Miracle-dz/UniFAR-FTUs huggingface dataset:
One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers

Learning from human demonstrations (behavior cloning) is a cornerstone of robot learning. However, most behavior cloning algorithms require a large number of demonstrations to learn a task, especially for general tasks that have a large variety of initial conditions. Humans, however, can learn to complete tasks, even complex ones, after only seeing one or two demonstrations. Our work seeks to emulate this ability, using behavior cloning to learn a task given only a single human demonstration. We achieve this goal by using linear transforms to augment the single demonstration, generating a set of trajectories for a wide range of initial conditions. With these demonstrations, we are able to train a behavior cloning agent to successfully complete three block manipulation tasks. Additionally, we developed a novel addition to the temporal ensembling method used by action chunking agents during inference. By incorporating the standard deviation of the action predictions into the ensembling method, our approach is more robust to unforeseen changes in the environment, resulting in significant performance improvements.
Characterizing morphic sequences

Morphic sequences form a natural class of infinite sequences, extending the well-studied class of automatic sequences. Where automatic sequences are known to have several equivalent characterizations and the class of automatic sequences is known to have several closure properties, for the class of morphic sequences similar closure properties are known, but only limited equivalent characterizations. In this paper we extend the latter. We discuss a known characterization of morphic sequences based on automata and we give a characterization of morphic sequences by finiteness of a particular class of subsequences. Moreover, we relate morphic sequences to rationality of infinite terms and describe them by infinitary rewriting.
Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons

Vehicles in platoons need to process many tasks to support various real-time vehicular applications. When a task arrives at a vehicle, the vehicle may not process the task due to its limited computation resource. In this case, it usually requests to offload the task to other vehicles in the platoon for processing. However, when the computation resources of all the vehicles in the platoon are insufficient, the task cannot be processed in time through offloading to the other vehicles in the platoon. Vehicular fog computing (VFC)-assisted platoon can solve this problem through offloading the task to the VFC which is formed by the vehicles driving near the platoon. Offloading delay is an important performance metric, which is impacted by both the offloading strategy for deciding where the task is offloaded and the number of the allocated vehicles in VFC to process the task. Thus, it is critical to propose an offloading strategy to minimize the offloading delay. In the VFC-assisted platoon system, vehicles usually adopt the IEEE 802.11p distributed coordination function (DCF) mechanism while having various computation resources. Moreover, when vehicles arrive and depart the VFC randomly, their tasks also arrive at and depart the system randomly. In this paper, we propose a semi-Markov decision process (SMDP) based offloading strategy while considering these factors to obtain the maximal long-term reward reflecting the offloading delay. Our research provides a robust strategy for task offloading in VFC systems, its effectiveness is demonstrated through simulation experiments and comparison with benchmark strategies.
A Modular Spatial Clustering Algorithm with Noise Specification

Clustering techniques have been the key drivers of data mining, machine learning and pattern recognition for decades. One of the most popular clustering algorithms is DBSCAN due to its high accuracy and noise tolerance. Many superior algorithms such as DBSCAN have input parameters that are hard to estimate. Therefore, finding those parameters is a time consuming process. In this paper, we propose a novel clustering algorithm Bacteria-Farm, which balances the performance and ease of finding the optimal parameters for clustering. Bacteria- Farm algorithm is inspired by the growth of bacteria in closed experimental farms - their ability to consume food and grow - which closely represents the ideal cluster growth desired in clustering algorithms. In addition, the algorithm features a modular design to allow the creation of versions of the algorithm for specific tasks / distributions of data. In contrast with other clustering algorithms, our algorithm also has a provision to specify the amount of noise to be excluded during clustering.
Improved Nonnegativity Testing in the Bernstein Basis via Geometric Means

We develop a new kind of nonnegativity certificate for univariate polynomials on an interval. In many applications, nonnegative Bernstein coefficients are often used as a simple way of certifying polynomial nonnegativity. Our proposed condition is instead an explicit lower bound for each Bernstein coefficient in terms of the geometric mean of its adjacent coefficients, which is provably less restrictive than the usual test based on nonnegative coefficients. We generalize to matrix-valued polynomials of arbitrary degree, and we provide numerical experiments suggesting the practical benefits of this condition. The techniques for constructing this inexpensive certificate could potentially be applied to other semialgebraic feasibility problems.
CloudSimSC: A Toolkit for Modeling and Simulation of Serverless Computing Environments

Serverless computing is gaining traction as an at-tractive model for the deployment of a multitude of work-loads in the cloud. Designing and building effective resource management solutions for any computing environment requires extensive long term testing, experimentation and analysis of the achieved performance metrics. Utilizing real test beds and serverless platforms for such experimentation work is often times not possible due to resource, time and cost constraints. Thus, employing simulators to model these environments is key to overcoming the challenge of examining the viability of such novel ideas for resource management. Existing simulation software developed for serverless environments lack generalizibility in terms of their architecture as well as the various aspects of re-source management, where most are purely focused on modeling function performance under a specific platform architecture. In contrast, we have developed a serverless simulation model with induced flexibility in its architecture as well as the key resource management aspects of function scheduling and scaling. Further, we incorporate techniques for easily deriving monitoring metrics required for evaluating any implemented solutions by users. Our work is presented as CloudSimSC, a modular extension to CloudSim which is a simulator tool extensively used for modeling cloud environments by the research community. We discuss the implemented features in our simulation tool using multiple use cases.
Virome of high-altitude canine digestive tract and genetic characterization of novel viruses potentially threatening human health

ABSTRACT The majority of currently emerging infectious illnesses are zoonotic infections, which have caused serious public health and economic implications. The development of viral metagenomics has helped us to explore unknown viruses. We collected 1,970 canine feces from Yushu and Guoluo in the plateau region of China for this study to do a metagenomics analysis of the viral community of the canine digestive tract. Our analysis identified 203 novel viruses, classified into 11 known families and 2 unclassified groups. These viruses include the hepatitis E virus, first identified in dogs, and the astrovirus, coronavirus, polyomavirus, and others. The relationship between the newly identified canine viruses and known viruses was investigated through the use of phylogenetic analysis. Furthermore, we demonstrated the cross-species transmission of viruses and predicted new viruses that may cause diseases in both humans and animals, providing technical support for the prevention and control of diseases caused by environmental pollution viruses. IMPORTANCE Most emerging infectious diseases are due to zoonotic disease agents. Because of their effects on the security of human or animal life, agriculture production, and food safety, zoonotic illnesses and livestock diseases are of worldwide significance. Because dogs are closely related to humans and domestic animals, they serve as one of the important links in the transmission of zoonotic and livestock diseases. Canines can contaminate the environment in which humans live such as water and soil through secretions, potentially altering the human gut microbiota or causing diseases. Our study enriched the viral community in the digestive tract microbiome of dogs and found types of viruses that threaten human health, providing technical support for the prevention and control of early warning of diseases caused by environmental contaminant viruses. Most emerging infectious diseases are due to zoonotic disease agents. Because of their effects on the security of human or animal life, agriculture production, and food safety, zoonotic illnesses and livestock diseases are of worldwide significance. Because dogs are closely related to humans and domestic animals, they serve as one of the important links in the transmission of zoonotic and livestock diseases. Canines can contaminate the environment in which humans live such as water and soil through secretions, potentially altering the human gut microbiota or causing diseases. Our study enriched the viral community in the digestive tract microbiome of dogs and found types of viruses that threaten human health, providing technical support for the prevention and control of early warning of diseases caused by environmental contaminant viruses.
Edge Caching Placement Strategy based on Evolutionary Game for Conversational Information Seeking in Edge Cloud Computing

In Internet applications, network conversation is the primary communication between the user and server. The server needs to efficiently and quickly return the corresponding service according to the conversation sent by the user to improve the users’ Quality of Service. Thus, Conversation Information Seeking (CIS) research has become a hot topic today. In Cloud Computing (CC), a central service mode, the conversation is transmitted between the user and the remote cloud over a long distance. With the explosive growth of Internet applications, network congestion, long-distance communication, and single point of failure have brought new challenges to the centralized service mode. People put forward Edge Cloud Computing (ECC) to meet the new challenges of the centralized service mode of CC. As a distributed service mode, ECC is an extension of CC. By migrating services from the remote cloud to the network edge closer to users, ECC can solve the above challenges in CC well. In ECC, people solve the problem of CIS through edge caching. The current research focuses on designing the edge cache strategy to achieve more predictable caching. In this paper, we propose an edge cache placement method Evolutionary Game based Caching Placement Strategy (EG-CPS). This method consists of three modules: the user preference prediction module, the content popularity calculation module, and the cache placement decision module. To maximize the predictability of the cache strategy, we are committed to optimizing the cache hit rate and service latency. The simulation experiment compares the proposed strategy with several other cache strategies. The experimental results illustrate that EG-CPS can reduce up to 2.4% of the original average content request latency, increase the average direct cache hit rate by 1.7%, and increase the average edge cache hit rate by 3.3%.
Primal-Dual ℓ 0 -Constrained Sparse Index Tracking

—Sparse index tracking is one of the prominent passive portfolio management strategies that construct a sparse portfolio to track a financial index. A sparse portfolio is desirable over a full portfolio in terms of transaction cost reduction and avoiding illiquid assets. To enforce the sparsity of the portfolio, conventional studies have proposed formulations based on ℓ p -norm regularizations as a continuous surrogate of the ℓ 0 - norm regularization. Although such formulations can be used to construct sparse portfolios, they are not easy to use in actual investments because parameter tuning to specify the exact upper bound on the number of assets in the portfolio is delicate and time-consuming. In this paper, we propose a new problem formulation of sparse index tracking using an ℓ 0 -norm constraint that enables easy control of the upper bound on the number of assets in the portfolio. In addition, our formulation allows the choice between portfolio sparsity and turnover sparsity constraints, which also reduces transaction costs by limiting the number of assets that are updated at each rebalancing. Furthermore, we develop an efficient algorithm for solving this problem based on a primal-dual splitting method. Finally, we illustrate the effectiveness of the proposed method through experiments on the S&P500 and NASDAQ100 index datasets.
Data Formulator: AI-Powered Concept-Driven Visualization Authoring

With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.
Implicit Incorporation of Heuristics in MPC-Based Control of a Hydrogen Plant

The replacement of fossil fuels in combination with an increasing share of renewable energy sources leads to an increased focus on decentralized microgrids. One option is the local production of green hydrogen in combination with fuel cell vehicles (FCVs). In this paper, we develop a control strategy based on Model Predictive Control (MPC) for an energy management system (EMS) of a hydrogen plant, which is currently under installation in Offenbach, Germany. The plant includes an electrolyzer, a compressor, a low pressure storage tank, and six medium pressure storage tanks with complex heuristic physical coupling during the filling and extraction of hydrogen. Since these heuristics are too complex to be incorporated into the optimal control problem (OCP) explicitly, we propose a novel approach to do so implicitly. First, the MPC is executed without considering them. Then, the so-called allocator uses a heuristic model (of arbitrary complexity) to verify whether the MPC’s plan is valid. If not, it introduces additional constraints to the MPC’s OCP to implicitly respect the tanks’ pressure levels. The MPC is executed again and the new plan is applied to the plant. Simulation results with real-world measurement data of the facility’s energy management and realistic fueling scenarios show its advantages over rule-based control.
The smallest functional antibody fragment: Ultralong CDR H3 antibody knob regions potently neutralize SARS-CoV-2

Significance Cows produce antibodies with unusually long CDR H3 regions that form their own unique disulfide bonded “knob” structure that protrudes far from the typical antibody surface. Potently neutralizing ultralong CDR H3 antibodies targeting coronavirus have knobs that can be produced independently of the antibody and maintain their function. These CDR H3-derived knob regions are the smallest antibody fragments with broad applications in biomedicine.
Approaches to Species Distribution Modelling in Dinosaurs and Other Fossil Organisms

SDM describes a family of methods aiming to predict the geographic distribution of organisms using environmental data such as climate variables. It is a versatile tool in estimating ecological communities and interactions both spatially and through time. However, it has had limited utility in predicting the geographic distribution of fossil organisms. Due to preservation and sampling biases, fossil data does not satisfy the assumptions of species distribution models. Here is presented an analysis proposing a new methodology, which substitutes the conventional pseudoabsence data for fossil occurrences from non-target species. This approach eliminates variation in preservation and sampling, and returns a SDM which performs better than a conventional example. This model concludes that the ceratopsian Triceratops may have been widespread across North America, and beyond the region where its fossils have been recovered. The geographic distribution of Triceratops appears to have been governed by seasonality in temperature and temperature of the coldest month.
Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning

The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems.
Logic Design of Neural Networks for High-Throughput and Low-Power Applications

Neural networks (NNs) have been successfully deployed in various fields. In NNs, a large number of multiply-accumulate (MAC) operations need to be performed. Most existing digital hardware platforms rely on parallel MAC units to accelerate these MAC operations. However, under a given area constraint, the number of MAC units in such platforms is limited, so MAC units have to be reused to perform MAC operations in a neural network. Accordingly, the throughput in generating classification results is not high, which prevents the application of traditional hardware platforms in extreme-throughput scenarios. Besides, the power consumption of such platforms is also high, mainly due to data movement. To overcome this challenge, in this paper, we propose to flatten and implement all the operations at neurons, e.g., MAC and ReLU, in a neural network with their corresponding logic circuits. To improve the throughput and reduce the power consumption of such logic designs, the weight values are embedded into the MAC units to simplify the logic, which can reduce the delay of the MAC units and the power consumption incurred by weight movement. The retiming technique is further used to improve the throughput of the logic circuits for neural networks. In addition, we propose a hardware-aware training method to reduce the area of logic designs of neural networks. Experimental results demonstrate that the proposed logic designs can achieve high throughput and low power consumption for several high-throughput applications.
Electroencephalography–functional magnetic resonance imaging for clinical evaluation in focal epilepsy

Abstract Objective We aimed to evaluate the contribution of simultaneous recording of electroencephalography–functional magnetic resonance imaging (EEG‐fMRI) in the diagnosis of epilepsy syndrome, localization of the epileptogenic zone (EZ), and decision‐making regarding surgical treatment. Methods We performed a retrospective study to evaluate patients with focal epilepsy who underwent EEG‐fMRI. Two evaluators assessed epilepsy syndrome, presumed focus, and surgical candidacy and defined confidence levels. They assessed these clinical characteristics first without EEG‐fMRI and then including EEG‐fMRI to assess how the results of EEG‐fMRI changed the evaluations. We also determined how the clinical evaluation was affected by the concordance level between the blood oxygen level‐dependent (BOLD) response and the presumed focus location, and by the confidence level of the BOLD response itself based on the t‐value of the primary and secondary clusters. Results Fifty‐one scans from 48 patients were included. The BOLD map affected 66.7% of the evaluations by altering evaluation items (epilepsy syndrome, presumed focus, or surgical candidacy) or their confidence levels. EEG‐fMRI results increased the confidence levels of epilepsy syndrome, presumed focus, or surgical candidacy in 47.1% of patients but reduced clinical confidence in these features in 11.8%. More specifically, the confidence levels increased for epilepsy syndrome in 28.5%, identification of presumed focus in 33.9%, and determination of surgical candidacy in 29.4%. The BOLD signal confidence level, whether high or low, did not influence these clinical factors. Significance Previous studies have emphasized the utility of EEG‐fMRI for the localization of the epileptogenic zone. This study demonstrated the potential of EEG‐fMRI to influence clinical confidence when determining epilepsy syndrome, the presumed epileptic focus, and surgical candidacy.