Q1: What other ways to generate fake sequences may be suitable for this problem? A1: That is a good question. GAN to generate some more difficult fake sequences to further improve the ability of the encoder. Q1: Comparison with other state-of-the-art deep clustering methods which are not designed for time-series. A1: Following your suggestion, we compare our method with two state-of-the-art deep clustering methods (i.e., DEC (Xie et al., Table 1: Comparisons on 36 time series datasets (The No. of datasets is consistent with the one in Table 2 in main text)Dataset DEC(RI) IDEC(RI) DTCR(RI) DTCR(NMI) DTCR(ACC) Dataset DEC(RI) IDEC(RI) DTCR(RI) DTCR(NMI) DTCR(ACC)1 0.5817 0.6210 0.6868(0.0026)

Unlike traditional cameras, event cameras measure changes in light intensity and report differences. This paper examines the conditions necessary for other traditional sensors to admit eventified versions that provide adequate information despite outputting only changes. The requirements depend upon the regularity of the signal space, which we show may depend on several factors including structure arising from the interplay of the robot and its environment, the input-output computation needed to achieve its task, as well as the specific mode of access (synchronous, asynchronous, polled, triggered). Further, there are additional properties of stability (or non-oscillatory behavior) that can be desirable for a system to possess and that we show are also closely related to the preceding notions. This paper contributes theory and algorithms (plus a hardness result) that addresses these considerations while developing several elementary robot examples along the way.

We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response. We are excited to introduce ChatGPT to get users' feedback and learn about its strengths and weaknesses. During the research preview, usage of ChatGPT is free.

ZURICH, SWITZERLAND, February 24, 2022 -- Modulos AG is presenting its unique Data-Centric AI platform designed to speed the development of trustworthy AI applications and help companies comply with forthcoming AI regulatory frameworks. The Modulos platform is designed to serve users that need reliable machine learning (ML) models that can be easily integrated into their business activities. The platform ensures fairness, accuracy, and robustness during development and deployment. As machine-learning models are built, humans are kept in the loop, ethical considerations are always paramount, and there is a focus on explainable decision-making throughout the journey. Iterating data is the next frontier in improving the performance of AI applications while the search for the most optimal solution is delegated to automated machine learning (AutoML).

Hypergraph data appear and are hidden in many places in the modern age. They are data structure that can be used to model many real data examples since their structures contain information about higher order relations among data points. One of the main contributions of our paper is to introduce a new topological structure to hypergraph data which bears a resemblance to a usual metric space structure. Using this new topological space structure of hypergraph data, we propose several approaches to study community detection problem, detecting persistent features arising from homological structure of hypergraph data. Also based on the topological space structure of hypergraph data introduced in our paper, we introduce a modified nearest neighbors methods which is a generalization of the classical nearest neighbors methods from machine learning. Our modified nearest neighbors methods have an advantage of being very flexible and applicable even for discrete structures as in hypergraphs. We then apply our modified nearest neighbors methods to study sign prediction problem in hypegraph data constructed using our method.

A weighted directed network (WDN) is a directed graph in which each edge is associated to a unique value called weight. These networks are very suitable for modeling real-world social networks in which there is an assessment of one vertex toward other vertices. One of the main problems studied in this paper is prediction of edge weights in such networks. We introduce, for the first time, a metric geometry approach to studying edge weight prediction in WDNs. We modify a usual notion of WDNs, and introduce a new type of WDNs which we coin the term \textit{almost-weighted directed networks} (AWDNs). AWDNs can capture the weight information of a network from a given training set. We then construct a class of metrics (or distances) for AWDNs which equips such networks with a metric space structure. Using the metric geometry structure of AWDNs, we propose modified $k$ nearest neighbors (kNN) methods and modified support-vector machine (SVM) methods which will then be used to predict edge weights in AWDNs. In many real-world datasets, in addition to edge weights, one can also associate weights to vertices which capture information of vertices; association of weights to vertices especially plays an important role in graph embedding problems. Adopting a similar approach, we introduce two new types of directed networks in which weights are associated to either a subset of origin vertices or a subset of terminal vertices . We, for the first time, construct novel classes of metrics on such networks, and based on these new metrics propose modified $k$NN and SVM methods for predicting weights of origins and terminals in these networks. We provide experimental results on several real-world datasets, using our geometric methodologies.

In this paper, we overview the recently introduced general framework of Description Logic Based Dynamic Systems, which leverages Levesque's functional approach to model systems that evolve the extensional part of a description logic knowledge base by means of actions. This framework is parametric w.r.t. the adopted description logic and the progression mechanism. In this setting, we discuss verification and adversarial synthesis for specifications expressed in a variant of first-order mu-calculus, with a controlled form of quantification across successive states, and present key decidability results under the natural assumption of state-boundedness.