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The purpose of this report is to develop a model that gives the best daily trading strategy based only on the price data before the day. We want to provide traders with strategies that will enable them to maximize their returns.we first utilize the LSTM time series neural network model using the first 956 data sets are used for training , and the subsequent 300 test sets are used to evaluate the model accuracy, calculate the prediction error and word comparison plots, and assess the difference between the prediction performance of the model and the actual value,This method has a better credibility. Finally, based on this model, we construct the optimal trading strategy model with the maximum value of total assets held as the objective function, add constraints, and finally arrive at an investment value of $1000.

Papillary thyroid cancer (PTC) is the most common endocrine system tumor. FOXK2 is involved in the development of different types of cancers, however, its function has not been investigated in papillary thyroid cancer. In the present study, we demonstrated that FOXK2 expression was up-regulated in papillary thyroid carcinoma tissues compared with matched normal tissues. Importantly, we found that FOXK2 expression was significantly associated with the tumor size, T stage, and TNM stage. Furthermore, stable knockdown of FOXK2 markedly inhibited PTC cell proliferation, significantly increased the ratio of LC3-II/LC3-I, and reduced p62 expression, whereas overexpression of FOXK2 showed opposite effects. In FOXK2 knockdown cell lines, mCherry-GFP-LC3 immunofluorescence demonstrated increased punctate aggregates of mCherry-GFP-LC3, and transmission electron microscopy revealed increased numbers of autophagosomes. Autophagy-related protein ULK1, VPS34, and FOXO3 were markedly up-regulated by FOXK2 knockdown and down-regulated by FOXK2 overexpression. Finally, autophagy inhibitor 3-MA attenuated autophagy activation and rescued the inhibition of cell proliferation caused by FOXK2 knockdown, suggesting that FOXK2 silencing inhibits cell proliferation through up-regulating autophagy. These findings revealed an important role of FOXK2 in PTC progression and suggested that FOXK2 might be a potential new target for the diagnosis and treatment of PTC.

Spatiotemporally continuous long-term leaf area index (LAI) products are urgently needed to monitor environmental changes. The current filter- or curve-fitting-based time series reconstructive algorithms fail to reconstruct the LAI time series with many continuous missing values or missing values in key phenological periods, which are common issues in high-spatial-resolution LAI time series. In this article, a meteorological data-driven backpropagation neural network (MBPNN) was proposed to reconstruct discontinuous LAI profiles with a two-step process using vegetation phenological information. As the basis of the strong dependence of vegetation growth on meteorological conditions, a reasonable growth trajectory of reconstructed LAI can be guaranteed by the algorithm even though if many observed values are missing. Validations for reconstructed LAI were conducted both spatially and temporally based on reference maps and field-measured long-term observations. The results showed that the LAI predicted by the MBPNN had a similar accuracy (RMSE = 0.4076) as the Landsat LAI inversions (RMSE = 0.4083) and a similar reconstructed trajectory as the field-measured LAI series even though over 100 days of continuous data were missing (RMSE = 0.1620). A comparison with the Harmonic ANalysis of Time Series (HANTS) algorithm showed that the accuracy of MBPNN was more stable regardless of the size/position of the missing data, and the proposed method performed much better when the data were continuously missing for 50 days or more.

Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed computing (CDC), it has been shown that it is possible to enable coding opportunities across intermediate results of different computation tasks to further reduce the communication load. We propose a new scheme, named compressed coded distributed computing (in short, compressed CDC ), which jointly exploits the above two techniques (i.e., combining the intermediate results of the same computation and coding across the intermediate results of different computations) to significantly reduce the communication load for computations with linear aggregation (reduction) of intermediate results in the final stage that are prevalent in machine learning (e.g., distributed training algorithms where partial gradients are computed distributedly and then averaged in the final stage). In particular, compressed CDC first compresses/combines several intermediate results for a single computation, and then utilizes multiple such combined packets to create a coded multicast packet that is simultaneously useful for multiple computations. We characterize the achievable communication load of compressed CDC and show that it substantially outperforms both combining methods and CDC scheme. Based on the compressed CDC technique, we then study a distributed training problem as one of its application. We characterize the communication load for this distributed training problem and show that it is asymptotically optimal.

We study two problems of private matrix multiplication, over a distributed computing system consisting of a master node, and multiple servers that collectively store a family of public matrices using Maximum-Distance-Separable (MDS) codes. In the first problem of Private and Secure Matrix Multiplication (PSMM) from colluding servers, the master intends to compute the product of its confidential matrix $\mathbf{A}$ with a target matrix stored on the servers, without revealing any information about $\mathbf{A}$ and the index of target matrix to some colluding servers. In the second problem of Fully Private Matrix Multiplication (FPMM) from colluding servers, the matrix $\mathbf{A}$ is also selected from another family of public matrices stored at the servers in MDS form. In this case, the indices of the two target matrices should both be kept private from colluding servers. We develop novel strategies for the two PSMM and FPMM problems, which simultaneously guarantee information-theoretic data/index privacy and computation correctness. We compare the proposed PSMM strategy with a previous PSMM strategy with a weaker privacy guarantee (non-colluding servers), and demonstrate substantial improvements over the previous strategy in terms of communication and computation overheads. Moreover, compared with a baseline FPMM strategy that uses the idea of Private Information Retrieval (PIR) to directly retrieve the desired matrix multiplication, the proposed FPMM strategy significantly reduces storage overhead, but slightly incurs large communication and computation overheads., 16 pages, 6 figures

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on their local data, in a privacy-preserving manner. SwiftAgg+ can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most $D=o(N)$ dropout users, SwiftAgg+ achieves a per-user communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ symbols and a server communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ symbols, with a worst-case information-theoretic security guarantee, against any subset of up to $T=o(N)$ semi-honest users who may also collude with the curious server. Moreover, the proposed SwiftAgg+ allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for $T, Comment: arXiv admin note: substantial text overlap with arXiv:2202.04169

We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N$ distributed users, each of size $L$, trained on their local data, in a privacy-preserving manner. Compared with state-of-the-art secure aggregation protocols, SwiftAgg significantly reduces the communication overheads without any compromise on security. Specifically, in presence of at most $D$ dropout users, SwiftAgg achieves a users-to-server communication load of $(T+1)L$ and a users-to-users communication load of up to $(N-1)(T+D+1)L$, with a worst-case information-theoretic security guarantee, against any subset of up to $T$ semi-honest users who may also collude with the curious server. The key idea of SwiftAgg is to partition the users into groups of size $D+T+1$, then in the first phase, secret sharing and aggregation of the individual models are performed within each group, and then in the second phase, model aggregation is performed on $D+T+1$ sequences of users across the groups. If a user in a sequence drops out in the second phase, the rest of the sequence remain silent. This design allows only a subset of users to communicate with each other, and only the users in a single group to directly communicate with the server, eliminating the requirements of 1) all-to-all communication network across users; and 2) all users communicating with the server, for other secure aggregation protocols. This helps to substantially slash the communication costs of the system.

Psychological studies have generally shown that individuals with Autism Spectrum Disorder (ASD) have specificity in the processing of social information by using static or abstract images. Yet, a recent study showed that there was no differ in their use of the added social or non-social cues in dynamic interactive situations. To establish the cause of the inconsistent results, we explored the processing pattern of gaze cues in individuals with ASD by using chase detection paradigm and eye-tracking methodologies. In this study, unlike typical controls, participants with ASD showed no detection advantage under the oriented condition. The results suggested that individuals with ASD may utilize an atypical processing pattern in dynamic interactive situations.

The problem of $X$-secure $T$-colluding symmetric Private Polynomial Computation (PPC) from coded storage system with $B$ Byzantine and $U$ unresponsive servers is studied in this paper. Specifically, a dataset consisting of $M$ files is stored across $N$ distributed servers according to $(N,K+X)$ Maximum Distance Separable (MDS) codes such that any group of up to $X$ colluding servers can not learn anything about the data files. A user wishes to privately evaluate one out of a set of candidate polynomial functions over the $M$ files from the system, while guaranteeing that any $T$ colluding servers can not learn anything about the identity of the desired function and the user can not learn anything about the $M$ data files more than the desired polynomial function evaluations, in the presence of $B$ Byzantine servers that can send arbitrary responses maliciously to confuse the user and $U$ unresponsive servers that will not respond any information at all. A novel symmetric PPC scheme using Lagrange encoding is proposed. This scheme achieves a PPC rate of $1-\frac{G(K+X-1)+T+2B}{N-U}$ with secrecy rate $\frac{G(K+X-1)+T}{N-(G(K+X-1)+T+2B+U)}$ and finite field size $N+\max\{K,N-(G(K+X-1)+T+2B+U)\}$, where $G$ is the maximum degree over all the candidate polynomial functions. Moreover, to further measure the efficiency of PPC schemes, upload cost, query complexity, server computation complexity and decoding complexity required to implement the scheme are analyzed. Remarkably, the PPC setup studied in this paper generalizes all the previous MDS coded PPC setups and the degraded schemes strictly outperform the best known schemes in terms of (asymptotical) PPC rate, which is the main concern of the PPC schemes.

Silicon nitride (Si3N4) with a high α-phase content is in demand due to its higher thermal conductivity for use in heat dissipation bases for electronic devices. However, more needs to be understood about its fibrous growth, therefore, rich fibrous morphologies were synthesized with the assistance of Fe powder and NH4Cl. The effects of these additives on the phase content and fiber morphologies of the products were investigated The results illustrate that the Si3N4 products possess upper and lower layers and that when the Fe powder content is 4 wt% and NH4Cl content is 5 wt%, the Si3N4 fibers have smooth surfaces, uniform diameters, and no floating particles. Moreover, the maximum α-Si3N4 content reached 94.8 wt%, demonstrating an increase in this phase. The double mechanisms of vapor-liquid-solid and vapor-solid are presented as the growth mechanism at the fiber.

How can we optimally trade extra computing power to reduce the communication load in distributed computing? We answer this question by characterizing a fundamental tradeoff between computation and communication in distributed computing, i.e., the two are inversely proportional to each other. More specifically, a general distributed computing framework, motivated by commonly used structures like MapReduce, is considered, where the overall computation is decomposed into computing a set of "Map" and "Reduce" functions distributedly across multiple computing nodes. A coded scheme, named "Coded Distributed Computing" (CDC), is proposed to demonstrate that increasing the computation load of the Map functions by a factor of $r$ (i.e., evaluating each function at $r$ carefully chosen nodes) can create novel coding opportunities that reduce the communication load by the same factor. An information-theoretic lower bound on the communication load is also provided, which matches the communication load achieved by the CDC scheme. As a result, the optimal computation-communication tradeoff in distributed computing is exactly characterized. Finally, the coding techniques of CDC is applied to the Hadoop TeraSort benchmark to develop a novel CodedTeraSort algorithm, which is empirically demonstrated to speed up the overall job execution by $1.97\times$ - $3.39\times$, for typical settings of interest., To appear in IEEE Transactions on Information Theory

We consider a wireless distributed computing system, in which multiple mobile users, connected wirelessly through an access point, collaborate to perform a computation task. In particular, users communicate with each other via the access point to exchange their locally computed intermediate computation results, which is known as data shuffling. We propose a scalable framework for this system, in which the required communication bandwidth for data shuffling does not increase with the number of users in the network. The key idea is to utilize a particular repetitive pattern of placing the dataset (thus a particular repetitive pattern of intermediate computations), in order to provide coding opportunities at both the users and the access point, which reduce the required uplink communication bandwidth from users to access point and the downlink communication bandwidth from access point to users by factors that grow linearly with the number of users. We also demonstrate that the proposed dataset placement and coded shuffling schemes are optimal (i.e., achieve the minimum required shuffling load) for both a centralized setting and a decentralized setting, by developing tight information-theoretic lower bounds., Comment: To appear in IEEE/ACM Transactions on Networking

In this paper, we propose coded Merkle tree (CMT), a novel hash accumulator that offers a constant-cost protection against data availability attacks in blockchains, even if the majority of the network nodes are malicious. A CMT is constructed using a family of sparse erasure codes on each layer, and is recovered by iteratively applying a peeling-decoding technique that enables a compact proof for data availability attack on any layer. Our algorithm enables any node to verify the full availability of any data block generated by the system by just downloading a \(\varTheta (1)\) byte block hash commitment and randomly sampling \(\varTheta (\log b)\) bytes, where b is the size of the data block. With the help of only one connected honest node in the system, our method also allows any node to verify any tampering of the coded Merkle tree by just downloading \(\varTheta (\log b)\) bytes. We provide a modular library for CMT in Rust and Python and demonstrate its efficacy inside the Parity Bitcoin client.

We introduce an information-theoretic framework, named Coded State Machine (CSM), to securely and efficiently execute multiple state machines on untrusted network nodes, some of which are Byzantine. The standard method of solving this problem is using State Machine Replication, which achieves high security at the cost of low efficiency. We propose CSM, which achieves the optimal linear scaling in storage efficiency, throughput, and security simultaneously with the size of the network. The storage efficiency is scaled via the design of Lagrange coded states and coded input commands that require the same storage size as their origins. The computational efficiency is scaled using a novel delegation algorithm, called INTERMIX, which is an information-theoretically verifiable matrix-vector multiplication algorithm of independent interest. Using INTERMIX, the network nodes securely delegate their coding operations to a single worker node, and a small group of randomly selected auditor nodes verify its correctness, so that computational efficiency can scale almost linearly with the network size, without compromising on security.

This study explored student abilities in applying conceptual knowledge when presented with structured performance tasks. Specifically, the study gauged proficiency in higher-order applications of students enrolled in earth and environmental science or biology. The student sample was drawn from a Redesigned STEM high school model where a tested performance assessment protocol was employed for the purposes of the investigation. It was determined that performance-based proficiency was not uniform within tasks and applications, but could be recognized through student artifacts of learning on a situational basis. Based on the findings of the study, several implications are highlighted.

Human gingival fibroblasts (HGFs) are responsible for connective tissue repair and scarring, and are exposed to mechanical forces under physiological and pathological conditions. The exact mechanisms underlying gingival tissue reconstruction under mechanical forces remain unclear. The present study aimfed to investigate the effects of mechanical forces on the proliferation and extracellular matrix synthesis in HGFs by establishing a 3‑dimensional (3D) HGF culture model using poly(lactide‑co‑glycolide) (PLGA) scaffolds. HGFs were cultured in 3D PLGA scaffolds and a mechanical force of 0, 5, 15, 25 or 35 g/cm2 was applied to HGFs for 24 h. A mechanical force of 25 g/cm2 induced the highest proliferation rate, and thus was selected for subsequent experiments. Cell viability was determined using the MTT assay at 0, 24, 48 and 72 h. The expression levels of type I collagen (COL‑1) and matrix metallopeptidase (MMP)‑1 were examined by reverse transcription‑quantitative polymerase chain reaction and ELISA, and transforming growth factor (TGF)‑β expression was evaluated by ELISA. The application of mechanical force on HGFs cultured on the 3D PLGA scaffolds resulted in a significant increase in cell proliferation and COL‑1 expression, as well as a decrease in MMP‑1 expression. A TGF‑β1 inhibitor was also applied, which attenuated the effects of mechanical force on HGF proliferation, and COL‑1 and MMP‑1 expression, thus suggesting that TGF‑β signaling pathways may mediate the mechanical force‑induced alterations observed in HGFs. In conclusion, these findings helped to clarify the mechanisms underlying mechanical force‑induced HGF proliferation and ECM synthesis, which may promote the development of targeted therapeutics to treat various diseases, including gingival atrophy caused by orthodontic treatment.

Communication overhead is one of the major performance bottlenecks in large-scale distributed computing systems, in particular for machine learning applications. Conventionally, compression techniques are used to reduce the load of communication by combining intermediate results of the same computation task as much as possible. Recently, via the development of coded distributed computing (CDC), it has been shown that it is possible to enable coding opportunities across intermediate results of different computation tasks to further reduce the communication load. We propose a new scheme, named compressed coded distributed computing (in short, compressed CDC), which jointly exploits the above two techniques (i.e., combining the intermediate results of the same computation and coding across the intermediate results of different computations) to significantly reduce the communication load for computations with linear aggregation (reduction) of intermediate results in the final stage that are prevalent in machine learning (e.g., distributed training algorithms where partial gradients are computed distributedly and then averaged in the final stage). In particular, compressed CDC first compresses/combines several intermediate results for a single computation, and then utilizes multiple such combined packets to create a coded multicast packet that is simultaneously useful for multiple computations. We characterize the achievable communication load of compressed CDC and show that it substantially outperforms both combining methods and CDC scheme., Comment: A shorter version to appear in ISIT 2018

Today’s blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling blockchains to scale efficiency is the notion of sharding: different subsets of nodes handle different portions of the blockchain, thereby reducing the load for each individual node. However, existing sharding proposals achieve efficiency scaling by compromising on trust - corrupting the nodes in a given shard will lead to the permanent loss of the corresponding portion of data. In this paper, we settle the trilemma by demonstrating a new protocol for coded storage and computation in blockchains. In particular, we propose PolyShard : “polynomially coded sharding” scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system. We provide simulation results that numerically demonstrate the performance improvement over state of the arts, and the scalability of the PolyShard system. Finally, we discuss potential enhancements, and highlight practical considerations in building such a system.

Student interest is associated with persistence in STEM courses of study (Maltese, Melki, & Wiebke, 2014). If peers decide, of their own accord, to discuss knowledge among each other outside of the classroom context, the behavior is indicative of deepening interest in the information being shared (Renninger & Hidi, 2002). Understanding outside classroom knowledge sharing behaviors among peers involved in a STEM course may help educators construct learning contexts that promote interest and persistence in STEM subjects. To that end, this study examined two important research questions: (1) what are the key factors that influence peer to peer knowledge sharing outside the classroom? and (2) what are the methods the student use to share content knowledge? In order to explore these questions, a qualitative study was designed to explore knowledge sharing between peers outside the classroom. A semi-structured interview protocol with eight students from a Mid-Atlantic community college was conducted to explore students’ perceptions of knowledge sharing between peers. Data were coded and analyzed by a group of researchers and themes were identified and theoretical and practical implications of the study were recorded. Several key facilitators of knowledge sharing were identified: self-efficacy, interpersonal relationships, interpersonal similarity and media richness. Implications for teachers are presented. Limitations and future research are included in the end of the study. Keywords: knowledge sharing, peer to peer, peer learning, knowledge transfer, content knowledge, college science teaching, community college

We consider a mobile edge computing problem, in which mobile users offload their computation tasks to computing nodes (e.g., base stations) at the network edge. The edge nodes compute the requested functions and communicate the computed results to the users via wireless links. For this problem, we propose a Universal Coded Edge Computing (UCEC) scheme for linear functions to simultaneously minimize the load of computation at the edge nodes, and maximize the physical-layer communication efficiency towards the mobile users. In the proposed UCEC scheme, edge nodes create coded inputs of the users, from which they compute coded output results. Then, the edge nodes utilize the computed coded results to create communication messages that zero-force all the interference signals over the air at each user. Specifically, the proposed scheme is universal since the coded computations performed at the edge nodes are oblivious of the channel states during the communication process from the edge nodes to the users., To Appear in ISIT 2017

Redundancy is abundant in Fog networks (i.e., many computing and storage points) and grows linearly with network size. We demonstrate the transformational role of coding in Fog computing for leveraging such redundancy to substantially reduce the bandwidth consumption and latency of computing. In particular, we discuss two recently proposed coding concepts, namely Minimum Bandwidth Codes and Minimum Latency Codes, and illustrate their impacts in Fog computing. We also review a unified coding framework that includes the above two coding techniques as special cases, and enables a tradeoff between computation latency and communication load to optimize system performance. At the end, we will discuss several open problems and future research directions., To appear in IEEE Communications Magazine, Issue on Fog Computing and Networking

We focus on sorting, which is the building block of many machine learning algorithms, and propose a novel distributed sorting algorithm, named Coded TeraSort, which substantially improves the execution time of the TeraSort benchmark in Hadoop MapReduce. The key idea of Coded TeraSort is to impose structured redundancy in data, in order to enable in-network coding opportunities that overcome the data shuffling bottleneck of TeraSort. We empirically evaluate the performance of CodedTeraSort algorithm on Amazon EC2 clusters, and demonstrate that it achieves 1.97x - 3.39x speedup, compared with TeraSort, for typical settings of interest., to appear in proceedings of 2017 International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics

Today's data centers have an abundance of computing resources, hosting server clusters consisting of as many as tens or hundreds of thousands of machines. To execute a complex computing task over a data center, it is natural to distribute computations across many nodes to take advantage of parallel processing. However, as we allocate more and more computing resources to a computation task and further distribute the computations, large amounts of (partially) computed data must be moved between consecutive stages of computation tasks among the nodes, hence the communication load can become the bottleneck. In this paper, we study the optimal allocation of computing resources in distributed computing, in order to minimize the total execution time in distributed computing accounting for both the duration of computation and communication phases. In particular, we consider a general MapReduce-type distributed computing framework, in which the computation is decomposed into three stages: \emph{Map}, \emph{Shuffle}, and \emph{Reduce}. We focus on a recently proposed \emph{Coded Distributed Computing} approach for MapReduce and study the optimal allocation of computing resources in this framework. For all values of problem parameters, we characterize the optimal number of servers that should be used for distributed processing, provide the optimal placements of the Map and Reduce tasks, and propose an optimal coded data shuffling scheme, in order to minimize the total execution time. To prove the optimality of the proposed scheme, we first derive a matching information-theoretic converse on the execution time, then we prove that among all possible resource allocation schemes that achieve the minimum execution time, our proposed scheme uses the exactly minimum possible number of servers.

Modern learning algorithms use gradient descent updates to train inferential models that best explain data. Scaling these approaches to massive data sizes requires proper distributed gradient descent schemes where distributed worker nodes compute partial gradients based on their partial and local data sets, and send the results to a master node where all the computations are aggregated into a full gradient and the learning model is updated. However, a major performance bottleneck that arises is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. We propose a distributed computing scheme, called Batched Coupon's Collector (BCC) to alleviate the effect of stragglers in gradient methods. We prove that our BCC scheme is robust to a near optimal number of random stragglers. We also empirically demonstrate that our proposed BCC scheme reduces the run-time by up to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation strategies. We also generalize the proposed BCC scheme to minimize the completion time when implementing gradient descent-based algorithms over heterogeneous worker nodes.

We propose a unified coded framework for distributed computing with straggling servers, by introducing a tradeoff between "latency of computation" and "load of communication" for some linear computation tasks. We show that the coded scheme of [1]-[3] that repeats the intermediate computations to create coded multicasting opportunities to reduce communication load, and the coded scheme of [4], [5] that generates redundant intermediate computations to combat against straggling servers can be viewed as special instances of the proposed framework, by considering two extremes of this tradeoff: minimizing either the load of communication or the latency of computation individually. Furthermore, the latency-load tradeoff achieved by the proposed coded framework allows to systematically operate at any point on that tradeoff to perform distributed computing tasks. We also prove an information-theoretic lower bound on the latency-load tradeoff, which is shown to be within a constant multiplicative gap from the achieved tradeoff at the two end points., a shorter version to appear in NetCod 2016

We introduce a general distributed computing framework, motivated by commonly used structures like MapReduce, and formulate an information-theoretic tradeoff between computation and communication in such a framework. We characterize the optimal tradeoff to within a constant factor, for all system parameters. In particular, we propose a coded scheme, namely “Coded MapReduce” (CMR), which creates and exploits coding opportunities in data shuffling for distributed computing, reducing the communication load by a factor that is linearly proportional to the computation load. We then prove a lower bound on the minimum communication load, and demonstrate that CMR achieves this lower bound to within a constant factor. This result reveals a fundamental connection between computation and communication in distributed computing - the two are inverse-linearly proportional to each other.

Co-substituted U-type hexagonal ferrite bulks, with composition of Ba4Ni2−xCoxFe36O60 (x=0.2, 0.4, 0.6, 0.8), were prepared by a conventional ceramic method. Saturation magnetization (4πMs), coercivity (Hc), and Curie temperature (Tc) were investigated. Anisotropy constant (K1) was calculated by fitting the magnetization curve (M-H) according to the law of approach to saturation, and anisotropy field (Ha) was calculated accordingly. The results reveal that all the samples possess the U-type hexagonal crystallographic structure. With increasing cobalt substitution content (x), the lattice parameters (a and c) almost remain the same owing to the similar radii of Ni2+ (0.72 A) Co2+ (0.74 A) ions. 4πMs goes up, while Hc Hc shows an opposite trend. K1 and Ha monotonously decrease resulting from that cobalt substitution weakens the c-axis orientation. Additionally, Tc increases from 467 °C to 484 °C.

Professional development offerings assist K–12 educators in addressing new and evolving classroom dynamics, circumstances, and situations. With the emerging demands of an increasingly science, technology, engineering, and mathematics (STEM)-infused society, teachers are challenged to provide high-quality service and equitable educational opportunities to all STEM education students, particularly to those students who traditionally are underrepresented in comparison to their peers in STEM education and/or have aspirations of participation in STEM-related careers. This study investigated K–12 STEM educator participation and perceived utility regarding professional development addressing specific needs of students with identified categorical disabilities and limited English proficiency (LEP). Collection and analysis methods employed data retrieval and tabulation from the 2011–2012 School and Staffing Survey (SASS) Teacher Questionnaire (TQ). The national restricted access dataset was used to identify targeted teacher populations as well as provide a profile of STEM teacher participation in practice-oriented professional development activities regarding the two specified student groups. The results were categorically summarized and compared across science, technology, and mathematics (STM) disciplines and also between STM educators, non-STM educators, and educators in general. The results indicated that STM teachers tended to engage in fewer professional development opportunities and dedicated fewer hours in the professional development regarding students with categorical disabilities and LEP than the remainder of the teaching population. Overall, STM teachers’ perceived utility of the provided professional development experience was lower than that of the remainder of the teaching population.

To explore the effects of different concentrations of putrescine on the proliferation, migration and apoptosis of human skin fibroblasts (HSF).HSF cultured in the presence of 0.5, 1.0, 5.0, 10, 50, 100, 500, and 1000 µg/ putrescine for 24 h were examined for the changes in the cell proliferation, migration, and apoptosis using MTS assay, Transwell migration assay, and flow cytometry, respectively.Compared with the control cells, HSF cultured with 0.5, 1.0, 5.0, and 10 µg/ putrescine showed significantly increased cell proliferation (P0.01), and the effect was the most obvious with 1 µg/ putrescine, whereas 500 and 1000 µg/ putrescine significantly reduced the cell proliferation (P0.01); 50 and 100 µg/ did not obviously affect the cell proliferation (P0.05). Putrescine at 1 µg/ most significantly enhanced the cell migration (P0.01), while at higher doses (50, 100, 500, and 1000 µg/) putrescine significantly suppressed the cell migration (P0.05); 0.5, 5.0, and 10 µg/ putrescine produced no obvious effects on the cell migration (P0.05). HSF treated with 0.5, 1.0, 5.0, and 10 µg/ putrescine obvious lowered the cell apoptosis rate compared with the control group (P0.01), and the cell apoptosis rate was the lowest in cells treated with 1 µg/ putrescine; but at the concentrations of 100, 500, and 1000 µg/, putrescine significantly increased the cell apoptosis rate (P0.01), while 50 µg/ml putrescine produced no obvious effect on cell apoptosis (P0.05).Low concentrations of putrescine can obviously enhance the proliferation ability and maintain normal migration ability of HSF in vitro, but at high concentrations, putrescine can obviously inhibit the cell migration and proliferation and induce cells apoptosis, suggesting the different roles of different concentrations of putrescine in wound healing.

In this paper, we study the communication problem from rovers on Mars' surface to Mars-orbiting satellites. We first justify that, to a good extent, the rover-to-orbiter communication problem can be modelled as communication over a $2 \times 2$ X-channel with the network topology varying over time. For such a fading X-channel where transmitters are only aware of the time-varying topology but not the time-varying channel state (i.e., no CSIT), we propose coding strategies that code across topologies, and develop upper bounds on the sum degrees-of-freedom (DoF) that is shown to be tight under certain pattern of the topology variation. Furthermore we demonstrate that the proposed scheme approximately achieves the ergodic sum-capacity of the network. Using the proposed coding scheme, we numerically evaluate the ergodic rate gain over a time-division-multiple-access (TDMA) scheme for Rayleigh and Rice fading channels. We also numerically demonstrate that with practical orbital parameters, a 9.6% DoF gain, as well as more than 11.6% throughput gain can be achieved for a rover-to-orbiter communication network., 13 pages, 6 figures. Accepted by IEEE Transactions on Communications