Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration

Prediction-oriented machine learning is becoming increasingly valuable to organizations, as it may drive applications in crucial business areas. However, decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms. We ascribe this issue to the widely held view on advanced machine learning algorithms as"black boxes"whose complexity does not allow for uncovering the factors that drive the output of a corresponding system. To contribute to overcome this adoption barrier, we argue that research in information systems should devote more attention to the design of prototypical prediction-oriented machine learning applications (i.e., artifacts) whose predictions can be explained to human decision-makers. However, despite the recent emergence of a variety of tools that facilitate the development of such artifacts, there has so far been little research on their development. We attribute this research gap to the lack of methodological guidance to support the creation of these artifacts. For this reason, we develop a methodology which unifies methodological knowledge from design science research and predictive analytics with state-of-the-art approaches to explainable artificial intelligence. Moreover, we showcase the methodology using the example of price prediction in the sharing economy (i.e., on Airbnb).
Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023

This technical report presents our 1st place solution for the Waymo Open Sim Agents Challenge (WOSAC) 2023. Our proposed MultiVerse Transformer for Agent simulation (MVTA) effectively leverages transformer-based motion prediction approaches, and is tailored for closed-loop simulation of agents. In order to produce simulations with a high degree of realism, we design novel training and sampling methods, and implement a receding horizon prediction mechanism. In addition, we introduce a variable-length history aggregation method to mitigate the compounding error that can arise during closed-loop autoregressive execution. On the WOSAC, our MVTA and its enhanced version MVTE reach a realism meta-metric of 0.5091 and 0.5168, respectively, outperforming all the other methods on the leaderboard.
Collision-Free Shepherding Control of a Single Target Within a Swarm

The shepherding problem refers to guiding a group of agents (called sheep) to a specific destination using an external agent with repulsive forces (called shepherd). Although various movement algorithms for the shepherd have been explored in the literature, there is a scarcity of methodologies for selective guidance, which is a key technology for precise swarm control. Therefore, this study investigates the problem of guiding a single target sheep within a swarm to a given destination using a shepherd. We first present our model of the dynamics of sheep agents and the interaction between sheep and shepherd agents. The model is shown to be well-defined with no collision if the interaction magnitude between sheep and shepherd is reasonably limited. Based on the analysis with Lyapunov stability principles, we design a shepherd control law to guide the target sheep to the origin while avoiding collisions among sheep agents. Experimental results demonstrate the effectiveness of the proposed method in guiding the target sheep in both small and large scale swarms.
Heterogeneous Coalition Formation and Scheduling with Multi-Skilled Robots

We present an approach to task scheduling in heterogeneous multi-robot systems. In our setting, the tasks to complete require diverse skills. We assume that each robot is multi-skilled, i.e., each robot offers a subset of the possible skills. This makes the formation of heterogeneous teams (coalitions) a requirement for task completion. We present two centralized algorithms to schedule robots across tasks and to form suitable coalitions, assuming stochastic travel times across tasks. The coalitions are dynamic, in that the robots form and disband coalitions as the schedule is executed. The first algorithm we propose guarantees optimality, but its runtime is acceptable only for small problem instances. The second algorithm we propose can tackle large problems with short runtimes, and is based on a heuristic approach that typically reaches 1x-2x of the optimal solution cost.
Almost spanning distance trees in subsets of finite vector spaces

For and an odd prime power , consider the vector space over the finite field , where the distance between two points and is defined as . A distance graph is a graph associated with a nonzero distance to each of its edges. We show that large subsets of vector spaces over finite fields contain every nearly spanning distance tree with bounded degree in each distance. This quantitatively improves results by Bennett, Chapman, Covert, Hart, Iosevich, and Pakianathan on finding distance paths, and results by Pham, Senger, Tait, and Thu on finding distance trees. A key ingredient in proving our main result is to obtain a colorful generalization of a classical result of Haxell about finding nearly spanning bounded‐degree trees in an expander.
Perturbative Solution of the Einstein Constraints with Spin and Momentum Far Away From a Binary Source in the Bowen‐York Formalism

The momentum and Hamiltonian constraints of vacuum Einstein equations, within the Bowen‐York formalism, for two interacting black holes in close separation, with anti‐parallel spins and anti‐parallel linear momenta is studied. An analytical solution using perturbation theory is given. The location and the shape of the apparent horizon which generically depend on all the parameters, angles, and the separation between the black holes are also computed. The solution only works for distances far away from the black holes. To gain more insight close into the black holes, one has to go to the higher orders in perturbation theory, which is a rather cumbersome process. But the solution presented here can be of some use for numerical computations as the latter should match our result for the described problem.
Edge Devices Inference Performance Comparison

In this work, we investigate the inference time of the MobileNet family, EfficientNet V1 and V2 family, VGG models, Resnet family, and InceptionV3 on four edge platforms. Specifically NVIDIA Jetson Nano, Intel Neural Stick, Google Coral USB Dongle, and Google Coral PCIe. Our main contribution is a thorough analysis of the aforementioned models in multiple settings, especially as a function of input size, the presence of the classification head, its size, and the scale of the model. Since throughout the industry, those architectures are mainly utilized as feature extractors we put our main focus on analyzing them as such. We show that Google platforms offer the fastest average inference time, especially for newer models like MobileNet or EfficientNet family, while Intel Neural Stick is the most universal accelerator allowing to run most architectures. These results should provide guidance for engineers in the early stages of AI edge systems development. All of them are accessible at https://bulletprove.com/research/edge_inference_results. csv
The Evaluation Measure of Text Clustering for the Variable Number of Clusters

This study proposes an innovative measure for evaluating the performance of text clustering. In using K-means algorithm and Kohonen Networks for text clustering, the number clusters is fixed initially by configuring it as their parameter, while in using single pass algorithm for text clustering, the number of clusters is not predictable. Using labeled documents, the result of text clustering using K-means algorithm or Kohonen Network is able to be evaluated by setting the number of clusters as the number of the given target categories, mapping each cluster to a target category, and using the evaluation measures of text. But in using single pass algorithm, if the number of clusters is different from the number of target categories, such measures are useless for evaluating the result of text clustering. This study proposes an evaluation measure of text clustering based on intra-cluster similarity and inter-cluster similarity, what is called CI (Clustering Index) in this article.
Diagnosis and Treatment of Gordonia Species Infection in a Peach-Faced Lovebird (Agapornis roseicollis)

Abstract: Respiratory distress is a common presentation for avian species. A 9-week-old peach-faced lovebird (Agapornis roseicollis) was presented with a 2-week history of progressive dyspnea. Computed tomographic (CT) images were suggestive of splenomegaly and bilateral granulomatous pulmonary disease. Polymerase chain reaction testing of samples from the choana, cloaca, and distal tracheal/syringeal area were positive for Mycobacterium species hsp65. A comparison search of the 400 base pair sequence in the NCBI/BLAST/blastn database revealed a best match of 93% similarity to Gordonia species and 91% similarity to Gordonia bronchialis. Gordonia is a genus in the phylum Actinomycetota, the same lineage that includes Mycobacterium species. Gordonia species can be mistaken for Mycobacterium species unless more definitive diagnostic testing is pursued. Infection caused by Gordonia species is rare in humans. Reports commonly cite infection of immunocompromised patients, and to our knowledge, no reports of treatment have been published in the veterinary literature. After the test results were obtained, the patient was treated with azithromycin and pradofloxacin for 3 months. The lovebird was presented for reexamination when the antibiotic treatment was complete. When reexamined, and a second series of CT images evaluated, it was determined that the treatment achieved clinical resolution of signs and lesions.
From Nurturing the Successor to Attracting New Founders: How Firm Platforms Organize a Market for Selling Businesses

Abstract Firm platforms organize a market for selling Mittelstand firms in Germany. In doing so they face a challenge: to instill a market frame in a segment of the German economy that is known for its family embedding. This study examines the strategies firm platforms use to reframe ownership transfer from a gift passed on within the family to a commodity traded on a market. Building on a content analysis of the seven largest and most innovative firm platforms in Germany, this study reveals how firm platforms argue from within a gift exchange frame and shift its parameters in social, object, and time dimensions. This study makes a more general point by arguing that digital platforms function as cultural trailblazers in marketization. This study contributes to succession, digital platform, and marketization debates.
Global Optimization with Hill Climbing in Earth Observation Mission Planning

We describe an algorithm for the global optimization of image take and downlink planning of a single satellite Earth observation mission. The algorithm is based on hill climbing methods with a suitably chosen search space. It is shown how subproblems of the planning can be solved exactly, and how this enables the integration of a hierarchical constraint solving and optimization process into the hill climbing. For a test data set of 500 Earth observation requests we compare benchmark results against other methods and exact optimization results, at least for a subset of constraints. The shown algorithm is found to be superior to simple greedy searches and as powerful as simulated annealing. In special cases it can reach a solution quality close to exact solutions.
Detock: High Performance Multi-region Transactions at Scale

Many globally distributed data stores need to replicate data across large geographic distances. Since synchronously replicating data across such distances is slow, those systems with high consistency requirements often geo-partition data and direct all linearizable requests to the primary region of the accessed data. This significantly improves performance for workloads where most transactions access data close to where they originate from. However, supporting serializable multi-geo-partition transactions is a challenge, and they often degrade the performance of the whole system. This becomes even more challenging when they conflict with single-partition requests, where optimistic protocols lead to high numbers of aborts, and pessimistic protocols lead to high numbers of distributed deadlocks. In this paper, we describe the design of concurrency control and deadlock resolution protocols, built within a practical, complete implementation of a geographically replicated database system called Detock, that enables processing strictly-serializable multi-region transactions with near-zero performance degradation at extremely high conflict and order of magnitude higher throughput relative to state-of-the art geo-replication approaches, while improving latency by up to a factor of 5.
A Survey on Hypergraph Representation Learning

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects—most commonly nodes—of an input hyper-network into a latent space such that both the structural and relational properties of the network can be encoded and preserved. We provide a thorough overview of existing literature and offer a new taxonomy of hypergraph embedding methods by identifying three main families of techniques, i.e., spectral, proximity-preserving, and (deep) neural networks. For each family, we describe its characteristics and our insights in a single yet flexible framework and then discuss the peculiarities of individual methods, as well as their pros and cons. We then review the main tasks, datasets, and settings in which hypergraph embeddings are typically used. We finally identify and discuss open challenges that would inspire further research in this field.
Edge-based Privacy-Sensitive Live Learning for Discovery of Training Data

Finding true positives (TPs) to construct a training set for a new class of interest in machine learning (ML) is often a challenge. The novelty of the class suggests that cloud archives are unlikely to be helpful. We observe that most video data collected for surveillance and briefly stored at the edge before being overwritten is currently unused. To efficiently harness this untapped resource, we describe Delphi, a privacy-sensitive interactive labeling system that continuously improves labeling productivity through background learning. Our experimental results confirm the value of Delphi for training set construction from edge-sourced data.
Efficient Star-based Truss Maintenance on Dynamic Graphs

K-truss is a useful notion of dense subgraphs, which can represent cohesive parts of a graph in a hierarchical way. In practice, in order to enable various truss-based applications to answer queries faster, the edge trussnesses are computed in advance. However, real-world graphs may not always be static and often have edges inserted or removed, leading to costly truss maintenance of recomputing all edge trussnesses. In this paper, we focus on dynamic graphs with star insertions/deletions, where a star insertion can represent a newly joined user with friend connections in social networks or a recently published paper with cited references in citation networks. To tackle such star-based truss maintenance, we propose a new structure of AffBall based on the local structure of an inserted/deleted star motif. With AffBall, we make use of the correlation of inserted edges to compute the trussnesses of the inner edges surrounding the star. Then, we analyze the onion layer of k-truss and conduct truss maintenance for the edges beyond the star, which can be efficiently achieved with a time complexity related to the number of the edges that change the onion layer. Moreover, we extend star-based truss maintenance to handle general updates and single-edge insertions/deletions. Extensive experiments on real-world dynamic graphs verify the effectiveness and efficiency of proposed algorithms against state-of-the-art truss maintenance algorithms.
Bifurcations in the Dynamical System for Three-Layered Magnetic Valve

Magnetization dynamics in a three-layered nanopillar Co/Cu/Co structure with one fixed and one free layer driven by external magnetic field and spin-polarized electric current was investigated using methods of the theory of bifurcations. Mathematical model is based on the Landau-Lifshits-Gilbert equation with the current term in the Sloncžewski–Berger form. Orientation of applied magnetic field was considered to be parallel to the anisotropy axis. Physical model included the magnetocrystalline anisotropy field and the demagnetizing field. Because of small size of the structure, the space dependence of magnetization, as usually, was not taken into account. The resulting system of equations has the form of the nonlinear dynamical system with the polynomial right sides. Mathematical simulation of magnetization dynamics for several typical values of field and current was performed. The numerical experiments revealed the features of switching process in more detail and permitted to find new regimes of magnetization dynamics, such as incomplete and accidental switching.
Timing of dense granule biogenesis in asexual malaria parasites

Malaria is an important infectious disease that continues to claim hundreds of thousands of lives annually. The disease is caused by infection of host erythrocytes by apicomplexan parasites of the genus Plasmodium. The parasite contains three different apical organelles – micronemes, rhoptries and dense granules – whose contents are secreted to mediate binding to and invasion of the host cell and the extensive remodelling of the host cell that occurs following invasion. Whereas the roles of micronemes and rhoptries in binding and invasion of the host erythrocyte have been studied in detail, the role of dense granules (DGs) in Plasmodium parasites are poorly understood. They have been proposed to control host cell remodelling through regulated protein secretion after invasion, but many basic aspects of the biology of DGs remain unknown. Here we describe DG biogenesis timing for the first time, using RESA localisation as a proxy for DG formation timing. We show that DG formation commences approximately 37 minutes prior to schizont egress, as measured by the recruitment of the DG marker RESA. Furthermore, using a bioinformatics approach, we aimed to predict additional cargo of the DGs and identified the J-dot protein HSP40 as a DG protein, further supporting the very early role of these organelles in the interaction of the parasite with the host cell.
DNA Conserved in Diverse Animals Since the Precambrian Controls Genes for Embryonic Development

DNA that controls gene expression (e.g. enhancers, promoters) has seemed almost never to be conserved between distantly-related animals, like vertebrates and arthropods. This is mysterious, because development of such animals is partly organized by homologous genes with similar complex expression patterns, termed “deep homology”. Here we report twenty-five regulatory DNA segments conserved across bilaterian animals, of which seven are also conserved in cnidaria (coral and sea anemone). They control developmental genes (e.g. Nr2f, Ptch, Rfx1/3, Sall, Smad6, Sp5, Tbx2/3), including six homeobox genes: Gsx, Hmx, Meis, Msx, Six1/2, and Zfhx3/4. The segments contain perfectly or near-perfectly conserved CCAAT boxes, E-boxes, and other sequences recognized by regulatory proteins. More such DNA conservation will surely be found soon, as more genomes are published and sequence comparison is optimized. This reveals a control system for animal development conserved since the Precambrian.
Local prediction-learning in high-dimensional spaces enables neural networks to plan

Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations that it receives so that it can generalize to unseen parts. This speeds up learning of navigation in 2D mazes and the locomotion with complex actuator systems, such as legged bodies. The cognitive map learner that we propose does not require a teacher, similar to self-attention networks (Transformers). But in contrast to Transformers, it does not require backpropagation of errors or very large datasets for learning. Hence it provides a blue-print for future energy-efficient neuromorphic hardware that acquires advanced cognitive capabilities through autonomous on-chip learning. One Sentence Summary Local synaptic plasticity is all you need for learning to solve problems
Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, demonstrates that: i) more than 50% of all antibodies failed in one or more tests, ii) yet, ∼50-75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first such study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.