Miracle-dz/UniFAR-FTUs huggingface dataset:
Ins-HOI: Instance Aware Human-Object Interactions Recovery

Accurately modeling detailed interactions between human/hand and object is an appealing yet challenging task. Current multi-view capture systems are only capable of reconstructing multiple subjects into a single, unified mesh, which fails to model the states of each instance individually during interactions. To address this, previous methods use template-based representations to track human/hand and object. However, the quality of the reconstructions is limited by the descriptive capabilities of the templates so that these methods are inherently struggle with geometry details, pressing deformations and invisible contact surfaces. In this work, we propose an end-to-end Instance-aware Human-Object Interactions recovery (Ins-HOI) framework by introducing an instance-level occupancy field representation. However, the real-captured data is presented as a holistic mesh, unable to provide instance-level supervision. To address this, we further propose a complementary training strategy that leverages synthetic data to introduce instance-level shape priors, enabling the disentanglement of occupancy fields for different instances. Specifically, synthetic data, created by randomly combining individual scans of humans/hands and objects, guides the network to learn a coarse prior of instances. Meanwhile, real-captured data helps in learning the overall geometry and restricting interpenetration in contact areas. As demonstrated in experiments, our method Ins-HOI supports instance-level reconstruction and provides reasonable and realistic invisible contact surfaces even in cases of extremely close interaction. To facilitate the research of this task, we collect a large-scale, high-fidelity 3D scan dataset, including 5.2k high-quality scans with real-world human-chair and hand-object interactions. The code and data will be public for research purposes.
A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification

Self-supervised learning addresses the challenge encountered by many supervised methods, i.e. the requirement of large amounts of annotated data. This challenge is particularly pronounced in fields such as the electroencephalography (EEG) research domain. Self-supervised learning operates instead by utilizing pseudo-labels, which are generated by pretext tasks, to obtain a rich and meaningful data representation. In this study, we aim at introducing a dual-stream pretext task architecture that operates both in the time and frequency domains. In particular, we have examined the incorporation of the novel Frequency Similarity (FS) pretext task into two existing pretext tasks, Relative Positioning (RP) and Temporal Shuffling (TS). We assess the accuracy of these models using the Physionet Challenge 2018 (PC18) dataset in the context of the downstream task sleep stage classification. The inclusion of FS resulted in a notable improvement in downstream task accuracy, with a 1.28 percent improvement on RP and a 2.02 percent improvement on TS. Furthermore, when visualizing the learned embeddings using Uniform Manifold Approximation and Projection (UMAP), distinct clusters emerge, indicating that the learned representations carry meaningful information.
Verification-Friendly Deep Neural Networks

Machine learning techniques often lack formal correctness guarantees. This is evidenced by the widespread adversarial examples that plague most deep-learning applications. This resulted in several research efforts that aim at verifying deep neural networks, with a particular focus on safety-critical applications. However, formal verification techniques still face major scalability and precision challenges when dealing with the complexity of such networks. The over-approximation introduced during the formal verification process to tackle the scalability challenge often results in inconclusive analysis. To address this challenge, we propose a novel framework to generate Verification-friendly Neural Networks (VNNs). We present a post-training optimization framework to achieve a balance between preserving prediction performance and robustness in the resulting networks. Our proposed framework proves to result in networks that are comparable to the original ones in terms of prediction performance, while amenable to verification. This essentially enables us to establish robustness for more VNNs than their deep neural network counterparts, in a more time-efficient manner.
An Analytical Cost-Optimal Cloudlet Placement Framework over Fiber-Wireless Networks with Quasi-Convex Latency Constraint

To meet the low-latency constraints arising from future smart mobile devices, Internet-of-Things, and 5G applications, major interest is currently given to the integration of centralized cloud computing and distributed edge computing infrastructures to deliver higher performance and reliability to edge devices in accessing mobile cloud services. The three-tier network architecture arising from cloud, cloudlet, and edge-devices can handle miscellaneous latency requirements for both latency-sensitive and latency-tolerant applications more efficiently than conventional two-tier networks. In this paper, we primarily focus on the static cloudlet network planning problem and propose an analytical hybrid cost-optimization framework for optimal cloudlet placement. We formulate this problem as a convex optimization problem and solve by using Karush-Kuhn-Tucker (KKT) conditions, and show that this framework can be evaluated without any scalability issues observed with integer programming based frameworks for large datasets. Moreover, we derive user-friendly closed form expressions that provide a first-hand estimation of cloudlet deployment cost depending on a few important network parameters like split-ratio, population density, and network bandwidth. Finally, we also show that the optimal solution of this analytical framework can be considered as a tight lower bound of the optimal solutions of integer programming based frameworks and makes a better cloudlet installation cost estimation compared to other existing frameworks.
Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models

Binary code summarization, while invaluable for understanding code semantics, is challenging due to its labor-intensive nature. This study delves into the potential of large language models (LLMs) for binary code comprehension. To this end, we present BinSum, a comprehensive benchmark and dataset of over 557K binary functions and introduce a novel method for prompt synthesis and optimization. To more accurately gauge LLM performance, we also propose a new semantic similarity metric that surpasses traditional exact-match approaches. Our extensive evaluation of prominent LLMs, including ChatGPT, GPT-4, Llama 2, and Code Llama, reveals 10 pivotal insights. This evaluation generates 4 billion inference tokens, incurred a total expense of 11,418 US dollars and 873 NVIDIA A100 GPU hours. Our findings highlight both the transformative potential of LLMs in this field and the challenges yet to be overcome.
Duality of Hoffman constants

Suppose $A\in \mathbb{R}^{m\times n}$ and consider the following canonical systems of inequalities defined by $A$: $$ \begin{array}{l} Ax=b\\ x \ge 0 \end{array} \qquad \text{ and }\qquad A^T y - c \le 0. $$ We establish some novel duality relationships between the Hoffman constants for the above constraint systems of linear inequalities provided some suitable Slater condition holds. The crux of our approach is a Hoffman duality inequality for polyhedral systems of constraints. The latter in turn yields an interesting duality identity between the Hoffman constants of the following box-constrained systems of inequalities: $$ \begin{array}{l} Ax=b\\ \ell \le x \le u \end{array}\qquad \text{ and }\qquad \ell \le A^T y - c \le u $$ for $\ell, u\in \mathbb{R}^n$ with $\ell
Quantifying macrostructures in viscoelastic sub-diffusive flows

We present a theory to quantify the formation of spatiotemporal macrostructures (or the non-homogeneous regions of high viscosity at moderate to high fluid inertia) for viscoelastic sub-diffusive flows, by introducing a mathematically consistent decomposition of the polymer conformation tensor, into the so-called structure tensor. Our approach bypasses an inherent problem in the standard arithmetic decomposition, namely, the fluctuating conformation tensor fields may not be positive definite and hence, do not retain their physical meaning. Using well-established results in matrix analysis, the space of positive definite matrices is transformed into a Riemannian manifold by defining and constructing a geodesic via the inner product on its tangent space. This geodesic is utilized to define three scalar invariants of the structure tensor, which do not suffer from the caveats of the regular invariants (such as trace and determinant) of the polymer conformation tensor. First, we consider the problem of formulating perturbative expansions of the structure tensor using the geodesic, which is consistent with the Riemannian manifold geometry. A constraint on the maximum time, during which the evolution of the perturbative solution can be well approximated by linear theory along the Euclidean manifold, is found. Finally, direct numerical simulations of the viscoelastic sub-diffusive channel flows (where the stress-constitutive law is obtained via coarse-graining the polymer relaxation spectrum at finer scale, Chauhan et. al., Phys. Fluids, DOI: 10.1063/5.0174598 (2023)), underscore the advantage of using these invariants in effectively quantifying the macrostructures.
Automating reward function configuration for drug design

Designing reward functions that guide generative molecular design (GMD) algorithms to desirable areas of chemical space is of critical importance in AI-driven drug discovery. Traditionally, this has been a manual and error-prone task; the selection of appropriate computational methods to approximate biological assays is challenging and the aggregation of computed values into a single score even more so, leading to potential reliance on trial-and-error approaches. We propose a novel approach for automated reward configuration that relies solely on experimental data, mitigating the challenges of manual reward adjustment on drug discovery projects. Our method achieves this by constructing a ranking over experimental data based on Pareto dominance over the multi-objective space, then training a neural network to approximate the reward function such that rankings determined by the predicted reward correlate with those determined by the Pareto dominance relation. We validate our method using two case studies. In the first study we simulate Design-Make-Test-Analyse (DMTA) cycles by alternating reward function updates and generative runs guided by that function. We show that the learned function adapts over time to yield compounds that score highly with respect to evaluation functions taken from the literature. In the second study we apply our algorithm to historical data from four real drug discovery projects. We show that our algorithm yields reward functions that outperform the predictive accuracy of human-defined functions, achieving an improvement of up to 0.4 in Spearman's correlation against a ground truth evaluation function that encodes the target drug profile for that project. Our method provides an efficient data-driven way to configure reward functions for GMD, and serves as a strong baseline for future research into transformative approaches for the automation of drug discovery.
iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning

Leveraging the potential of Virtualised Network Functions (VNFs) requires a clear understanding of the link between resource consumption and performance. The current state of the art tries to do that by utilising Machine Learning (ML) and specifically Supervised Learning (SL) models for given network environments and VNF types assuming single-objective optimisation targets. Taking a different approach poses a novel VNF profiler optimising multi-resource type allocation and performance objectives using adapted Reinforcement Learning (RL). Our approach can meet Key Performance Indicator (KPI) targets while minimising multi-resource type consumption and optimising the VNF output rate compared to existing single-objective solutions. Our experimental evaluation with three real-world VNF types over a total of 39 study scenarios (13 per VNF), for three resource types (virtual CPU, memory, and network link capacity), verifies the accuracy of resource allocation predictions and corresponding successful profiling decisions via a benchmark comparison between our RL model and SL models. We also conduct a complementary exhaustive search-space study revealing that different resources impact performance in varying ways per VNF type, implying the necessity of multi-objective optimisation, individualised examination per VNF type, and adaptable online profile learning, such as with the autonomous online learning approach of iOn-Profiler.
Gene-MOE: A Sparsely-gated Framework for Pan-Cancer Genomic Analysis

Analyzing the genomic information from the Pan-Cancer database can help us understand cancer-related factors and contribute to the cancer diagnosis and prognosis. However, existing computational methods and deep learning methods can not effectively find the deep correlations between tens of thousands of genes, which leads to precision loss. In this paper, we proposed a novel pretrained model called Gene-MOE to learn the general feature representations of the Pan-Cancer dataset and transfer the pretrained weights to the downstream tasks. The Gene-MOE fully exploits the mixture of expert (MOE) layers to learn rich feature representations of high-dimensional genes. At the same time, we build a mixture of attention expert (MOAE) model to learn the deep semantic relationships within genetic features. Finally, we proposed a new self-supervised pretraining strategy including loss function design, data enhancement, and optimization strategy to train the Gene-MOE and further improve the performance for the downstream analysis. We carried out cancer classification and survival analysis experiments based on the Gene-MOE. According to the survival analysis results on 14 cancer types, using Gene-MOE outperformed state-of-the-art models on 12 cancer types. According to the classification results, the total accuracy of the classification model for 33 cancer classifications reached 95.2%. Through detailed feature analysis, we found the Gene-MOE model can learn rich feature representations of high-dimensional genes.
The cosmological constant and scale hierarchies with emergent gauge symmetries

Motivated by the stability of the electroweak Higgs vacuum we consider the possibility that the Standard Model might work up to large scales between about 1010 GeV and close to the Planck scale. A plausible scenario is an emergent Standard Model with gauge symmetries originating in some topological-like phase transition deep in the ultraviolet. In this case, the cosmological constant scale and neutrino masses should be of similar size, suppressed by factor of the large scale of emergence. The key physics involves a subtle interplay of Poincaré invariance, mass generation and renormalization group invariance. The Higgs mass would be environmentally selected in connection with vacuum stability. Consequences for dark matter scenarios are discussed. This article is part of the theme issue ‘The particle-gravity frontier’.
Insights into the flame transitions and flame stabilization mechanisms in a freely falling burning droplet encountering a co-flow

Abstract The present study investigates the flame dynamics of a contactless burning fuel droplet under free fall subjected to a co-flow. The dynamic external relative flow established due to co-flow and droplet acceleration results in a series of droplet flame transitions. Different flame structures were observed, including a wake flame, reversed wake flame and enveloped flame. Following ignition, the droplet is allowed to fall through the central tube of a co-flow arrangement, and, at its exit, the droplet flame encounters the co-flow. The wake flame, which was established based on the droplet's instantaneous velocity of descent, encounters the abrupt relative velocity jump due to the co-flow. This causes the droplet flame to go through various transitions as it approaches equilibrium with the surrounding flow. Once it equilibrates, the droplet flame evolves in response to the instantaneous relative flow velocity. The droplet flame evolves by altering both its shape and the stabilization mechanism. Two stabilization mechanisms were identified for the droplet wake flame: edge-flame stabilization and bluff-body stabilization. The stabilization mechanism for different flame structures and the transition events have been theoretically analysed, and the relation between flame shape evolution and flow velocity has been determined based on the flow-field characteristics at the corresponding Re (Reynolds number) range. Furthermore, these correlations are employed in a mathematical formulation based on the spring–mass analogy, which predicts the droplet flame evolution after encountering the co-flow, including all the transition events.
Improving Utilization of Dataflow Unit for Multi-Batch Processing.

Dataflow architectures can achieve much better performance and higher efficiency than general-purpose core, approaching the performance of a specialized design while retaining programmability. However, advanced application scenarios place higher demands on the hardware in terms of cross-domain and multi-batch processing. In this paper, we propose a unified scale-vector architecture that can work in multiple modes and adapt to diverse algorithms and requirements efficiently. First, a novel reconfigurable interconnection structure is proposed, which can organize execution units into different cluster typologies as a way to accommodate different data-level parallelism. Second, we decouple threads within each DFG node into consecutive pipeline stages and provide architectural support. By time-multiplexing during these stages, dataflow hardware can achieve much higher utilization and performance. In addition, the task-based program model can also exploit multi-level parallelism and deploy applications efficiently. Evaluated in a wide range of benchmarks, including digital signal processing algorithms, CNNs, and scientific computing algorithms, our design attains up to 11.95 × energy efficiency (performance-per-watt) improvement over GPU (V100), and 2.01 × energy efficiency improvement over state-of-the-art dataflow architectures.
Protein search processes mediated by chromatin topology

We investigate the role of compaction of chromatin domains in modulating search kinetics of proteins. Collapsed conformations of chromatin, characterised by long loops which bring distant regions of the genome into contact, and manifested structurally as Topologically Associated Domains (TADs) affect search kinetics of DNA associated transcription factors and other proteins. In this study, we investigate the role of the compactness of chromatin on the dynamics of proteins using a minimal model. Using theory and simulations, we show that an optimal compaction exists for which the residence time of proteins on a chromatin-like polymer backbone is minimum. We then show that the fraction of unique sites visited also shows a similar non-monotonic behavior as a function of the polymer compaction. We extend these results to more detailed polymer models - using the Freely Rotating Chain model, a Lennard-Jones bead-spring polymer, and a soft polymer chain model, which approximates chromatin behavior. We show that our results continue to hold for these polymer models, with a minimum residence time at an optimum polymer compaction. Finally, we also analyse the dynamics of proteins on networks generated using experimental chromatin conformation data for several TADs extracted from human chromosomes. Our analysis suggests that TADs exist near this zone of optimality, indicating that chromatin conformations can play a crucial role in modulating protein search strategies.
NeMu: A Comprehensive Pipeline for Accurate Reconstruction of Neutral Mutation Spectra from Evolutionary Data

One of the most important characteristics of each contemporary model of molecular evolution is the assumption that mutations occur in a constant manner; however, in the real world, the mutations are determined by the combination of the effects of DNA replication and repair. This affects the nucleotide composition of the genome and guides not just neutral but adaptive evolution1. Mutation accumulation experiments are the de facto standard for the neutral mutation spectra estimation. However, recent studies have demonstrated that the mutation fraction under selection pressure is significantly underestimated in mutation accumulation experiments, and, therefore the precise extraction of neutral mutation spectra from mutation accumulation experiments is not trivial2. To unravel the neutral mutation spectra, it is very important to analyze all the mutations available in depth, based on the evolutionary timescale, taking into consideration all the existing knowledge. In order to facilitate this analysis, we have created a novel pipeline, called NeMu (https://biopipelines.kantiana.ru/nemu/).