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Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a rare form of cytotoxic T-cell lymphoma. The objective of this study was to characterize the clinical presentation, treatment, and prognosis of patients with SPTCL. Twenty-one patients with SPTCL were seen at Mayo Clinic (Rochester, Minnesota, USA) between July 1973 and June 2004. The median age at diagnosis was 42 years (range 23-80 years) and 15 (71%) were women. Constitutional symptoms occurred in 14 (67%) patients, including fever, serositis, arthralgias and myalgias. The Eastern Cooperative Oncology Group performance score was poor (3-4) in 3 (15%) patients. Liver enzymes (at least 2 enzymes, Aspartate aminotransferase (AST), alkaline phosphatase and/or lactate dehydrogenase) were elevated in 11 (52%) patients. Therapy consisted of chemotherapy in 13 (62%) patients, or other therapeutic interventions in 8 (38%) patients, including surgical excision, corticosteroids alone or in combination with either plaquenil, colchicine, hydroxychoroquine, or azathioprine. Bone marrow transplantation was performed in 5 (24%) patients, 3 autologous and 2 allogeneic. The median overall survival from diagnosis was 15 months (range 0.1-104 months). Two groups of patients were identified and categorized as having a favorable or unfavorable disease course. The factors associated with an unfavorable disease course were a low white blood cell count or elevated lactate dehydrogenase. Patients treated aggressively with stem cell transplantation appeared to have an improved overall survival.

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Abstract-The quality of the data being analyzed is a critical factor that affects the accuracy of data mining algorithms. There are two important aspects of the data quality, one is relevance and the other is data redundancy. The inclusion of irrelevant and redundant features in the data mining model results in poor predictions and high computational overhead. This paper presents an efficient method concerning both the relevance of the features and the pairwise features correlation in order to improve the prediction and accuracy of our data mining algorithm. We introduce a new feature correlation metric Q Y ðX i ; X j Þ and feature subset merit measure eðSÞ to quantify the relevance and the correlation among features with respect to a desired data mining task (e.g., detection of an abnormal behavior in a network service due to network attacks). Our approach takes into consideration not only the dependency among the features, but also their dependency with respect to a given data mining task. Our analysis shows that the correlation relationship among features depends on the decision task and, thus, they display different behaviors as we change the decision task. We applied our data mining approach to network security and validated it using the DARPA KDD99 benchmark data set. Our results show that, using the new decision dependent correlation metric, we can efficiently detect rare network attacks such as User to Root (U2R) and Remote to Local (R2L) attacks. The best reported detection rates for U2R and R2L on the KDD99 data sets were 13.2 percent and 8.4 percent with 0.5 percent false alarm, respectively. For U2R attacks, our approach can achieve a 92.5 percent detection rate with a false alarm of 0.7587 percent. For R2L attacks, our approach can achieve a 92.47 percent detection rate with a false alarm of 8.35 percent.

Increasingly, more people are suffering from the effects of air pollution. This study took Beijing as an example and proposed an attention-based air quality predictor (AAQP) that could better protect people from air pollution. The AAQP is a seq2seq model, and it exploits historical air quality data and weather data to predict future air quality indexes. Although existing research has promoted seq2seq for air quality prediction, there are still two problems. First, the seq2seq has a slow training speed so the original RNN in the encoder was replaced with a fully connected encoder to accelerate the training process. Position embedding was also introduced to help the fully connected encoder find the sequential relationships among source sequences. Another problem is error accumulation caused by recurrent prediction. Accordingly, the n-step recurrent prediction was proposed to solve this problem. The experimental results validated that the AAQP with n-step recurrent prediction had better performance than the related arts since the error accumulation was reduced, and the training time was significantly decreased compared with the original seq2seq attention model.

ost of the Internet's infrastructure was designed to withstand physical failures-such as broken wires or computers-rather than attacks launched by legal network users. 1-3 The Internet's rapid growth, however, coupled with its cost-effective ability to move data across geographically dispersed heterogeneous information systems, has made it a virtual breeding ground for attackers. Furthermore, the improvisation and sophistication of hackers' attack strategies and methods have overshadowed progress in security systems development. A sustained attack on the Internet could cause a catastrophic infrastructure breakdown. According to a study on the Internet's structure, its reliance on a few key nodes makes it especially vulnerable to organized attacks by hackers and terrorists. 4 According to that report, if 1 percent of the key nodes were disabled, the Internet's average performance would be reduced by a factor of two; if 4 percent were shut down, the Internet's infrastructure would become fragmented and unusable. To make networked systems reliable and robust, we need vulnerability metrics that let us monitor, analyze, and quantify network and application behavior under a range of faults and attacks. Here, we present an agentbased framework for analyzing network vulnerability in real time. The framework lets us quantify how attacks and faults impact network performance and services, discover attack points, and examine how critical network components behave during an attack or system fault. Attack analysis In most network attacks, attackers overwhelm the target system with a continuous flood of traffic designed to consume all system resources (such as CPU cycles, memory, network bandwidth, and packet buffers). These attacks degrade service and can eventually lead to a complete shutdown. 5 There are two common types of attacks: • Server attacks. There are many types of server attacks, 6 including TCP SYN, Smurf IP, ICMP flood, and Ping of Death attacks. In some attacks, the attacker makes overwhelming connection requests to a victim server with spoofed source IP addresses. Due to TCP/IP protocol stack vulnerabilities, the victim server cannot complete the connection requests and wastes all of its system resources. As a result, the server cannot service legitimate traffic, which severely impacts network performance. • Routing attacks. Distributed denial-of-service (DDoS) attacks increasingly focus on routers. Once a router is compromised, it will forward traffic according to the attackers' intent. Similar to server attacks, the attackers aim to consume all router resources, forcing the router to drop all incoming packets, thus negatively affecting network performance and behavior. Analyzing vulnerability in networks and in Internet infrastructure still is in its infancy, and there's much room for improvement. Several existing tools, which are based on modeling network specifications, fault trees, graph models, and performance models, analyze vulnerability by checking logs of system software and monitoring performance me...