design without fine-tuning on 5 additional datasets.
Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion

In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022. Due to recent successes in leveraging machine learning for clinical decision support, there has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data. A major challenge hindering widespread adoption in clinical use is poor generalization of such networks due to scarcity of large-scale, diverse, balanced prostate imaging datasets for training such networks. In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy. To the best of the authors' knowledge, this is the first study to leverage conditioning for synthesis of prostate cancer imaging. Experimental results show that the proposed strategy, which we call Cancer-Net PCa-Gen, enhances synthesis of diverse prostate images through controllable tumour locations and better anatomical and textural fidelity. These crucial features make it well-suited for augmenting real patient data, enabling neural networks to be trained on a more diverse and comprehensive data distribution. The Cancer-Net PCa-Gen framework and sample images have been made publicly available at https://www.kaggle. com/datasets/deetsadi/cancer-net-pca-gen-dataset as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer.
RIS-Assisted Generalized Receive Quadrature Spatial Modulation

In this paper, reconfigurable intelligent surface (RIS)-assisted generalized receive quadrature spatial modulation (RIS-GRQSM) is proposed to improve the spectral efficiency of RIS-aided quadrature spatial modulation (QSM) systems by utilizing the concept of generalized spatial modulation (GSM). That is, multiple antennas are activated at the receiver independently for both the real and imaginary parts. We propose a max-min optimization problem to adjust the phase shifts of all RIS elements to maximize the relevant signal-to-noise ratios (SNRs) at all activated receive antennas. Using Lagrange duality, the non-convex optimization problem involving the phase shifts of all RIS elements reduces to a convex optimization involving a number of variables equal to the number of activated receive antennas. A successive greedy detector (GD) can be used at the receiver to detect the active antennas, which simplifies the detection process. The numerical results show that the proposed scheme outperforms the benchmark schemes in terms of error rate performance, especially in systems with a larger number of receive antennas. In the special case where each receive antenna corresponds to a user and is activated, the RIS-GRQSM system becomes a multicast communication system. In this context, in contrast to existing phase shift optimization algorithms which exhibit an impractical level of complexity, our proposed solution offers the advantage of low complexity and practical feasibility of implementation.
PEAN: A Diffusion-based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution

Scene text image super-resolution (STISR) aims at simultaneously increasing the resolution and readability of low-resolution scene text images, thus boosting the performance of the downstream recognition task. Two factors in scene text images, semantic information and visual structure, affect the recognition performance significantly. To mitigate the effects from these factors, this paper proposes a Prior-Enhanced Attention Network (PEAN). Specifically, a diffusion-based module is developed to enhance the text prior, hence offering better guidance for the SR network to generate SR images with higher semantic accuracy. Meanwhile, the proposed PEAN leverages an attention-based modulation module to understand scene text images by neatly perceiving the local and global dependence of images, despite the shape of the text. A multi-task learning paradigm is employed to optimize the network, enabling the model to generate legible SR images. As a result, PEAN establishes new SOTA results on the TextZoom benchmark. Experiments are also conducted to analyze the importance of the enhanced text prior as a means of improving the performance of the SR network. Code will be made available at https://github.com/jdfxzzy/PEAN.
Evolution of giant monopole resonance with triaxial deformation

Background: The isoscalar giant monopole resonance (ISGMR) splits into two peaks in prolately deformed nuclei. When a nucleus is triaxially deformed, a peak appears in the middle between the two peaks. Purpose: We investigate the mechanism of the appearance of the middle peak in the ISGMR in triaxial nuclei. Method: We perform the constrained Skyrme-Hartree-Fock-Bogoliubov (CHFB) calculation for arbitrary triaxial shapes in $^{100}$Mo. We calculate the strength functions of the isoscalar monopole (ISM) and IS quadrupole modes on the CHFB states. Furthermore, we investigate vibrations of matter distributions in $x$, $y$, and $z$ directions induced by the external ISM field, with the $z$ axis being the longest axis of the triaxial shape. Results: The middle peak in the ISM strength evolves from the triaxial degree $\gamma=0^\circ$ to $60^\circ$. This is because the difference between the vibration in $x$ direction and that in $y$ direction is evident with an increase in $\gamma$ and the quadrupole $K=2$ component of the induced density of the ISM at the middle peak increases as $\gamma$ increases, where $K$ denotes the $z$ component of the angular momentum. This property is also obtained in the unperturbed ISM strength without the residual fields. Conclusions: The mixing between the monopole and quadrupole modes is primarily determined by the ground-state deformation. Therefore, the ISM strength of the middle peak becomes strong as the triaxial degree in the ground state increases.
Modelling Einstein cluster using Einasto profile

We demonstrate a general relativistic approach to model dark matter halos using the Einstein cluster, with the matter stress-energy generated by collisionless particles moving on circular geodesics in all possible angular directions and orbital radii. Such matter, as is known, allows an anisotropic pressure profile with non-zero tangential but zero radial pressure. We use the Einasto density profile for the Einstein cluster. Analytical studies on its properties (metric functions) and stability issues are investigated. Further, to establish this model (with the Einasto profile) as one for a dark matter halo, we use the SPARC galactic rotation curve data and estimate the best-fit values for the model parameters. General relativistic features (beyond the Keplerian velocities) such as the tangential pressure profile, are quantitatively explored. Thus, Einstein clusters with the Einasto profile, which tally well with observations, may be considered as a viable model for dark matter halos.
On the Maximum Black Hole Mass at Solar Metallicity

In high-metallicity environments the mass that black holes (BHs) can reach just after core collapse widely depends on how much mass their progenitor stars lose via winds. On one hand, new theoretical and observational insights suggest that early-stage winds should be weaker than what many canonical models prescribe. On the other hand, the proximity to the Eddington limit should affect the formation of optically thick envelopes already during the earliest stages of stars with initial masses M ZAMS ≳ 100 M ⊙, hence resulting in higher mass-loss rates during the main sequence. We use the evolutionary codes MESA and Genec to calculate a suite of tracks for massive stars at solar metallicity Z ⊙ = 0.014, which incorporate these changes in our wind-mass-loss prescription. In our calculations we employ moderate rotation, high overshooting, and magnetic angular momentum transport. We find a maximum BH mass M BH , max = 28.3 M ⊙ at Z ⊙. The most massive BHs are predicted to form from stars with M ZAMS ≳ 250 M ⊙, with the BH mass directly proportional to its progenitor’s M ZAMS. We also find in our models that at Z ⊙ almost any BH progenitor naturally evolves into a Wolf–Rayet star due to the combined effect of internal mixing and wind mass loss. These results are considerably different from most recent studies regarding the final mass of stars before their collapse into BHs. While we acknowledge the inherent uncertainties in stellar evolution modeling, our study underscores the importance of employing the most up-to-date physics in BH mass predictions.
The Trifecta: Three simple techniques for training deeper Forward-Forward networks

Modern machine learning models are able to outperform humans on a variety of non-trivial tasks. However, as the complexity of the models increases, they consume significant amounts of power and still struggle to generalize effectively to unseen data. Local learning, which focuses on updating subsets of a model's parameters at a time, has emerged as a promising technique to address these issues. Recently, a novel local learning algorithm, called Forward-Forward, has received widespread attention due to its innovative approach to learning. Unfortunately, its application has been limited to smaller datasets due to scalability issues. To this end, we propose The Trifecta, a collection of three simple techniques that synergize exceptionally well and drastically improve the Forward-Forward algorithm on deeper networks. Our experiments demonstrate that our models are on par with similarly structured, backpropagation-based models in both training speed and test accuracy on simple datasets. This is achieved by the ability to learn representations that are informative locally, on a layer-by-layer basis, and retain their informativeness when propagated to deeper layers in the architecture. This leads to around 84% accuracy on CIFAR-10, a notable improvement (25%) over the original FF algorithm. These results highlight the potential of Forward-Forward as a genuine competitor to backpropagation and as a promising research avenue.
On the number of monochromatic solutions to multiplicative equations

Given an $r$-colouring of the interval $\{2, \dotsc, N \}$, what is the minimum number of monochromatic solutions of the equation $xy = z$? For $r=2$, we show that there are always asymptotically at least $(1/2\sqrt{2}) N^{1/2} \log N$ monochromatic solutions, and that the leading constant is sharp. We also establish a stability version of this result. For general $r$, we show that there are at least $C_r N^{1/S(r-1)}$ monochromatic solutions, where $S(r)$ is the Schur number for $r$ colours and $C_r$ is a constant. This bound is sharp up to logarithmic factors when $r \leq 4$. We also obtain results for more general multiplicative equations of the form $x_1^{a_1} \dotsb x_k^{a_k} = y$, where $a_1, \dotsc, a_k$ are positive integers, at least one of which equals $1$. Our corresponding upper bounds are given in terms of certain `interval Rado numbers' for additive equations. We pose a number of open problems concerning these numbers.
Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization

We propose novel optimal and parameter-free algorithms for computing an approximate solution with small (projected) gradient norm. Specifically, for computing an approximate solution such that the norm of its (projected) gradient does not exceed $\varepsilon$, we obtain the following results: a) for the convex case, the total number of gradient evaluations is bounded by $O(1)\sqrt{L\|x_0 - x^*\|/\varepsilon}$, where $L$ is the Lipschitz constant of the gradient and $x^*$ is any optimal solution; b) for the strongly convex case, the total number of gradient evaluations is bounded by $O(1)\sqrt{L/\mu}\log(\|\nabla f(x_0)\|/\epsilon)$, where $\mu$ is the strong convexity modulus; and c) for the nonconvex case, the total number of gradient evaluations is bounded by $O(1)\sqrt{Ll}(f(x_0) - f(x^*))/\varepsilon^2$, where $l$ is the lower curvature constant. Our complexity results match the lower complexity bounds of the convex and strongly cases, and achieve the above best-known complexity bound for the nonconvex case for the first time in the literature. Moreover, for all the convex, strongly convex, and nonconvex cases, we propose parameter-free algorithms that do not require the input of any problem parameters. To the best of our knowledge, there do not exist such parameter-free methods before especially for the strongly convex and nonconvex cases. Since most regularity conditions (e.g., strong convexity and lower curvature) are imposed over a global scope, the corresponding problem parameters are notoriously difficult to estimate. However, gradient norm minimization equips us with a convenient tool to monitor the progress of algorithms and thus the ability to estimate such parameters in-situ.
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control

Advanced vehicle control is a fundamental building block in the development of autonomous driving systems. Reinforcement learning (RL) promises to achieve control performance superior to classical approaches while keeping computational demands low during deployment. However, standard RL approaches like soft-actor critic (SAC) require extensive amounts of training data to be collected and are thus impractical for real-world application. To address this issue, we apply recently developed data-efficient deep RL methods to vehicle trajectory control. Our investigation focuses on three methods, so far unexplored for vehicle control: randomized ensemble double Q-learning (REDQ), probabilistic ensembles with trajectory sampling and model predictive path integral optimizer (PETS-MPPI), and model-based policy optimization (MBPO). We find that in the case of trajectory control, the standard model-based RL formulation used in approaches like PETS-MPPI and MBPO is not suitable. We, therefore, propose a new formulation that splits dynamics prediction and vehicle localization. Our benchmark study on the CARLA simulator reveals that the three identified data-efficient deep RL approaches learn control strategies on a par with or better than SAC, yet reduce the required number of environment interactions by more than one order of magnitude.
LayerCollapse: Adaptive compression of neural networks

Handling the ever-increasing scale of contemporary deep learning and transformer-based models poses a significant challenge. Overparameterized Transformer networks outperform prior art in Natural Language processing and Computer Vision. These models contain hundreds of millions of parameters, demanding significant computational resources and making them prone to overfitting. In this work we present LayerCollapse, a form of structured pruning to reduce the depth of fully connected layers. We develop a novel regularizer allowing for post-training compression without finetuning, while having limited impact on performance. LayerCollapse controls model expressiveness with regularization on the activations between fully connected layers, modulating the linearity of activation functions. A linear activation function reduces the rank of the transformation to the rank of the corresponding linear transformation. We demonstrate the effectiveness of LayerCollapse by showing its compression capabilities in sentimental analysis and image classification benchmarks. Moreover we show LayerCollapse is an effective compression aware regularization method in a language modeling benchmark.
Enhancing the security of image transmission in Quantum era: A Chaos-Assisted QKD Approach using entanglement

The emergence of quantum computing has introduced unprecedented security challenges to conventional cryptographic systems, particularly in the domain of optical communications. This research addresses these challenges by innovatively combining quantum key distribution (QKD), specifically the E91 protocol, with logistic chaotic maps to establish a secure image transmission scheme. Our approach utilizes the unpredictability of chaotic systems alongside the robust security mechanisms inherent in quantum entanglement. The scheme is further fortified with an eavesdropping detection mechanism based on CHSH inequality, thereby enhancing its resilience against unauthorized access. Through quantitative simulations, we demonstrate the effectiveness of this scheme in encrypting images, achieving high entropy and sensitivity to the original images. The results indicate a significant improvement in encryption and decryption efficiency, showcasing the potential of the scheme as a viable solution against the vulnerabilities posed by quantum computing advancements. Our research offers a novel perspective in secure optical communications, blending the principles of chaos theory with QKD to create a more robust cryptographic framework.
Predicted $\Xi_b(6087)^0$ and further predictions

The methods of QCD sum rules and light-cone sum rules within the framework of heavy quark effective theory have been widely applied to study the singly heavy baryons, and especially, we have applied these methods to predict not only the mass and width of the $\Xi_b(6087)^0$ recently discovered by LHCb, but also its observation channel and its mass difference from the $\Xi_b(6100)^-$. We apply the same approach to perform a complete study on the $P$-wave bottom baryons of the $SU(3)$ flavor $\mathbf{\bar 3}_F$ and investigate their fine structure. Besides the $\Lambda_b(5912)^0$, $\Lambda_b(5920)^0$, $\Xi_b(6087)^0$, and $\Xi_b(6100)^-$, our results suggest the existence of the other two $\Lambda_b$ and two $\Xi_b$ baryons, whose widths are limited. They are the $\Lambda_b(J^P = 3/2^-)$ with the mass and width about $(5.93^{+0.13}_{-0.13}~{\rm GeV},\,0.0^{+12.0}_{-~0.0}~{\rm MeV})$, the $\Lambda_b(5/2^-)$ with $(5.94^{+0.13}_{-0.13}~{\rm GeV},\,\approx0~{\rm MeV})$, the $\Xi_b(3/2^-)$ with $(6.10^{+0.15}_{-0.10}~{\rm GeV},\,1.4{^{+11.6}_{-~1.4}}~{\rm MeV})$, and the $\Xi_b(5/2^-)$ with $(6.11^{+0.15}_{-0.10}~{\rm GeV},\,1.0{^{+7.4}_{-1.0}}~{\rm MeV})$. Their mass splittings are calculated to be $M_{\Lambda_b(5/2^-)} - M_{\Lambda_b(3/2^-)} = 17\pm7~{\rm MeV}$ and $M_{\Xi_b(5/2^-)} - M_{\Xi_b(3/2^-)} = 14\pm7~{\rm MeV}$. All these baryons are explained as the $P$-wave bottom baryons of the $\rho$-mode, where the orbital excitation is between the two light quarks. However, the existence of this mode is still controversial, so their experimental searches can verify both our approach and the existence of the $\rho$-mode, which can significantly improve our understanding on the internal structure of hadrons.
Analyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answering

Transformer-based language models have been shown to be highly effective for several NLP tasks. In this paper, we consider three transformer models, BERT, RoBERTa, and XLNet, in both small and large versions, and investigate how faithful their representations are with respect to the semantic content of texts. We formalize a notion of semantic faithfulness, in which the semantic content of a text should causally figure in a model's inferences in question answering. We then test this notion by observing a model's behavior on answering questions about a story after performing two novel semantic interventions—deletion intervention and negation intervention. While transformer models achieve high performance on standard question answering tasks, we show that they fail to be semantically faithful once we perform these interventions for a significant number of cases (∼ 50% for deletion intervention, and ∼ 20% drop in accuracy for negation intervention). We then propose an intervention-based training regime that can mitigate the undesirable effects for deletion intervention by a significant margin (from ∼ 50% to ∼ 6%). We analyze the inner-workings of the models to better understand the effectiveness of intervention-based training for deletion intervention. But we show that this training does not attenuate other aspects of semantic unfaithfulness such as the models' inability to deal with negation intervention or to capture the predicate-argument structure of texts. We also test InstructGPT, via prompting, for its ability to handle the two interventions and to capture predicate-argument structure. While InstructGPT models do achieve very high performance on predicate-argument structure task, they fail to respond adequately to our deletion and negation interventions.
A new flat mite of the genus Brevipalpus Donnadieu (Trombidiformes: Tenuipalpidae) from Brazil, with complete ontogenetic pattern of setal additions

Abstract A new species of flat mite (Acari: Tenuipalpidae), Brevipalpus canastra Castro, Beard & Ochoa sp. nov., is described from the adult female, deutonymph, protonymph and larval stages collected on Sabicea brasiliensis (Rubiaceae), in the Cerrado region of Brazil. The new species belongs to the Brevipalpus phoenicis species group. The ontogenetic changes in the idiosoma and leg chaetotaxy of all stages are presented and micrographs using low-temperature scanning electron microscopy (LT-SEM) are provided.