Alignment Problem Research Articles

Hybrid compliant control with variable-stiffness wrist for assembly and grinding application

This research presents a novel robot system that combines active and passive components to enhance compliance and dependability. The system is based on a continuous variable stiffness wrist. A wrist was created that met the requirements and a combination of active and passive control methods was suggested to insert and regulate forces effectively. The control strategy is based on the Cosserat rod model, with the fundamental concept being calculating the position and orientation of the component using data on the force exerted during contact between the parts and the stiffness of the contact between the shaft and hole components. This process converts the hard assembly into a flexible contact. Compliance is monitored via force and vision sensors, which allows for the shaft-hole assembly operation to be carried out even with attitude alignment problems, resulting in a notable decrease in the precision needed for component alignment. Initially, the camera supplies the first positional data of the shaft component for the robotic system. In addition, the performance of the wrist with variable stiffness is evaluated in terms of stiffness. Additionally, the calculation of relative deformation between components is examined using contact force information. Moreover, a robust active/passive hybrid insertion control technique, which relies on contact force, is proposed. Finally, the shaft-hole assembly task substantiates the necessity for contact force monitoring in the insertion assembly process. This control technique has demonstrated its efficacy in ensuring passive-compliant assembly performance. Furthermore, the variable stiffness wrist has been employed in robotic grinding for surfaces with curved contours to validate its effectiveness.

Haplotype-aware sequence alignment to pangenome graphs.

Modern pangenome graphs are built using haplotype-resolved genome assemblies. When mapping reads to a pangenome graph, prioritizing alignments that are consistent with the known haplotypes improves genotyping accuracy. However, the existing rigorous formulations for colinear chaining and alignment problems do not consider the haplotype paths in a pangenome graph. This often leads to spurious read alignments to those paths that are unlikely recombinations of the known haplotypes. In this paper, we develop novel formulations and algorithms for sequence-to-graph alignment and chaining problems. Inspired by the genotype imputation models, we assume that a query sequence is an imperfect mosaic of reference haplotypes. Accordingly, we introduce a recombination penalty in the scoring functions for each haplotype switch. First, we solve haplotype-aware sequence-to-graph alignment in [Formula: see text] time, where Q is the query sequence, E is the set of edges, and H is the set of haplotypes represented in the graph. To complement our solution, we prove that an algorithm significantly faster than [Formula: see text] is impossible under the strong exponential time hypothesis (SETH). Second, we propose a haplotype-aware chaining algorithm that runs in [Formula: see text] time after graph preprocessing, where N is the count of input anchors. We then establish that a chaining algorithm significantly faster than [Formula: see text] is impossible under SETH. As a proof-of-concept, we implemented our chaining algorithm in the Minichain aligner. By aligning sequences sampled from the human major histocompatibility complex (MHC) to a pangenome graph of 60 MHC haplotypes, we demonstrate that our algorithm achieves better consistency with ground-truth recombinations compared with a haplotype-agnostic algorithm.

The use of business and technology management (BTM) integrated practice in business development initiatives – an ADR cycle

PurposeThe paper reports a study that evaluated the effectiveness and usefulness of Business and Technology Management (BTM), a management practice for formulating business digital strategies that address organization, processes and technology in an integrative way.Design/methodology/approachThe research followed action design research guidelines that combine the methodological principles of design science research and action research.FindingsEvidence from the study confirmed the adequacy and usefulness of BTM as a solution to an old management problem of internal fit, synergies, alignment or strategic integration. It also led to an improvement in its way of working, namely, a management practice with four process stages: (1) define trade objectives, (2) design an integrated business model, (3) manage the business transformation and execution and (4) evaluate the results obtained.Research limitations/implicationsThe study exemplifies how practice research can be used within a long-term research pursuit to provide empirical evidence that permits evaluating and improving a research-originated management approach.Practical implicationsBeing a theory-for-action, BTM has direct relevance for managers engaged in defining organizational and technological strategies connected with business results. The paper discloses an improved version of BTM together with a description of its application in a medium-sized company operating in the tourism sector.Originality/valueBTM addresses a recurrent issue raised by researchers and practitioners concerning a failure to bring different management perspectives together when formulating a strategy. Therefore, its value lies in its ability to assist in the integration of management perspectives into business development initiatives.

Existential risk from transformative AI: an economic perspective

The prospective arrival of transformative artificial intelligence (TAI) will be a filter for the human civilization – a threshold beyond which it will either strongly accelerate its growth, or vanish. Historical evidence on technological progress in AI capabilities and economic incentives to pursue it suggest that TAI will most likely be developed in just one to four decades. In contrast, theoretical problems of AI alignment, needed to be solved in order for TAI to be “friendly” towards humans rather than cause our extinction, appear difficult and impossible to solve by mechanically increasing the amount of compute. This means that transformative AI poses an imminent existential risk to the humankind which ought to be urgently addressed. Starting from this premise, this paper provides new economic perspectives on discussions surrounding the issue: whether addressing existential risks is cost effective and fair towards the contemporary poor, whether it constitutes “Pascal’s mugging”, how to quantify risks that have never materialized in the past, how discounting affects our assessment of existential risk, and how to include the prospects of upcoming singularity in economic forecasts. The paper also suggests possible policy actions, such as ramping up public funding on research on existential risks and AI safety, and improving regulation of the AI sector, preferably within a global policy framework.

TFAN: A Task-adaptive Feature Alignment Network for few-shot website fingerprinting attacks on Tor

Few-shot website fingerprinting (WF) attacks aim to infer which website a user browsed through anonymity networks, such as Tor, using limited labeled traces. Recent methods either adopt complex metric strategies or perform time-consuming transfer learning, neither of which yields the most efficient performance in dynamic network environments. In this paper, we introduce a novel Task-adaptive Feature Alignment Network (TFAN) following the meta-learning paradigm. TFAN regards the few-shot WF attack as a feature alignment problem in class latent space, aiming to depict each location in the query feature map as a weighted sum of support features of a given class. Ridge regression provides a closed-form solution without extra parameters or techniques, ensuring high computational efficiency. Moreover, we also propose a Task-adaptive Modulation Unit (TMU), which activates the differences between support prototypes to generate task-level channel weights, making channels with significant discriminative details for each task contribute more to alignment. Extensive experiments on public Tor datasets demonstrate the superiority of TFAN in different scenarios. Notably, it is the only method that maintains over 90% accuracy in the 1-shot setting even 42 days later. Our code is available at https://github.com/Crybaby98/TFAN.

Philosophical Investigations into AI Alignment: A Wittgensteinian Framework

We argue that the later Wittgenstein’s philosophy of language and mathematics, substantially focused on rule-following, is relevant to understand and improve on the Artificial Intelligence (AI) alignment problem: his discussions on the categories that influence alignment between humans can inform about the categories that should be controlled to improve on the alignment problem when creating large data sets to be used by supervised and unsupervised learning algorithms, as well as when introducing hard coded guardrails for AI models. We cast these considerations in a model of human–human and human–machine alignment and sketch basic alignment strategies based on these categories and further reflections on rule-following like the notion of meaning as use. To sustain the validity of these considerations, we also show that successful techniques employed by AI safety researchers to better align new AI systems with our human goals are congruent with the stipulations that we derive from the later Wittgenstein’s philosophy. However, their application may benefit from the added specificities and stipulations of our framework: it extends on the current efforts and provides further, specific AI alignment techniques. Thus, we argue that the categories of the model and the core alignment strategies presented in this work can inform further AI alignment techniques.

Cross-modal semantic aligning and neighbor-aware completing for robust text–image person retrieval

Most existing text–image person re-identification (TIReID) methods are performed in an ideal environment where both image and text instances are fully intact and identity annotated. However, in real-world open environments, these ideal assumptions often cannot be satisfied. In this study, we are the first to explore how to enhance the robustness of the TIReID model to better adapt to open environments, focusing on two aspects: 1) unlabeled multi-modal data, and 2) incomplete multi-modal data. Here, unlabeled multi-modal data faces two key issues: intra-class variations and cross-modal aligning problems, while incomplete data leads to data semantic inconsistency problems and severe performance degradation. In order to tackle the above issues, we propose a novel Cross-modal Semantic Aligning and Neighbor-aware Completing (CANC) method for robust text–image person re-identification (RTIReID). Specifically, to tackle the intra-class variation problem, we propose intra-view prototype contrastive matching for image and text modalities, which helps the model learn intra-modal discriminative representations while increasing inter-class distance and suppressing intra-class variation. Furthermore, to resolve the cross-modal alignment problem, we propose the cross-view instance projection matching to establish the relationship between different modal representations. Additionally, regarding how to effectively handle incomplete samples, we introduce nearest neighbor consistent completion, which can restore high-quality completion features. We adopt CLIP-ViT and CLIP-Xformer as image and text encoders respectively, and Rank-k metrics (k=1,5,10) as primary evaluation metrics. Extensive experiments on several public datasets demonstrate that our method can effectively address TIReID in open environments, consistently surpassing several SOTA TIReID methods.

Analysing the Motivation and Effect of Cross-border Mergers and Acquisitions: A Case Study of the Acquisition of YOUJIUSHIDAI by SHANGHAI ACE CO., LTD.

Under the background of globalisation and information technology revolution, cross-border mergers and acquisitions (M&A) have become a key strategy for corporate strategic transformation and market expansion. Taking the acquisition of YOUJIUSHIDAI by SACE as an example, this study adopts the methods of literature review, case study analysis and AMPS model to explore in depth the motives, effects and impacts of cross-border M&A on corporate performance. This study finds that although M&A enhances shareholder wealth in the short term, long-term profitability falls short of expectations, which is mainly limited by the lack of R&D capability and market alignment problems. This study emphasises the importance of synergies and points out that high-premium M&As may lead to goodwill impairment risk, thus affecting the financial status of the firms and the interests of shareholders, and puts forward suggestions to optimise M&A strategies and strengthen regulation, in order to protect the interests of small and medium-sized investors and to promote the healthy development of the capital market.

Robust adaptive non‐linear alignment algorithm for SINS/DVL integrated navigation system based on variational Bayesian

AbstractThe problem of dynamic attitude alignment for strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation has become a research hotspot. Underwater complex environment makes DVL output vulnerable to non‐Gaussian noise pollution, which makes it difficult to converge the SINS misalignment angle to within 1° based on analytical coarse alignment. This is to say, the performance of SINS fine alignment via Kalman filter will be degraded because the model of initial alignment exhibits non‐linearity. The inaccurate and/or unknown prior information of measurement noise statistical characteristics will also degrade the filtering performance. To ensure the SINS/DVL alignment accuracy under non‐linear, non‐Gaussian and uncertain conditions, this paper proposed a robust adaptive unscented Kalman filter (UKF) based on Variational Bayesian (VB) method (VBRAUKF). The proposed VBRAUKF improves the adaptability and robustness of UKF from the following two main aspects. Firstly, an adaptive estimation strategy for the measurement noise covariance is designed based on the VB algorithm, which can restrain the effect of inaccurate observation model and improve the adaptive ability of the filtering method. Secondly, an expansion factor is designed based on Mahalanobis distance algorithm, which can restrain the effect of non‐Gaussian noise and improve the robustness of the filtering method. The experimental results for the problem of the SINS/DVL dynamic alignment under mixed Gaussian noise and/or outlier conditions demonstrate the superiority of the proposed VBRAUKF over the traditional ones.

External codes for multiple unicast networks via interference alignment.

We introduce a formal framework to study the multiple unicast problem for a coded network in which the network code is linear over a finite field and fixed. We show that the problem corresponds to an interference alignment problem over a finite field. In this context, we establish an outer bound for the achievable rate region and provide examples of networks where the bound is sharp. We finally give evidence of the crucial role played by the field characteristic in the problem.

Human Values from Indian Philosophy to Solve AI Alignment Problem

Human Values from Indian Philosophy to Solve AI Alignment Problem

Intermedial and theatrical perspectives of AI: Re-framing the Turing test

Artificial intelligence (AI), the understanding and building of computational agents that act intelligently, is claimed to be a powerful, pervasive medium. Although we interact with it and hear a lot about it, we do not ‘see’ AI but experience its manifestations and/or outcomes, such as chatterbots and virtual assistants. A criterion for deeming an artificial agent as intelligent has been already proposed by Turing in 1950, coined as the ‘Imitation Game’, where a machine takes the place of a man, known as the Turing test. Despite the test being initially conceived as a make-believe game, AI has been enmeshed in major fields of human social activity and co-determines our lives. In this article, AI is compared with the media type of theatre performance, the epitome of make-believe, on the basis of intermediality. Furthermore, the analogies between AI and theatre are discussed and the paradigm of the puppet theatre as well as the medium of the mask prevail in the analysis. Findings are discussed, especially in light of the mind–body split and the alignment problem, and their implications are contemplated, allowing a re-estimation and re-framing of the Turing test in its theatrical and performative dimension.

Chromatically modeling the parsec-scale dusty structure in the center of NGC 1068

Context. The Very Large Telescope Interferometer (VLTI) has been providing breakthrough images of the dust in the central parsecs of active galactic nuclei (AGNs), which is thought to be a key component of the AGN unification scheme and AGN host galaxy interaction. In single infrared bands, these images can enjoin multiple interpretations, some of which could challenge the unification scheme. This is the case for the archetypal type 2 AGN of NGC 1068, whereby the degeneracy is reduced by multi-band temperature maps that are hindered by an ambiguity in the alignment between different single-band images. Aims. We aim to solve this problem by creating a chromatic model capable of simultaneously explaining the VLTI GRAVITY+MATISSE 2 μm–13 μm observations of AGNs hosted by NGC 1068. Methods. We employed a simple disk and wind geometry populated by spherical black-body emitters and dust obscuration to create a versatile multi-wavelength modelling method for chromatic IR interferometric data of dusty objects. Results. This simple geometry is capable of reproducing the spectro-interferometric data of NGC 1068 from the K through N bands. It explains the complex single band images with obscuration and inclination effects, and it solves the alignment problem between bands. We find that the resulting model disk and wind geometry is consistent with previous studies of comparable and larger scales. For example, compared to molecular gas emission, our model wind position angle (PA) of 2322° is close to the mas-scale outflowing CO(6–5) PA of ∼33° seen with ALMA. The equivalent 90° offset model disk PA is also consistent with the CO(6–5) disk axis of 112° as well as the mas-scale disk axis from CO(2–1), CO(3–2), and HCO+(4–3) of 115 ± 5°. Furthermore, the resulting model images visually resemble the multiple achromatic image reconstructions of the same data when evaluated at the same wavelengths. We conclude that the IR emitting structure surrounding the AGN within NGC 1068 can indeed be explained by the clumpy disk+wind iteration of the AGN unification scheme. Within the scheme, we find that it is best explained as a type 2 AGN and the obscuring dust chemistry can be explained by a mix of olivine silicates and 16 ± 1% amorphous carbon.

Aligning artificial intelligence with moral intuitions: an intuitionist approach to the alignment problem

Aligning artificial intelligence with moral intuitions: an intuitionist approach to the alignment problem

Committing to the wrong artificial delegate in a collective-risk dilemma is better than directly committing mistakes

While autonomous artificial agents are assumed to perfectly execute the strategies they are programmed with, humans who design them may make mistakes. These mistakes may lead to a misalignment between the humans’ intended goals and their agents’ observed behavior, a problem of value alignment. Such an alignment problem may have particularly strong consequences when these autonomous systems are used in social contexts that involve some form of collective risk. By means of an evolutionary game theoretical model, we investigate whether errors in the configuration of artificial agents change the outcome of a collective-risk dilemma, in comparison to a scenario with no delegation. Delegation is here distinguished from no-delegation simply by the moment at which a mistake occurs: either when programming/choosing the agent (in case of delegation) or when executing the actions at each round of the game (in case of no-delegation). We find that, while errors decrease success rate, it is better to delegate and commit to a somewhat flawed strategy, perfectly executed by an autonomous agent, than to commit execution errors directly. Our model also shows that in the long-term, delegation strategies should be favored over no-delegation, if given the choice.

Enhancing Multiple Sequence Alignment with Genetic Algorithms: A Bioinformatics Approach in Biomedical Engineering

This study aimed to create a genetic information processing technique for the problem of multiple alignment of genetic sequences in bioinformatics. The objective was to take advantage of the computer hardware's capabilities and analyze the results obtained regarding quality, processing time, and the number of evaluated functions. The methodology was based on developing a genetic algorithm in Java, which resulted in four different versions: Gp1, Gp2, Gp3 and Gp4 . A set of genetic sequences were processed, and the results were evaluated by analyzing numerical behavior profiles. The research found that algorithms that maintained diversity in the population produced better quality solutions, and parallel processing reduced processing time. It was observed that the time required to perform the process decreased, according to the generated performance profile. The study concluded that conventional computer equipment can produce excellent results when processing genetic information if algorithms are optimized to exploit hardware resources. The computational effort of the hardware used is directly related to the number of evaluated functions. Additionally, the comparison method based on the determination of the performance profile is highlighted as a strategy for comparing the algorithm results in different metrics of interest, which can guide the development of more efficient genetic information processing techniques.

Object detection in drone video based on recurrent motion attention

In recent years, object detection in drone videos has garnered widespread attention. Conventional image-based detectors have encountered a performance bottleneck for drone images due to the severe appearance deterioration of targets and numerous background clutters. Video object detection (VOD) is a promising paradigm for such problems as it can effectively enhance target appearance by aggregating features from nearby frames in drone videos. However, struggling with the feature alignment problem which is particularly important for drone images where the background varies frequently. Following the VOD philosophy, we propose the Recurrent Motion Attention Network (RMA-Net) to extract spatial-temporal image features for object detection in drone vision. A recurrent fashion is designed to effectively and efficiently learn motion features in long sequences, only requiring a few frames of optical flows for initialization. Experiments demonstrate that the proposed method could easily promote existing object detectors to achieve high performance on drone videos. Specifically, our RMA adapted YOLOv7 outperforms the state-of-the-arts by a large margin on the challenging VisDrone2019-VID dataset, not only boosts the mean average precision by 6.99 % but also reaches a batch-1 inference speed of 31 frames per second.

Extending Undirected Graph Techniques to Directed Graphs via Category Theory

We use Category Theory to construct a ‘bridge’ relating directed graphs with undirected graphs, such that the notion of direction is preserved. Specifically, we provide an isomorphism between the category of simple directed graphs and a category we call ‘prime graphs category’; this has as objects labeled undirected bipartite graphs (which we call prime graphs), and as morphisms undirected graph morphisms that preserve the labeling (which we call prime graph morphisms). This theoretical bridge allows us to extend undirected graph techniques to directed graphs by converting the directed graphs into prime graphs. To give a proof of concept, we show that our construction preserves topological features when applied to the problems of network alignment and spectral graph clustering.

Towards a Resolution of the Spin Alignment Problem

Towards a Resolution of the Spin Alignment Problem

Concrete Highway Crack Detection Based on Visible Light and Infrared Silicate Spectrum Image Fusion.

Cracks provide the earliest and most immediate visual response to structural deterioration of asphalt pavements. Most of the current methods for crack detection are based on visible light sensors and convolutional neural networks. However, such an approach obviously limits the detection to daytime and good lighting conditions. Therefore, this paper proposes a crack detection technique cross-modal feature alignment of YOLOV5 based on visible and infrared images. The infrared spectrum characteristics of silicate concrete can be an important supplement. The adaptive illumination-aware weight generation module is introduced to compute illumination probability to guide the training of the fusion network. In order to alleviate the problem of weak alignment of the multi-scale feature map, the FA-BIFPN feature pyramid module is proposed. The parallel structure of a dual backbone network takes 40% less time to train than a single backbone network. As determined through validation on FLIR, LLVIP, and VEDAI bimodal datasets, the fused images have more stable performance compared to the visible images. In addition, the detector proposed in this paper surpasses the current advanced YOLOV5 unimodal detector and CFT cross-modal fusion module. In the publicly available bimodal road crack dataset, our method is able to detect cracks of 5 pixels with 98.3% accuracy under weak illumination.