Miracle-dz/UniFAR-FTUs huggingface dataset:
The Time Complexity of Fully Sparse Matrix Multiplication

What is the time complexity of matrix multiplication of sparse integer matrices with $m_{in}$ nonzeros in the input and $m_{out}$ nonzeros in the output? This paper provides improved upper bounds for this question for almost any choice of $m_{in}$ vs. $m_{out}$, and provides evidence that these new bounds might be optimal up to further progress on fast matrix multiplication. Our main contribution is a new algorithm that reduces sparse matrix multiplication to dense (but smaller) rectangular matrix multiplication. Our running time thus depends on the optimal exponent $\omega(a,b,c)$ of multiplying dense $n^a\times n^b$ by $n^b\times n^c$ matrices. We discover that when $m_{out}=\Theta(m_{in}^r)$ the time complexity of sparse matrix multiplication is $O(m_{in}^{\sigma+\epsilon})$, for all $\epsilon>0$, where $\sigma$ is the solution to the equation $\omega(\sigma-1,2-\sigma,1+r-\sigma)=\sigma$. No matter what $\omega(\cdot,\cdot,\cdot)$ turns out to be, and for all $r\in(0,2)$, the new bound beats the state of the art, and we provide evidence that it is optimal based on the complexity of the all-edge triangle problem. In particular, in terms of the input plus output size $m = m_{in} + m_{out}$ our algorithm runs in time $O(m^{1.3459})$. Even for Boolean matrices, this improves over the previous $m^{\frac{2\omega}{\omega+1}+\epsilon}=O(m^{1.4071})$ bound [Amossen, Pagh; 2009], which was a natural barrier since it coincides with the longstanding bound of all-edge triangle in sparse graphs [Alon, Yuster, Zwick; 1994]. We find it interesting that matrix multiplication can be solved faster than triangle detection in this natural setting. In fact, we establish an equivalence to a special case of the all-edge triangle problem.
A Perceptron-based Fine Approximation Technique for Linear Separation

This paper presents a novel online learning method that aims at finding a separator hyperplane between data points labelled as either positive or negative. Since weights and biases of artificial neurons can directly be related to hyperplanes in high-dimensional spaces, the technique is applicable to train perceptron-based binary classifiers in machine learning. In case of large or imbalanced data sets, use of analytical or gradient-based solutions can become prohibitive and impractical, where heuristics and approximation techniques are still applicable. The proposed method is based on the Perceptron algorithm, however, it tunes neuron weights in just the necessary extent during searching the separator hyperplane. Due to an appropriate transformation of the initial data set we need not to consider data labels, neither the bias term. respectively, reducing separability to a one-class classification problem. The presented method has proven converge; empirical results show that it can be more efficient than the Perceptron algorithm, especially, when the size of the data set exceeds data dimensionality.
Robot Parkour Learning

Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse but blind locomotion skills or vision-based but specialized skills by using reference animal data or complex rewards. However, autonomous parkour requires robots to learn generalizable skills that are both vision-based and diverse to perceive and react to various scenarios. In this work, we propose a system for learning a single end-to-end vision-based parkour policy of diverse parkour skills using a simple reward without any reference motion data. We develop a reinforcement learning method inspired by direct collocation to generate parkour skills, including climbing over high obstacles, leaping over large gaps, crawling beneath low barriers, squeezing through thin slits, and running. We distill these skills into a single vision-based parkour policy and transfer it to a quadrupedal robot using its egocentric depth camera. We demonstrate that our system can empower two different low-cost robots to autonomously select and execute appropriate parkour skills to traverse challenging real-world environments.
'Only' without its scales

This work deals with one specific aspect of the semantics of the exclusive adverb only, namely its scalarity. It is argued that, contra several analyses, it is not necessary to postulate that only conventionally encodes a scalar constraint on its associate. Rather, I argue in favor of the idea that the scalar effects that can be observed in some uses of only are pragmatic by-products of its core exclusive meaning. This position is essentially defended in the light of data where only intuitively marks a high point on a scale, rather than a low point as is usually assumed. This data proves to be problematic for all accounts that encode scalarity directly into only. This article raises some empirical and theoretical issues regarding the treatment of the adverb only as a scalar element. While it is not denied that the use of only can be accompanied by scalar effects, I argue that these are by-products of a simple exclusive semantics. I begin by reviewing several analyses of only with a specific emphasis on their treatment of the examples that motivate a scalar account of only (Sect. 1). I then present some data that weakens the scalarity claim behind only, and provides counter-examples to some analyses (Sect. 2). In a final section (Sect. 3), I propose a remedial analysis based on a simpler semantics for only and then go on to explain how scalar effects can be derived as the result of pragmatic inferences. 1 The semantics of only and its scalar component This section aims at presenting the arguments behind a treatment of only as encoding a scalar component. I introduce the elements that form the semantics of only by beginning with the less controversial ones. I then present various analyses of only and focus on their treatment of the core “scalar” examples. ∗I thank the audience at the workshop ’Formal approaches on discourse particles’ at ESSLLI 11 for comments on this work. I am also heavily indebted to my two reviewers for the contents of this article. Finally, I thank Matthijs Westera for informing me on the subtleties of Dutch. All errors and mistakes remain my own.
Anisotropy-assisted magnon condensation in ferromagnetic thin films

We theoretically demonstrate that adding an easy-axis magnetic anisotropy facilitates magnon condensation in thin yttrium iron garnet (YIG) films. Dipolar interactions in a quasi-equilibrium state stabilize room-temperature magnon condensation in YIG. Even though the out-of-plane easy-axis anisotropy generally competes with the dipolar interactions, we show that adding such magnetic anisotropy may even assist the generation of the magnon condensate electrically via the spin transfer torque mechanism. We use analytical calculations and micromagnetic simulations to illustrate this effect. Our results may explain the recent experiment on Bi-doped YIG and open a pathway toward applying current-driven magnon condensation in quantum spintronics.
Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments

The acoustic variability of noisy and reverberant speech mixtures is influenced by multiple factors, such as the spectro-temporal characteristics of the target speaker and the interfering noise, the signal-to-noise ratio (SNR) and the room characteristics. This large variability poses a major challenge for learning-based speech enhancement systems, since a mismatch between the training and testing conditions can substantially reduce the performance of the system. Generalization to unseen conditions is typically assessed by testing the system with a new speech, noise or binaural room impulse response (BRIR) database different from the one used during training. However, the difficulty of the speech enhancement task can change across databases, which can substantially influence the results. The present study introduces a generalization assessment framework that uses a reference model trained on the test condition, such that it can be used as a proxy for the difficulty of the test condition. This allows to disentangle the effect of the change in task difficulty from the effect of dealing with new data, and thus to define a new measure of generalization performance termed the generalization gap. The procedure is repeated in a cross-validation fashion by cycling through multiple speech, noise, and BRIR databases to accurately estimate the generalization gap. The proposed framework is applied to evaluate the generalization potential of a feedforward neural network (FFNN), Conv-TasNet, DCCRN and MANNER. We find that for all models, the performance degrades the most in speech mismatches, while good noise and room generalization can be achieved by training on multiple databases. Moreover, while recent models show higher performance in matched conditions, their performance substantially decreases in mismatched conditions and can become inferior to that of the FFNN-based system.
From inhomogeneous random digraphs to random graphs with fixed arc counts

Consider a random graph model with $n$ vertices where each vertex has a vertex-type drawn from some discrete distribution. Suppose that the number of arcs to be placed between each pair of vertex-types is known, and that each arc is placed uniformly at random without replacement between one of the vertex-pairs with matching types. In this paper, we will show that under certain conditions this random graph model is equivalent to the well-studied inhomogeneous random digraph model. We will use this equivalence in three applications. First, we will apply the equivalence on some well known random graph models (the Erd\H{o}s-R\'enyi model, the stochastic block model, and the Chung-Lu model) to showcase what their equivalent counterparts with fixed arcs look like. Secondly, we will extend this equivalence to a practical model for inferring cell-cell interactions to showcase how theoretical knowledge about inhomogeneous random digraphs can be transferred to a modeling context. Thirdly, we will show how our model induces a natural fast algorithm to generate inhomogeneous random digraphs.
A Co-design Study for Multi-Stakeholder Job Recommender System Explanations

Recent legislation proposals have significantly increased the demand for eXplainable Artificial Intelligence (XAI) in many businesses, especially in so-called `high-risk' domains, such as recruitment. Within recruitment, AI has become commonplace, mainly in the form of job recommender systems (JRSs), which try to match candidates to vacancies, and vice versa. However, common XAI techniques often fall short in this domain due to the different levels and types of expertise of the individuals involved, making explanations difficult to generalize. To determine the explanation preferences of the different stakeholder types - candidates, recruiters, and companies - we created and validated a semi-structured interview guide. Using grounded theory, we structurally analyzed the results of these interviews and found that different stakeholder types indeed have strongly differing explanation preferences. Candidates indicated a preference for brief, textual explanations that allow them to quickly judge potential matches. On the other hand, hiring managers preferred visual graph-based explanations that provide a more technical and comprehensive overview at a glance. Recruiters found more exhaustive textual explanations preferable, as those provided them with more talking points to convince both parties of the match. Based on these findings, we describe guidelines on how to design an explanation interface that fulfills the requirements of all three stakeholder types. Furthermore, we provide the validated interview guide, which can assist future research in determining the explanation preferences of different stakeholder types.
Gait Design of a Novel Arboreal Concertina Locomotion for Snake-like Robots

In this paper, we propose a novel strategy for a snake robot to move straight up a cylindrical surface. Prior works on pole-climbing for a snake robot mainly utilized a rolling helix gait, and although proven to be efficient, it does not reassemble movements made by a natural snake. We take inspiration from nature and seek to imitate the Arboreal Concertina Locomotion (ACL) from real-life serpents. In order to represent the 3D curves that make up the key motion patterns of ACL, we establish a set of parametric equations that identify periodic functions, which produce a sequence of backbone curves. We then build up the gait equation using the curvature integration method, and finally, we propose a simple motion estimation strategy using virtual chassis and non-slip model assumptions. We present experimental results using a 20-DOF snake robot traversing outside of a straight pipe.
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation

Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous domain knowledge can be hardly directly deployed to a new domain without additional costs. In this paper, we provide a new perspective and approach of alleviating the domain shifts, by proposing a Reconstruction-Simulation-Perception (ReSimAD) scheme. Specifically, the implicit reconstruction process is based on the knowledge from the previous old domain, aiming to convert the domain-related knowledge into domain-invariant representations, e.g., 3D scene-level meshes. Besides, the point clouds simulation process of multiple new domains is conditioned on the above reconstructed 3D meshes, where the target-domain-like simulation samples can be obtained, thus reducing the cost of collecting and annotating new-domain data for the subsequent perception process. For experiments, we consider different cross-domain situations such as Waymo-to-KITTI, Waymo-to-nuScenes, Waymo-to-ONCE, etc, to verify the zero-shot target-domain perception using ReSimAD. Results demonstrate that our method is beneficial to boost the domain generalization ability, even promising for 3D pre-training.
Transferability analysis of data-driven additive manufacturing knowledge: a case study between powder bed fusion and directed energy deposition

Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that haven’t been mined and formalized in an integrated way. Moreover, no tools or guidelines exist to support data-driven knowledge transfer from one context to another. As a result, data-driven solutions using specific AI techniques are being developed and validated only for specific AM process technologies. There is a potential to exploit the inherent similarities across various AM technologies and adapt the existing solutions from one process or problem to another using AI, such as Transfer Learning. We propose a three-step knowledge transferability analysis framework in AM to support data-driven AM knowledge transfer. As a prerequisite to transferability analysis, AM knowledge is featurized into identified knowledge components. The framework consists of pre-transfer, transfer, and post-transfer steps to accomplish knowledge transfer. A case study is conducted between flagship metal AM processes. Laser Powder Bed Fusion (LPBF) is the source of knowledge motivated by its relative matureness in applying AI over Directed Energy Deposition (DED), which drives the need for knowledge transfer as the less explored target process. We show successful transfer at different levels of the data-driven solution, including data representation, model architecture, and model parameters. The pipeline of AM knowledge transfer can be automated in the future to allow efficient cross-context or cross-process knowledge exchange.
Evaluating the Risk of Changes in a Microservices Architecture

In a microservices-based system, reliability and availability are key components to guarantee the best-in-class experience for the consumers. One of the key advantages of microservices architecture is the ability to independently deploy services, providing maximum change flexibility. However, this introduces an extra complexity in managing the risk associated with every change: any mutation of a service might cause the whole system to fail. In this research, we would propose an algorithm to enable development teams to determine the risk associated with each change to any of the microservices in the system.
A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models

Analysis of high-dimensional data has led to increased interest in both single index models (SIMs) and best subset selection. SIMs provide an interpretable and flexible modeling framework for high-dimensional data, while best subset selection aims to find a sparse model from a large set of predictors. However, best subset selection in high-dimensional models is known to be computationally intractable. Existing methods tend to relax the selection, but do not yield the best subset solution. In this paper, we directly tackle the intractability by proposing the first provably scalable algorithm for best subset selection in high-dimensional SIMs. Our algorithmic solution enjoys the subset selection consistency and has the oracle property with a high probability. The algorithm comprises a generalized information criterion to determine the support size of the regression coefficients, eliminating the model selection tuning. Moreover, our method does not assume an error distribution or a specific link function and hence is flexible to apply. Extensive simulation results demonstrate that our method is not only computationally efficient but also able to exactly recover the best subset in various settings (e.g., linear regression, Poisson regression, heteroscedastic models).
Behind The Wings: The Case of Reverse Engineering and Drone Hijacking in DJI Enhanced Wi-Fi Protocol

This research paper entails an examination of the Enhanced Wi-Fi protocol, focusing on its control command reverse-engineering analysis and subsequent demonstration of a hijacking attack. Our investigation discovered vulnerabilities in the Enhanced Wi-Fi control commands, rendering them susceptible to hijacking attacks. Notably, the study established that even readily available and cost-effective commercial off-the-shelf Wi-Fi routers could be leveraged as effective tools for executing such attacks. To illustrate this vulnerability, a proof-of-concept remote hijacking attack was carried out on a DJI Mini SE drone, whereby we intercepted the control commands to manipulate the drone's flight trajectory. The findings of this research emphasize the critical necessity of implementing robust security measures to safeguard unmanned aerial vehicles against potential hijacking threats. Considering that civilian drones are now used as war weapons, the study underscores the urgent need for further exploration and advancement in the domain of civilian drone security.
Integrating Python data analysis in an existing introductory laboratory course

In this article we describe how we successfully incorporated data analysis in Python in a first-year laboratory course without significantly altering the course structure and without overburdening students. We show how we created and used carefully designed Jupyter Notebooks with exercises and physics application examples that allow students to master data analysis programming in the laboratory course. We use these Notebooks to guide students through the fundamentals of data handling and analysis in Python while performing simple experiments. We present our teaching approach and the developed materials. We discuss the effectiveness of our intervention based on the results from pre- and post- course questionnaires and students' group work. The results presented give insights about advantages and challenges of introducing computation at the early stage of the curriculum in a laboratory course setting and are informative for other instructors and the physics education research community.
AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity Recognition and Linking

This paper presents a novel approach to address the Entity Recognition and Linking Challenge at NLPCC 2015. The task involves extracting named entity mentions from short search queries and linking them to entities within a reference Chinese knowledge base. To tackle this problem, we first expand the existing knowledge base and utilize external knowledge to identify candidate entities, thereby improving the recall rate. Next, we extract features from the candidate entities and utilize Support Vector Regression and Multiple Additive Regression Tree as scoring functions to filter the results. Additionally, we apply rules to further refine the results and enhance precision. Our method is computationally efficient and achieves an F1 score of 0.535.
Enhancing Multi-modal Cooperation via Fine-grained Modality Valuation

One primary topic of multimodal learning is to jointly incorporate heterogeneous information from different modalities. However, most models often suffer from unsatisfactory multimodal cooperation, which cannot jointly utilize all modalities well. Some methods are proposed to identify and enhance the worse learnt modality, but they are often hard to provide the fine-grained observation of multimodal cooperation at sample-level with theoretical support. Hence, it is essential to reasonably observe and improve the fine-grained cooperation between modalities, especially when facing realistic scenarios where the modality discrepancy could vary across different samples. To this end, we introduce a sample-level modality valuation metric to evaluate the contribution of each modality for each sample. Via modality valuation, we observe that modality discrepancy indeed could be different at sample-level, beyond the global contribution discrepancy at dataset-level. We further analyze this issue and improve cooperation between modalities at sample-level by enhancing the discriminative ability of low-contributing modalities in a targeted manner. Overall, our methods reasonably observe the fine-grained uni-modal contribution and achieve considerable improvement. The source code and dataset are available at \url{https://github.com/GeWu-Lab/Valuate-and-Enhance-Multimodal-Cooperation}.
Reinforcement Learning for Supply Chain Attacks Against Frequency and Voltage Control

The ongoing modernization of the power system, involving new equipment installations and upgrades, exposes the power system to the introduction of malware into its operation through supply chain attacks. Supply chain attacks present a significant threat to power systems, allowing cybercriminals to bypass network defenses and execute deliberate attacks at the physical layer. Given the exponential advancements in machine intelligence, cybercriminals will leverage this technology to create sophisticated and adaptable attacks that can be incorporated into supply chain attacks. We demonstrate the use of reinforcement learning for developing intelligent attacks incorporated into supply chain attacks against generation control devices. We simulate potential disturbances impacting frequency and voltage regulation. The presented method can provide valuable guidance for defending against supply chain attacks.