OpenRaiser/Intern-Atlas huggingface dataset:
Taming False Positives in Out-of-Distribution Detection with Human Feedback
Venue: arXiv cs.LG 2024
Year: 2024

Robustness to out-of-distribution (OOD) samples is crucial for safely deploying machine learning models in the open world. Recent works have focused on designing scoring functions to quantify OOD uncertainty. Setting appropriate thresholds for these scoring functions for OOD detection is challenging as OOD samples are often unavailable up front. Typically, thresholds are set to achieve a desired true positive rate (TPR), e.g., $95\%$ TPR. However, this can lead to very high false positive rates (FPR), ranging from 60 to 96\%, as observed in the Open-OOD benchmark. In safety-critical real-life applications, e.g., medical diagnosis, controlling the FPR is essential when dealing with various OOD samples dynamically. To address these challenges, we propose a mathematically grounded OOD detection framework that leverages expert feedback to \emph{safely} update the threshold on the fly. We provide theoretical results showing that it is guaranteed to meet the FPR constraint at all times while minimizing the use of human feedback. Another key feature of our framework is that it can work with any scoring function for OOD uncertainty quantification. Empirical evaluation of our system on synthetic and benchmark OOD datasets shows that our method can maintain FPR at most $5\%$ while maximizing TPR.
Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics
Venue: arXiv cs.CE 2024
Year: 2024

In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilities and the renewed advances of machine learning. In this context, new methods such as the virtual fields method and physics-informed neural networks have been developed as alternatives to the already established least-squares and finite element-based approaches. Moreover, model discovery problems are starting to emerge and can also be addressed in a parameter estimation framework. These developments call for a new unified perspective, which is able to cover both traditional parameter estimation methods and novel approaches in which the state variables or the model structure itself are inferred as well. Adopting concepts discussed in the inverse problems community, we distinguish between all-at-once and reduced approaches. With this general framework, we are able to structure a large portion of the literature on parameter estimation in computational mechanics - and we can identify combinations that have not yet been addressed, two of which are proposed in this paper. We also discuss statistical approaches to quantify the uncertainty related to the estimated parameters, and we propose a novel two-step procedure for identification of complex material models based on both frequentist and Bayesian principles. Finally, we illustrate and compare several of the aforementioned methods with mechanical benchmarks based on synthetic and real data.
IDIL: Imitation Learning of Intent-Driven Expert Behavior
Venue: arXiv cs.LG 2024
Year: 2024

When faced with accomplishing a task, human experts exhibit intentional behavior. Their unique intents shape their plans and decisions, resulting in experts demonstrating diverse behaviors to accomplish the same task. Due to the uncertainties encountered in the real world and their bounded rationality, experts sometimes adjust their intents, which in turn influences their behaviors during task execution. This paper introduces IDIL, a novel imitation learning algorithm to mimic these diverse intent-driven behaviors of experts. Iteratively, our approach estimates expert intent from heterogeneous demonstrations and then uses it to learn an intent-aware model of their behavior. Unlike contemporary approaches, IDIL is capable of addressing sequential tasks with high-dimensional state representations, while sidestepping the complexities and drawbacks associated with adversarial training (a mainstay of related techniques). Our empirical results suggest that the models generated by IDIL either match or surpass those produced by recent imitation learning benchmarks in metrics of task performance. Moreover, as it creates a generative model, IDIL demonstrates superior performance in intent inference metrics, crucial for human-agent interactions, and aptly captures a broad spectrum of expert behaviors.
Record Acceleration of the Two-Dimensional Ising Model Using High-Performance Wafer Scale Engine
Venue: arXiv cs.AR 2024
Year: 2024

The versatility and wide-ranging applicability of the Ising model, originally introduced to study phase transitions in magnetic materials, have made it a cornerstone in statistical physics and a valuable tool for evaluating the performance of emerging computer hardware. Here, we present a novel implementation of the two-dimensional Ising model on a Cerebras Wafer-Scale Engine (WSE), a revolutionary processor that is opening new frontiers in computing. In our deployment of the checkerboard algorithm, we optimized the Ising model to take advantage of the unique WSE architecture. Specifically, we employed a compressed bit representation storing 16 spins on each int16 word, and efficiently distributed the spins over the processing units enabling seamless weak scaling and limiting communications to only immediate neighboring units. Our implementation can handle up to 754 simulations in parallel, achieving an aggregate of over 61.8 trillion flip attempts per second for Ising models with up to 200 million spins. This represents a gain of up to 148 times over previously reported single-device with a highly optimized implementation on NVIDIA V100 and up to 88 times in productivity compared to NVIDIA H100. Our findings highlight the significant potential of the WSE in scientific computing, particularly in the field of materials modeling.
What You Use is What You Get: Unforced Errors in Studying Cultural Aspects in Agile Software Development
Venue: arXiv cs.SE 2024
Year: 2024

Context: Cultural aspects are of high importance as they guide people's behaviour and thus, influence how people apply methods and act in projects. In recent years, software engineering research emphasized the need to analyze the challenges of specific cultural characteristics. Investigating the influence of cultural characteristics is challenging due to the multi-faceted concept of culture. People's behaviour, their beliefs and underlying values are shaped by different layers of culture, e.g., regions, organizations, or groups. In this study, we focus on agile methods, which are agile approaches that focus on underlying values, collaboration and communication. Thus, cultural and social aspects are of high importance for their successful use in practice. Objective: In this paper, we address challenges that arise when using the model of cultural dimensions by Hofstede to characterize specific cultural values. This model is often used when discussing cultural influences in software engineering. Method: As a basis, we conducted an exploratory, multiple case study, consisting of two cases in Japan and two in Germany. Contributions: In this study, we observed that cultural characteristics of the participants differed significantly from cultural characteristics that would typically be expected for people from the respective country. This drives our conclusion that for studies in empirical software engineering that address cultural factors, a case-specific analysis of the characteristics is needed.
First-Fit Coloring of Forests in Random Arrival Model
Venue: arXiv cs.DM 2024
Year: 2024

We consider a graph coloring algorithm that processes vertices in order taken uniformly at random and assigns colors to them using First-Fit strategy. We show that this algorithm uses, in expectation, at most $(1 + o(1))\cdot \ln n \,/\, \ln\ln n$ different colors to color any forest with $n$ vertices. We also construct a family of forests that shows that this bound is best possible.
Computational hardness of detecting graph lifts and certifying lift-monotone properties of random regular graphs
Venue: arXiv cs.CC 2024
Year: 2024

We introduce a new conjecture on the computational hardness of detecting random lifts of graphs: we claim that there is no polynomial-time algorithm that can distinguish between a large random $d$-regular graph and a large random lift of a Ramanujan $d$-regular base graph (provided that the lift is corrupted by a small amount of extra noise), and likewise for bipartite random graphs and lifts of bipartite Ramanujan graphs. We give evidence for this conjecture by proving lower bounds against the local statistics hierarchy of hypothesis testing semidefinite programs. We then explore the consequences of this conjecture for the hardness of certifying bounds on numerous functions of random regular graphs, expanding on a direction initiated by Bandeira, Banks, Kunisky, Moore, and Wein (2021). Conditional on this conjecture, we show that no polynomial-time algorithm can certify tight bounds on the maximum cut of random 3- or 4-regular graphs, the maximum independent set of random 3- or 4-regular graphs, or the chromatic number of random 7-regular graphs. We show similar gaps asymptotically for large degree for the maximum independent set and for any degree for the minimum dominating set, finding that naive spectral and combinatorial bounds are optimal among all polynomial-time certificates. Likewise, for small-set vertex and edge expansion in the limit of very small sets, we show that the spectral bounds of Kahale (1995) are optimal among all polynomial-time certificates.
Out-of-Distribution Detection using Maximum Entropy Coding
Venue: arXiv cs.IT 2024
Year: 2024

Given a default distribution $P$ and a set of test data $x^M=\{x_1,x_2,\ldots,x_M\}$ this paper seeks to answer the question if it was likely that $x^M$ was generated by $P$. For discrete distributions, the definitive answer is in principle given by Kolmogorov-Martin-Löf randomness. In this paper we seek to generalize this to continuous distributions. We consider a set of statistics $T_1(x^M),T_2(x^M),\ldots$. To each statistic we associate its maximum entropy distribution and with this a universal source coder. The maximum entropy distributions are subsequently combined to give a total codelength, which is compared with $-\log P(x^M)$. We show that this approach satisfied a number of theoretical properties. For real world data $P$ usually is unknown. We transform data into a standard distribution in the latent space using a bidirectional generate network and use maximum entropy coding there. We compare the resulting method to other methods that also used generative neural networks to detect anomalies. In most cases, our results show better performance.
Solving the Graph Burning Problem for Large Graphs
Venue: arXiv cs.DM 2024
Year: 2024

We propose an exact algorithm for the Graph Burning Problem ($\texttt{GBP}$), an NP-hard optimization problem that models the spread of influence on social networks. Given a graph $G$ with vertex set $V$, the objective is to find a sequence of $k$ vertices in $V$, namely, $v_1, v_2, \dots, v_k$, such that $k$ is minimum and $\bigcup_{i = 1}^{k} \{u\! \in\! V\! : d(u, v_i) \leq k - i\} = V$, where $d(u,v)$ denotes the distance between $u$ and $v$. We formulate the problem as a set covering integer programming model and design a row generation algorithm for the $\texttt{GBP}$. Our method exploits the fact that a very small number of covering constraints is often sufficient for solving the integer model, allowing the corresponding rows to be generated on demand. To date, the most efficient exact algorithm for the $\texttt{GBP}$, denoted here by $\texttt{GDCA}$, is able to obtain optimal solutions for graphs with up to 14,000 vertices within two hours of execution. In comparison, our algorithm finds provably optimal solutions approximately 236 times faster, on average, than $\texttt{GDCA}$. For larger graphs, memory space becomes a limiting factor for $\texttt{GDCA}$. Our algorithm, however, solves real-world instances with almost 200,000 vertices in less than 35 seconds, increasing the size of graphs for which optimal solutions are known by a factor of 14.
The Web unpacked: a quantitative analysis of global Web usage
Venue: arXiv cs.CY 2024
Year: 2024

This paper presents a comprehensive analysis of global web usage patterns based on data from SimilarWeb, a leading source for estimating web traffic. Leveraging a dataset comprising over 250,000 websites, we estimate the total web traffic and investigate its distribution among domains and industry sectors. We detail the characteristics of the top 116 domains, which comprise an estimated one-third of all web traffic. Our analysis scrutinizes various attributes of these domains, including their content sources and types, access requirements, offline presence, and ownership features. Our analysis reveals a significant concentration of web traffic, with a diminutive number of top websites capturing the majority of visits. Search engines, news and media, social networks, streaming, and adult content emerge as primary attractors of web traffic, which is also highly concentrated on platforms and USA-owned websites. Much of the traffic goes to for-profit but mostly free-of-charge websites, highlighting the dominance of business models not based on paywalls.
Process Mining Embeddings: Learning Vector Representations for Petri Nets
Venue: arXiv cs.AI 2024
Year: 2024

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.
Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspaper
Venue: arXiv cs.CL 2024
Year: 2024

Dominant pre-trained language models (PLMs) have demonstrated the potential risk of memorizing and outputting the training data. While this concern has been discussed mainly in English, it is also practically important to focus on domain-specific PLMs. In this study, we pre-trained domain-specific GPT-2 models using a limited corpus of Japanese newspaper articles and evaluated their behavior. Experiments replicated the empirical finding that memorization of PLMs is related to the duplication in the training data, model size, and prompt length, in Japanese the same as in previous English studies. Furthermore, we attempted membership inference attacks, demonstrating that the training data can be detected even in Japanese, which is the same trend as in English. The study warns that domain-specific PLMs, sometimes trained with valuable private data, can ''copy and paste'' on a large scale.
Neuro-Symbolic Embedding for Short and Effective Feature Selection via Autoregressive Generation
Venue: arXiv cs.LG 2024
Year: 2024

Feature selection aims to identify the optimal feature subset for enhancing downstream models. Effective feature selection can remove redundant features, save computational resources, accelerate the model learning process, and improve the model overall performance. However, existing works are often time-intensive to identify the effective feature subset within high-dimensional feature spaces. Meanwhile, these methods mainly utilize a single downstream task performance as the selection criterion, leading to the selected subsets that are not only redundant but also lack generalizability. To bridge these gaps, we reformulate feature selection through a neuro-symbolic lens and introduce a novel generative framework aimed at identifying short and effective feature subsets. More specifically, we found that feature ID tokens of the selected subset can be formulated as symbols to reflect the intricate correlations among features. Thus, in this framework, we first create a data collector to automatically collect numerous feature selection samples consisting of feature ID tokens, model performance, and the measurement of feature subset redundancy. Building on the collected data, an encoder-decoder-evaluator learning paradigm is developed to preserve the intelligence of feature selection into a continuous embedding space for efficient search. Within the learned embedding space, we leverage a multi-gradient search algorithm to find more robust and generalized embeddings with the objective of improving model performance and reducing feature subset redundancy. These embeddings are then utilized to reconstruct the feature ID tokens for executing the final feature selection. Ultimately, comprehensive experiments and case studies are conducted to validate the effectiveness of the proposed framework.
Phase-aggregated Dual-branch Network for Efficient Fingerprint Dense Registration
Venue: arXiv cs.CV 2024
Year: 2024

Fingerprint dense registration aims to finely align fingerprint pairs at the pixel level, thereby reducing intra-class differences caused by distortion. Unfortunately, traditional methods exhibited subpar performance when dealing with low-quality fingerprints while suffering from slow inference speed. Although deep learning based approaches shows significant improvement in these aspects, their registration accuracy is still unsatisfactory. In this paper, we propose a Phase-aggregated Dual-branch Registration Network (PDRNet) to aggregate the advantages of both types of methods. A dual-branch structure with multi-stage interactions is introduced between correlation information at high resolution and texture feature at low resolution, to perceive local fine differences while ensuring global stability. Extensive experiments are conducted on more comprehensive databases compared to previous works. Experimental results demonstrate that our method reaches the state-of-the-art registration performance in terms of accuracy and robustness, while maintaining considerable competitiveness in efficiency.
Scrutinizing Data from Sky: An Examination of Its Veracity in Area Based Traffic Contexts
Venue: arXiv cs.ET 2024
Year: 2024

Traffic data collection has been an overwhelming task for researchers as well as authorities over the years. With the advancement in technology and introduction of various tools for processing and extracting traffic data the task has been made significantly convenient. Data from Sky (DFS) is one such tool, based on image processing and artificial intelligence (AI), that provides output for macroscopic as well as microscopic variables of the traffic streams. The company claims to provide 98 to 100 percent accuracy on the data exported using DFS tool. The tool is widely used in developed countries where the traffic is homogenous and has lane-based movements. In this study, authors have checked the veracity of DFS tool in heterogenous and area-based traffic movement that is prevailing in most developing countries. The validation is done using various methods using Classified Volume Count (CVC), Space Mean Speeds (SMS) of individual vehicle classes and microscopic trajectory of probe vehicle to verify DFS claim. The error for CVCs for each vehicle class present in the traffic stream is estimated. Mean Absolute Percentage Error (MAPE) values are calculated for average speeds of each vehicle class between manually and DFS extracted space mean speeds (SMSs), and the microscopic trajectories are validated using a GPS based tracker put on probe vehicles. The results are fairly accurate in the case of data taken from a bird eye view with least errors. The other configurations of data collection have some significant errors, that are majorly caused by the varied traffic composition, the view of camera angle, and the direction of traffic.
Set Selection with Uncertain Weights: Non-Adaptive Queries and Thresholds
Venue: arXiv cs.DS 2024
Year: 2024

We study set selection problems where the weights are uncertain. Instead of its exact weight, only an uncertainty interval containing its true weight is available for each element. In some cases, some solutions are universally optimal; i.e., they are optimal for every weight that lies within the uncertainty intervals. However, it may be that no universal optimal solution exists, unless we are revealed additional information on the precise values of some elements. In the minimum cost admissible query problem, we are tasked to (non-adaptively) find a minimum-cost subset of elements that, no matter how they are revealed, guarantee the existence of a universally optimal solution. We introduce thresholds under uncertainty to analyze problems of minimum cost admissible queries. Roughly speaking, for every element e, there is a threshold for its weight, below which e is included in all optimal solutions and a second threshold above which e is excluded from all optimal solutions. We show that computing thresholds and finding minimum cost admissible queries are essentially equivalent problems. Thus, the analysis of the minimum admissible query problem reduces to the problem of computing thresholds. We provide efficient algorithms for computing thresholds in the settings of minimum spanning trees, matroids, and matchings in trees; and NP-hardness results in the settings of s-t shortest paths and bipartite matching. By making use of the equivalence between the two problems these results translate into efficient algorithms for minimum cost admissible queries in the settings of minimum spanning trees, matroids, and matchings in trees; and NP-hardness results in the settings of s-t shortest paths and bipartite matching.
Scene-Extrapolation: Generating Interactive Traffic Scenarios
Venue: arXiv cs.RO 2024
Year: 2024

Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verifying these systems, predominantly occurring within simulation. In our approach, we use traffic scenes as a starting point (seed-scene) to address the individuality of various highly automated driving functions and to avoid the problems associated with a predefined test traffic scenario. Different highly autonomous driving functions, or their distinct iterations, may display different behaviors under the same operating conditions. To make a generalizable statement about a seed-scene, we simulate possible outcomes based on various behavior profiles. We utilize our lightweight simulation environment and populate it with rule-based and machine learning behavior models for individual actors in the scenario. We analyze resulting scenarios using a variety of criticality metrics. The density distributions of the resulting criticality values enable us to make a profound statement about the significance of a particular scene, considering various eventualities.