Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing the Need for Backpropagation and Discovering Better Optima With Explicit Optimizations of Neural Networks

Iterative differential approximation methods that rely upon backpropagation have enabled the optimization of neural networks; however, at present, they remain computationally expensive, especially when training models at scale. In this paper, we propose a computationally efficient alternative for optimizing neural networks that can both reduce the costs of scaling neural networks and provide high-efficiency optimizations for low-resource applications. We derive an explicit solution to a simple feed-forward language model (LM) by mathematically analyzing its gradients. This solution generalizes from single-layer LMs to the class of all single-layer feed-forward softmax-activated neural models trained on positive-valued features, as is demonstrated by our extension of this solution application to MNIST digit classification. For both LM and digit classifiers, we find computationally that explicit solutions perform near-optimality in experiments showing that 1) iterative optimization only marginally improves the explicit solution parameters and 2) randomly initialized parameters iteratively optimize towards the explicit solution. We also preliminarily apply the explicit solution locally by layer in multi-layer networks and discuss how the solution's computational savings increase with model complexity -- for both single- and mult-layer applications of the explicit solution, we emphasize that the optima achieved cannot be reached by backpropagation alone, i.e., better optima appear discoverable only after explicit solutions are applied. Finally, we discuss the solution's computational savings alongside its impact on model interpretability and suggest future directions for the derivation of explicit solutions to complex- and multi-layer architectures.
Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and Audio

While 3D human body modeling has received much attention in computer vision, modeling the acoustic equivalent, i.e. modeling 3D spatial audio produced by body motion and speech, has fallen short in the community. To close this gap, we present a model that can generate accurate 3D spatial audio for full human bodies. The system consumes, as input, audio signals from headset microphones and body pose, and produces, as output, a 3D sound field surrounding the transmitter's body, from which spatial audio can be rendered at any arbitrary position in the 3D space. We collect a first-of-its-kind multimodal dataset of human bodies, recorded with multiple cameras and a spherical array of 345 microphones. In an empirical evaluation, we demonstrate that our model can produce accurate body-induced sound fields when trained with a suitable loss. Dataset and code are available online.
BClean: A Bayesian Data Cleaning System

There is a considerable body of work on data cleaning which employs various principles to rectify erroneous data and transform a dirty dataset into a cleaner one. One of prevalent approaches is probabilistic methods, including Bayesian methods. However, existing probabilistic methods often assume a simplistic distribution (e.g., Gaussian distribution), which is frequently underfitted in practice, or they necessitate experts to provide a complex prior distribution (e.g., via a programming language). This requirement is both labor-intensive and costly, rendering these methods less suitable for real-world applications. In this paper, we propose BClean, a Bayesian Cleaning system that features automatic Bayesian network construction and user interaction. We recast the data cleaning problem as a Bayesian inference that fully exploits the relationships between attributes in the observed dataset and any prior information provided by users. To this end, we present an automatic Bayesian network construction method that extends a structure learning-based functional dependency discovery method with similarity functions to capture the relationships between attributes. Furthermore, our system allows users to modify the generated Bayesian network in order to specify prior information or correct inaccuracies identified by the automatic generation process. We also design an effective scoring model (called the compensative scoring model) necessary for the Bayesian inference. To enhance the efficiency of data cleaning, we propose several approximation strategies for the Bayesian inference, including graph partitioning, domain pruning, and pre-detection. By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0.9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
Bridging the Sim-to-Real Gap with Dynamic Compliance Tuning for Industrial Insertion

Contact-rich manipulation tasks often exhibit a large sim-to-real gap. For instance, industrial assembly tasks frequently involve tight insertions where the clearance is less than 0.1 mm and can even be negative when dealing with a deformable receptacle. This narrow clearance leads to complex contact dynamics that are difficult to model accurately in simulation, making it challenging to transfer simulation-learned policies to real-world robots. In this paper, we propose a novel framework for robustly learning manipulation skills for real-world tasks using simulated data only. Our framework consists of two main components: the"Force Planner"and the"Gain Tuner". The Force Planner plans both the robot motion and desired contact force, while the Gain Tuner dynamically adjusts the compliance control gains to track the desired contact force during task execution. The key insight is that by dynamically adjusting the robot's compliance control gains during task execution, we can modulate contact force in the new environment, thereby generating trajectories similar to those trained in simulation and narrowing the sim-to-real gap. Experimental results show that our method, trained in simulation on a generic square peg-and-hole task, can generalize to a variety of real-world insertion tasks involving narrow and negative clearances, all without requiring any fine-tuning. Videos are available at https://dynamic-compliance.github. io.
Hunting WIMPs with LISA: Correlating dark matter and gravitational wave signals

The thermal freeze-out mechanism in its classical form is tightly connected to physics beyond the Standard Model around the electroweak scale, which has been the target of enormous experimental efforts. In this work we study a dark matter model in which freeze-out is triggered by a strong first-order phase transition in a dark sector, and show that this phase transition must also happen close to the electroweak scale, i.e. in the temperature range relevant for gravitational wave searches with the LISA mission. Specifically, we consider the spontaneous breaking of a $U(1)^\prime$ gauge symmetry through the vacuum expectation value of a scalar field, which generates the mass of a fermionic dark matter candidate that subsequently annihilates into dark Higgs and gauge bosons. In this set-up the peak frequency of the gravitational wave background is tightly correlated with the dark matter relic abundance, and imposing the observed value for the latter implies that the former must lie in the milli-Hertz range. A peculiar feature of our set-up is that the dark sector is not necessarily in thermal equilibrium with the Standard Model during the phase transition, and hence the temperatures of the two sectors evolve independently. Nevertheless, the requirement that the universe does not enter an extended period of matter domination after the phase transition, which would strongly dilute any gravitational wave signal, places a lower bound on the portal coupling that governs the entropy transfer between the two sectors. As a result, the predictions for the peak frequency of gravitational waves in the LISA band are robust, while the amplitude can change depending on the initial dark sector temperature.
Nonsmooth-Optimization-Based Bandwidth Optimal Control for Precision Motion Systems

Precision motion systems are at the core of various manufacturing equipment. The rapidly increasing demand for higher productivity necessitates higher control bandwidth in the motion systems to effectively reject disturbances while maintaining excellent positioning accuracy. However, most existing optimal control methods do not explicitly optimize for control bandwidth, and the classic loop-shaping method suffers from conservative designs and fails to address cross-couplings, which motivates the development of new control solutions for bandwidth optimization. This paper proposes a novel bandwidth optimal control formulation based on nonsmooth optimization for precision motion systems. Our proposed method explicitly optimizes the system's MIMO control bandwidth while constraining the H-infinity norm of the closed-loop sensitivity function for robustness. A nonsmooth optimization solver, GRANSO, is used to solve the proposed program, and an augmented quadratic programming (QP)--based descent direction search is proposed to facilitate convergence. Simulation evaluations show that the bandwidth optimal control method can achieve a 23% higher control bandwidth than conventional loop-shaping design, and the QP-based descent direction search can reduce iteration number by 60%, which illustrates the effectiveness and efficiency of the proposed approach.
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning

To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Flows (NFs). However, we find that prior work with NFs attempts to match the complex target distribution topologically with naive base distributions leading to adverse implications. In this work, we circumvent this topological mismatch using an expressive class-conditional base distribution trained with an information-theoretic objective to match the required topology. The proposed method enjoys the merits of wide compatibility with existing learned models without any performance degradation and minimum computation overhead while enhancing OOD detection capabilities. We demonstrate superior results in density estimation and 2D object detection benchmarks in comparison with extensive baselines. Moreover, we showcase the applicability of the method with a real-robot deployment.
A Template Is All You Meme

Memes are a modern form of communication and meme templates possess a base semantics that is customizable by whomever posts it on social media. Machine learning systems struggle with memes, which is likely due to such systems having insufficient context to understand memes, as there is more to memes than the obvious image and text. Here, to aid understanding of memes, we release a knowledge base of memes and information found on www.knowyourmeme. com, which we call the Know Your Meme Knowledge Base (KYMKB), composed of more than 54,000 images. The KYMKB includes popular meme templates, examples of each template, and detailed information about the template. We hypothesize that meme templates can be used to inject models with the context missing from previous approaches. To test our hypothesis, we create a non-parametric majority-based classifier, which we call Template-Label Counter (TLC). We find TLC more effective than or competitive with fine-tuned baselines. To demonstrate the power of meme templates and the value of both our knowledge base and method, we conduct thorough classification experiments and exploratory data analysis in the context of five meme analysis tasks.
Rings Additively Generated by Periodic Elements

In the present paper, as a generalization of the classical periodic rings, we explore those rings whose elements are additively generated by two (or more) periodic elements by calling them additively periodic. We prove that, in some major cases, additively periodic rings remain periodic too; this includes, for instance, algebraic algebras, group rings, and matrix rings over commutative rings. Moreover, we also obtain some independent results for the new class of rings; for example, the triangular matrix rings retain that property.
Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis

The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing its value across different areas, our study investigates ChatGPT's capacity to predict stock market movements using only social media tweets and sentiment analysis. We aim to see if ChatGPT can tap into the vast sentiment data on platforms like Twitter to offer insightful predictions about stock trends. We focus on determining if a tweet has a positive, negative, or neutral effect on two big tech giants Microsoft and Google's stock value. Our findings highlight a positive link between ChatGPT's evaluations and the following days stock results for both tech companies. This research enriches our view on ChatGPT's adaptability and emphasizes the growing importance of AI in shaping financial market forecasts.
Omitted Labels in Causality: A Study of Paradoxes

We explore what we call ``omitted label contexts,'' in which training data is limited to a subset of the possible labels. This setting is common among specialized human experts or specific focused studies. We lean on well-studied paradoxes (Simpson's and Condorcet) to illustrate the more general difficulties of causal inference in omitted label contexts. Contrary to the fundamental principles on which much of causal inference is built, we show that ``correct'' adjustments sometimes require non-exchangeable treatment and control groups. These pitfalls lead us to the study networks of conclusions drawn from different contexts and the structures the form, proving an interesting connection between these networks and social choice theory.
Open-Set Graph Anomaly Detection via Normal Structure Regularisation

This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to detect anomalous nodes using a small number of labelled training normal and anomaly nodes (known as seen anomalies) that cannot illustrate all possible inference-time abnormalities. The availability of that labelled data provides crucial prior knowledge about abnormalities for GAD models, enabling substantially reduced detection errors. However, current methods tend to over-emphasise fitting the seen anomalies, leading to a weak generalisation ability to detect unseen anomalies, i.e., those that are not illustrated by the labelled anomaly nodes. Further, they were introduced to handle Euclidean data, failing to effectively capture important non-Euclidean features for GAD. In this work, we propose a novel open-set GAD approach, namely Normal Structure Regularisation (NSReg), to achieve generalised detection ability to unseen anomalies, while maintaining its effectiveness on detecting seen anomalies. The key idea in NSReg is to introduce a regularisation term that enforces the learning of compact, semantically-rich representations of normal nodes based on their structural relations to other nodes. When being optimised with supervised anomaly detection losses, the regularisation term helps incorporate strong normality into the modelling, empowering the joint learning of both seen abnormality and normality of the nodes, and thus, it effectively avoids the over emphasis on solely fitting the seen anomalies during training. Extensive empirical results on six real-world datasets demonstrate the superiority of our proposed NSReg for open-set GAD.
Unveiling Human Factors and Message Attributes in a Smishing Study

With the booming popularity of smartphones, threats related to these devices are increasingly on the rise. Smishing, a combination of SMS (Short Message Service) and phishing has emerged as a treacherous cyber threat used by malicious actors to deceive users, aiming to steal sensitive information, money or install malware on their mobile devices. Despite the increase in smishing attacks in recent years, there are very few studies aimed at understanding the factors that contribute to a user's ability to differentiate real from fake messages. To address this gap in knowledge, we have conducted an online survey on smishing detection with 214 participants. In this study, we presented them with 16 SMS screenshots and evaluated how different factors affect their decision making process in smishing detection. Next, we conducted a follow-up survey to garner information on the participants' security attitudes, behavior and knowledge. Our results highlighted that attention and security behavioral scores had a significant impact on participants' accuracy in identifying smishing messages. Interestingly, we found that participants had more difficulty identifying real messages from fake ones, with an accuracy of 65.6% with fake messages and 44.6% with real messages. Our study is crucial in developing proactive strategies to encounter and mitigate smishing attacks. By understanding what factors influence smishing detection, we aim to bolster users' resilience against such threats and create a safer digital environment for all.
Optimality Conditions For Multi-Objective Interval-Valued Optimization Problem On Hadamard Manifolds

The KKT optimality conditions for multi-objective interval-valued optimization problem on Hadamard manifold are studied in this paper. Several concepts of Pareto optimal solutions, considered under LU and CW ordering on the class of all closed intervals in $\mathbb{R}$, are given. The KKT conditions are presented under the notions of convexity, pseudo-convexity and generalized Hukuhara difference. We show, with the help of an example, that the results done in this paper for solving multi-objective interval-valued optimization problems on Hadamard spaces are more general than the existing ones on Euclidean spaces. The main results are supported by examples.
Opening the species box: What microscopic models of neutral speciation have to say about macroevolution

In the last two decades, lineage-based models of diversification, where species are viewed as particles that can divide (speciate) or die (become extinct) at rates depending on some evolving trait, have been very popular tools to study macroevolutionary processes. Here, we argue that this approach cannot be used to break down the inner workings of species diversification and that “opening the species box” is necessary to understand the causes of macroevolution. We set up a general framework for individual-based models of neutral speciation (i.e. no selection forces other than those acting against hybrids) that rely on a minimal number of mechanistic principles: (i) reproductive isolation is caused by excessive dissimilarity between pheno/genotypes; (ii) dissimilarity results from a balance between differentiation processes and homogenization processes; and (iii) dissimilarity can feed back on these processes by decelerating homogenization. We classify such models according to the main process responsible for homogenization: (1) clonal evolution models (ecological drift), (2) models of genetic isolation (gene flow) and (3) models of isolation by distance (spatial drift). We review these models and their specific predictions on macroscopic variables such as species abundances, speciation rates, interfertility relationships, phylogenetic tree structure… We propose new avenues of research by displaying conceptual questions remaining to be solved and new models to address them: the failure of speciation at secondary contact, the feedback of dissimilarity on homogenization, the emergence in space of reproductive barriers.
m6A RNA modification of mHtt intron 1 regulates the generation of Htt1a in Huntington’s Disease

Huntington’s disease (HD) is a dominantly inherited neurodegenerative disorder caused by an expanded, somatically unstable CAG repeat in the first exon of the huntingtin gene (HTT). In the presence of an expanded CAG repeat, huntingtin mRNA undergoes an aberrant processing that generates HTT1a transcripts with exon 1 and intron 1 sequences, which encodes the aggregation-prone and pathogenic HTTexon 1 protein. The regulatory mechanisms that contribute to the production of HTT1a are not fully understood. In a previous transcriptome-wide m6A landscape study performed in Hdh+/Q111 knock-in mice, we have found that the proximal region of intron 1 to exon1-intron 1 splice site in Htt RNA is highly modified by m6A. Several pieces of evidence have demonstrated that m6A is involved in RNA processing and splicing. Therefore, in this study we set out to explore the impact of m6A RNA modifications in the generation of Htt1a. We show in the striatum of Hdh+/Q111 mice that m6A is enriched in intronic sequences 5’ to the cryptic poly (A) sites (IpA1 and IpA2) at 680 and 1145 bp into intron 1 as well as in Htt1a polyadenylated mRNA. We also verified the presence of specific m6A-modified sites near the 5’ exon1-intron1 splice donor site. Intronic HTT m6A methylation was recapitulated in human samples showing a significantly increased methylation ratio in HD putamen post-mortem samples and in HD fibroblast cell lines from pre-symptomatic and symptomatic patients. In order to test the hypothesis that the m6A modification is involved in mutant Htt RNA processing, we performed a pharmacological inhibition of METTL3 and a targeted demethylation of Htt intron 1 in HD cells using a dCas13-ALKBH5 system. We found that Htt1a transcript levels in HD cells are regulated by METTL3 and by methylation status in Htt intron 1. Site-specific manipulation with an RNA editing system resulted in decreased expression levels of Htt1a, which was accompanied by a reduction in DNA damage, a major hallmark in HD. Finally, we propose that m6A methylation in intron 1 is likely dependent on the expanded CAG repeats. These findings provide insight into the role of m6A in the generation of the aberrantly spliced mutant Htt transcripts with important implications for therapeutic strategies.
A Survey of Trust Management for Internet of Things

Internet of Things (IoT) is a network of devices that communicate with each other through the internet and provide intelligence to industry and people. These devices are running in potentially hostile environments, so the need for security is critical. Trust management aims to ensure the reliability of the network by assigning a trust value to every node, indicating its trust level. In this paper, we systematically review and analyze the current state of the art in trust management appraoches for IoT. We provide a classification into nine categories based on the tools, methods, and technologies used to form trust management techniques (collect information for trust formation, compute, and store the trust values). We also discuss the limitations and strengths of each category, as well as the open challenges and future research directions. We aim to help the reader understand the current challenges of the field, design a solid trust management system, and navigate through the literature.
Parallel Symbolic Cholesky Factorization

We present a hybrid sequential/parallel symbolic Cholesky factorization algorithm that computes the sparsity pattern of the symbolic factors in parallel. We evaluate the performance on a large subset of the SuiteSparse matrix collection and multicore CPUs as well as flagship GPUs by AMD and NVIDIA, achieving speedups of an order of magnitude compared to a state-of-the-art sequential symbolic Cholesky factorization.