Mercurial Signatures for Variable-Length Messages

Mercurial signatures are a useful building block for privacy-preserving schemes, such as anonymous credentials, delegatable anonymous credentials, and related applications. They allow a signature σ on a message m under a public key pk to be transformed into a signature σ′ on an equivalent message m′ under an equivalent public key pk′ for an appropriate notion of equivalence. For example, pk and pk′ may be unlinkable pseudonyms of the same user, and m and m′ may be unlinkable pseudonyms of a user to whom some capability is delegated. The only previously known construction of mercurial signatures suffers a severe limitation: in order to sign messages of length ℓ, the signer’s public key must also be of length ℓ. In this paper, we eliminate this restriction and provide an interactive signing protocol that admits messages of any length. We prove our scheme existentially unforgeable under chosen open message attacks (EUF-CoMA) under a variant of the asymmetric bilinear decisional Diffie-Hellman assumption (ABDDH).

CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction

Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development. In this task, some relational facts can only be inferred from the document rather than single sentence. Recently, researchers investigate graph-based approaches to extract relations across sentences. It iteratively combines the information from neighbor nodes to model the interactions in entity mentions that exist in different sentences. Despite their success, one severe limitation of the graph-based approaches is the over-smoothing problem, which decreases the model distinguishing ability. In this paper, we propose CID-GCN, an effective Graph Convolutional Networks (GCNs) with gating mechanism, for CID relation extraction. Specifically, we construct a heterogeneous graph which contains mention, sentence and entity nodes. Then, the graph convolution operation is employed to aggregate interactive information on the constructed graph. Particularly, we combine gating mechanism with the graph convolution operation to address the over-smoothing problem. The experimental results demonstrate that our approach significantly outperforms the baselines.

Holding Geostationary Satellite at Given Standing Point, Taking into Account Additional Phase Restrictions

The active lifetime of orbital facilities in the geostationary orbit (GSO), which include stationary artificial earth satellites (SAES) for various purposes, can be more than 15 years. At the same time, in modern conditions of orbital grouping increment, the number of space debris, including those on the GSO, also increases: SAES, which have finished its active lifetime and were not transferred to disposal orbit for some reasons, shards of SAES appeared from collision with meteors or accidents. This leads to the increase of probability of collisions with active SAESs. The listed factors determine the need of considering not only the problem of keeping SAESs in vicinities of position, but also the task of avoiding collisions with space debris objects (SDO), while the costs of the working fluid should not increase. A great attention is being paid to rational power units placing during the projection of new space shuttles, especially those with long useful lifetime. In this article, it is assumed that SESs are equipped with several correction motors, which make it possible to create control accelerations in only several directions, without changing the orientation of the SES itself. In other words, in this task it is assumed that the corrections of the parameters of the AES orbit do not affect the orientation of the SAES itself. This condition is a severe limitation in the synthesis of the SES’s control system. In the considered methodological approach, the costs of the working fluid are set as a functionality from control, which are necessary to perform the next correction, after which the SAES will not have dangerous distances and approaching in projection horizon’s interval. This makes it possible to avoid situations when the decision on control is being made after the SES leaves the vicinity of the station point, and first of all, the approach to the SDO at a distance less than a liminal one. This article provides the results of modeling, which indicate the effectiveness of the proposed solutions. An important advantage compared with the existing methods is the consideration of the movement of the SAES relatively not only to the stationary point, but also to several other objects located in its vicinity, both controlled and uncontrolled. Moreover, there can be any given number of objects.

The process for individuating TRIZ Inventive Principles: deterministic, stochastic or domain-oriented?

Although TRIZ is widely acknowledged as a powerful aid to improve efficacy and efficiency of the creative design process, practitioners diffusedly experience difficulties in the selection of the most suitable tool. Such an issue represents a severe limitation in consideration of the large number of tools TRIZ offers. Here, Inventive Principles (IPs) are acknowledged as the most popular TRIZ technique, and their conjointly use with the Contradiction Matrix makes the selection of the appropriate IP a sufficiently supported task. However, the reliability of the Contradiction Matrix is often questioned and an agreement on a solid and reliable procedure for the selection of IPs is far from being reached. In such a context, the paper investigates the recurrence of IPs to solve contradictions, with reference to a classification framework that takes into consideration the nature of the problem to be solved and the technical-scientific domain it belongs to. The outcomes of the analysis reveal that leveraged IPs are considerably related with the technical-scientific domain and the nature of the problem to be solved. The found relationships are worth delving into and translating into selection guidelines.

An Innovative Hydrogen Peroxide-Sensing Scaffold and Insights Towards its Potential as an ROS-Activated Persulfide Donor

Reactive sulfur species, such as hydrogen sulfide, persulfides, and polysulfides, have recently emerged as key signaling molecules and important physiological mediators within mammalian systems. To further assess the therapeutic potential of their exogenous administration, we report on the development of a unique hydrogen peroxide (H₂O₂)-sensing motif and its capacity for providing cellular protection against oxidative stress while serving as a reactive oxygen species (ROS)-activated persulfide donor. With the strategic implementation of a <i>gem</i>-dimethyl group that promotes both cyclization and stability, we found the initial rate of payload release from this newly derived scaffold to be directly proportional to the concentration of H₂O₂ and to proceed via an unprecedented pathway that avoids the production of electrophilic byproducts, a severe limitation that has plagued the physiological application of previous designs.

An Innovative Hydrogen Peroxide-Sensing Scaffold and Insights Towards its Potential as an ROS-Activated Persulfide Donor

Reactive sulfur species, such as hydrogen sulfide, persulfides, and polysulfides, have recently emerged as key signaling molecules and important physiological mediators within mammalian systems. To further assess the therapeutic potential of their exogenous administration, we report on the development of a unique hydrogen peroxide (H₂O₂)-sensing motif and its capacity for providing cellular protection against oxidative stress while serving as a reactive oxygen species (ROS)-activated persulfide donor. With the strategic implementation of a <i>gem</i>-dimethyl group that promotes both cyclization and stability, we found the initial rate of payload release from this newly derived scaffold to be directly proportional to the concentration of H₂O₂ and to proceed via an unprecedented pathway that avoids the production of electrophilic byproducts, a severe limitation that has plagued the physiological application of previous designs.

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.

Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View

Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing issue (indistinguishable representations of nodes in different classes). In this work, we present a systematic and quantitative study on the over-smoothing issue of GNNs. First, we introduce two quantitative metrics, MAD and MADGap, to measure the smoothness and over-smoothness of the graph nodes representations, respectively. Then, we verify that smoothing is the nature of GNNs and the critical factor leading to over-smoothness is the low information-to-noise ratio of the message received by the nodes, which is partially determined by the graph topology. Finally, we propose two methods to alleviate the over-smoothing issue from the topological view: (1) MADReg which adds a MADGap-based regularizer to the training objective; (2) AdaEdge which optimizes the graph topology based on the model predictions. Extensive experiments on 7 widely-used graph datasets with 10 typical GNN models show that the two proposed methods are effective for relieving the over-smoothing issue, thus improving the performance of various GNN models.

Behold the Tree: An Exploration of the Social Integration of Boys on the Autistic Spectrum in a Mainstream Primary School through a Dramatherapy Intervention

This article examines the process of a dramatherapy group with three boys on the autistic spectrum who attended a mainstream school. The boys were part of a mixed group where their neuro-typical peers were both group participants and witnesses. The main body of the article focuses on eight sessions based around the theme of ‘the tree’ which proved to be a turning point for all three boys when with growing confidence they were able to express their emotional needs. Thoughts are shared on ‘theory of mind’ and why the lack of this quintessential quality for those on the autistic spectrum places severe limitation on their social functioning.

The Correlation of Decreased Heart Rate Recovery and Chronotropic Incompetence with Exercise Capacity in Idiopathic Pulmonary Arterial Hypertension Patients

We show by this study that a decrease in HRR1 in IPAH patients is associated with severe limitation of exercise capacity. HRR1 < 16 beats and CI just after completion of a CPET could be an indicator of poor prognosis.