SDN-based virtual machine management for cloud data centers

Software-Defined Networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in Cloud Data Centers (DCs), even though network and server resources converge over the same infrastructure and typically over a single administrative entity, disjoint control mechanisms are used for their respective management. In this paper, we propose a unified server-network control mechanism for converged ICT environments. We present a SDN-based orchestration framework for live Virtual Machine (VM) management where server hypervisors exploit temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented and Mininet is used to evaluate the impact of diverse orchestration algorithms

A resource-saving collective approach to biomedical semantic role labeling

BACKGROUND: Biomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem. However, in BioSRL such an approach has not been attempted because it would require more training data to recognize the more specialized and diverse terms found in biomedical literature, increasing training time and computational complexity. RESULTS: We first constructed a collective BioSRL system based on MLN. This system, called collective BIOSMILE (CBIOSMILE), is trained on the BioProp corpus. To reduce the resources used in BioSRL training, we employ a tree-pruning filter to remove unlikely nodes from the parse tree and four argument candidate identifiers to retain candidate nodes in the tree. Nodes not recognized by any candidate identifier are discarded. The pruned annotated parse trees are used to train a resource-saving MLN-based system, which is referred to as resource-saving collective BIOSMILE (RCBIOSMILE). Our experimental results show that our proposed CBIOSMILE system outperforms BIOSMILE, which is the top BioSRL system. Furthermore, our proposed RCBIOSMILE maintains the same level of accuracy as CBIOSMILE using 92% less memory and 57% less training time. CONCLUSIONS: This greatly improved efficiency makes RCBIOSMILE potentially suitable for training on much larger BioSRL corpora over more biomedical domains. Compared to real-world biomedical corpora, BioProp is relatively small, containing only 445 MEDLINE abstracts and 30 event triggers. It is not large enough for practical applications, such as pathway construction. We consider it of primary importance to pursue SRL training on large corpora in the future

Maine Distributed Solar Valuation Study

During its 2014 session, the Maine Legislature enacted an Act to Support Solar Energy Development in Maine. P.L Chapter 562 (April 24, 2014) (codified at 35‐A M.R.S. §§ 3471‐3473) (“Act”). Section 1 of the Act contains the Legislative finding that it is in the public interest is to develop renewable energy resources, including solar energy, in a manner that protects and improves the health and well‐being of the citizens and natural environment of the State while also providing economic benefits to communities, ratepayers and the overall economy of the State. Section 2 of the Act requires the Public Utilities Commission (Commission) to determine the value of distributed solar energy generation in the State, evaluate implementation options, and to deliver a report to the Legislature. To support this work, the Commission engaged a project team comprising Clean Power Research (Napa, California), Sustainable Energy Advantage (Framingham, Massachusetts), Pace Energy and Climate Center at the Pace Law School (White Plains, New York), and Dr. Richard Perez (Albany, New York). Under the project, the team developed the methodology under a Commission‐run stakeholder review process, conducted a valuation on distributed solar for three utility territories, and developed a summary of implementation options for increasing deployment of distributed solar generation in the State. The report includes three volumes which accompany this Executive Summary: Volume I Methodology; Volume II Valuation Results; Volume III Implementation Options

EPG2S: Speech Generation and Speech Enhancement based on Electropalatography and Audio Signals using Multimodal Learning

Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the ability to speak. Although various techniques have been proposed to this end, electropalatography (EPG), which is a monitoring technique that records contact between the tongue and hard palate during speech, has not been adequately explored. Herein, we propose a novel multimodal EPG-to-speech (EPG2S) system that utilizes EPG and speech signals for speech generation and enhancement. Different fusion strategies based on multiple combinations of EPG and noisy speech signals are examined, and the viability of the proposed method is investigated. Experimental results indicate that EPG2S achieves desirable speech generation outcomes based solely on EPG signals. Further, the addition of noisy speech signals is observed to improve quality and intelligibility. Additionally, EPG2S is observed to achieve high-quality speech enhancement based solely on audio signals, with the addition of EPG signals further improving the performance. The late fusion strategy is deemed to be the most effective approach for simultaneous speech generation and enhancement.Comment: Accepted By IEEE Signal Processing Lette

SIGMA: spectral interpretation using gaussian mixtures and autoencoder

Identification of unknown micro- and nano-sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope—energy dispersive X-ray spectroscopy (SEM-EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non-ideal sample preparation, and the presence of mixed chemical signals generated within the electron-beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine-learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel-wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non-negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM-EDS data sets: a synthetic mixture sample and a real-world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background-subtracted single-phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe-bearing particles and their background-subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user-friendly Python code with a graphical user interface for ease of use by general users

Angular-Independent Photonic Pigments via the Controlled Micellization of Amphiphilic Bottlebrush Block Copolymers.

Photonic materials with angular-independent structural color are highly desirable because they offer the broad viewing angles required for application as colorants in paints, cosmetics, textiles, or displays. However, they are challenging to fabricate as they require isotropic nanoscale architectures with only short-range correlation. Here, porous microparticles with such a structure are produced in a single, scalable step from an amphiphilic bottlebrush block copolymer. This is achieved by exploiting a novel "controlled micellization" self-assembly mechanism within an emulsified toluene-in-water droplet. By restricting water permeation through the droplet interface, the size of the pores can be precisely addressed, resulting in structurally colored pigments. Furthermore, the reflected color can be tuned to reflect across the full visible spectrum using only a single polymer (Mn  = 290 kDa) by altering the initial emulsification conditions. Such "photonic pigments" have several key advantages over their crystalline analogues, as they provide isotropic structural coloration that suppresses iridescence and improves color purity without the need for either refractive index matching or the inclusion of a broadband absorber.National Natural Science Foundation of China [51873098] Winton Programme for the Physics of Sustainabilit

Antibodies Elicited by Inactivated Propionibacterium acnes-Based Vaccines Exert Protective Immunity and Attenuate the IL-8 Production in Human Sebocytes: Relevance to Therapy for Acne Vulgaris

Propionibacterium acnes is a key pathogen involved in the progression of inflammation in acne vulgaris. We examined whether vaccination against P. acnes suppressed P. acnes-induced skin inflammation. Inactivation of P. acnes with heat was employed to create a P. acnes-based vaccine. Intranasal immunization in mice with this inactivated vaccine provoked specific antibodies against P. acnes. Most notably, immunization with inactivated vaccines generated in vivo protective immunity against P. acnes challenge and facilitated the resolution of ear inflammation in mice. In addition, antibodies elicited by inactivated vaccines effectively neutralized the cytotoxicity of P. acnes and attenuated the production of proinflammatory cytokine IL-8 in human sebocyte SZ95 cells. Intranasal immunization using heat-inactivated P. acnes-based vaccines provided a simple modality to develop acne vaccines. These observations highlight the concept that development of vaccines targeting microbial products may represent an alternative strategy to conventional antibiotic therapy

Synergistic policy and virtual machine consolidation in cloud data centers

In modern Cloud Data Centers (DC)s, correct implementation of network policies is crucial to provide secure, efficient and high performance services for tenants. It is reported that the inefficient management of network policies accounts for 78% of DC downtime, challenged by the dynamically changing network characteristics and by the effects of dynamic Virtual Machine (VM) consolidation. While there has been significant research in policy and VM management, they have so far been treated as disjoint research problems. In this paper, we explore the simultaneous, dynamic VM and policy consolidation, and formulate the Policy-VM Consolidation (PVC) problem, which is shown to be NP-Hard. We then propose Sync, an efficient and synergistic scheme to jointly consolidate network policies and virtual machines. Extensive evaluation results and a testbed implementation of our controller show that policy and VM migration under Sync significantly reduces flow end-to-end delay by nearly 40%, and network-wide communication cost by 50% within few seconds, while adhering strictly to the requirements of network policies