Miracle-dz/UniFAR-FTUs huggingface dataset:
Future prediction of regional city based on causal inference using time-series data

Regional cities in Japan have a lot of social issues. Various measures are being considered to solve these social issues, but it is difficult to ascertain and implement practical and effective measures to address them. In this study, we proposed a method for selecting indicators that have a causal relation to solve the social issues based on a causal inference. If there was a causal relation between two sets of time-series data, the slope of the approximation line of the time-shifted correlation coefficients at the base time returned a negative value. The causal inference was verified by using samples of time-series data and we constructed a network of the causal indicators based on the causal inference. In addition, we achieved future predictions via the vector autoregressive model using the network of causal indicators. The model was verified using the actual time-series data of the 87 regional cities. As a result, it was possible to simulate future predictions by introducing practical and effective measure that originated from the social issue.
Airborne Rabies Virus Isolation

Airborne rabies virus was isolated from Frio Cave, Texas, using a mechanical air sampler. Several types of samplers have been used, but only an electrostatic precipitation device has collected measurable quantities of virus. Prior to this, the only known reported isolations of airborne rabies virus had occurred when susceptible animals were confined in bat caves for long periods of time. The use of a mechanical sampler can facilitate quantitation of virus in caves.
THE ROLE OF PURIFIED CD8+ T CELLS IN GRAFT‐VERSUS‐LEUKEMIA ACTIVITY AND ENGRAFTMENT AFTER ALLOGENEIC BONE MARROW TRANSPLANTATION

The ability of highly purified CD8+ T cells to mediate GVL activity and facilitate engraftment of allogeneic bone marrow cells was studied in the C57BIV KaBALB/c mouse strain combination. Splenic CD8+ T cells were enriched by depletion of CD44 T cells by “panning” or purified by positive selection by cell sorting. Although C57BL/Ka bone marrow cells reconstitute lethally irradiated BALB/c mice without severe GVHD, the addition of at least 1. 0χ106 donor spleen cells induced uniform acute lethal GVHD. Equivalent doses of spleen cells depleted of CD4+ T cells failed to induce lethal GVHD. Allogeneic bone marrow cells alone failed to mediate antitumor activity against the BCL1 B cell leukemia/lymphoma as compared with syngeneic bone marrow and spleen cell injections. Despite the inability to induce severe GVHD, CD4+ T cell-depleted allogeneic spleen cells prevented the progressive growth of the BCLj tumor, and eliminated BCL1 idiotype-positive tumor cells in the blood. In order to determine whether CDS+ T cells can prevent tumor growth in the absence of other spleen cell subsets, such as NK cells, that are present in the CD4- population, highly purified CD8+ T cells were obtained by positive selection using flow cytometry. The latter cells prevented the progressive growth of the tumor, and markedly reduced the level of tumor cells in the blood. Sorted CD8+ T cells facilitated the engraftment of allogeneic marrow cells in sublethally irradiated hosts. Thus, addition of highly purified CD8+ T cells to marrow cells provides GVL activity and facilitates engraftment without inducing severe GVHD in most recipients.
The Neurologic Effects of Thiopental Therapy Following Experimental Cardiac Arrest in Cats

To define the utility of high-dose barbiturate therapy following an episode of complete global cerebral ischemia, we investigated the effects of 60 mg/kg of thiopental given to cats five minutes after resuscitation from 12, 14, or 16 min of electrically induced ventricular fibrillation (VF). All aspects of the arrest, resuscitation, with post-arrest care were carefully controlled, with the EEG becoming isoelectric 20–25 s after the onset of VF, a 89–91 per cent rate of successful resuscitation, with an overall mean resuscitation time of 2.5 ± 0.2 (SEM) min. For any given duration of VF, there were no differences (control vs thiopental) in any pre- or post-arrest parameters (blood pressure, blood gases, electrolytes, etc.) A total of 68 resuscitated cats were entered into various treatment and control groups, and all but one group received 20–24 h of post-resuscitation paralysis, mechanical ventilation, and ICU support before being extubated. Cats received an additional six days of aggressive nursing care, and daily examinations were performed with the assignment of a neurologic deficit score (NDS) between 0 (normal) and 100 (brain dead). Autopsies were performed to determine the cause of death in animals which died before the end of the seven-day observation period.The early post-arrest period was marked by the occurrence of repetitive, rhythmic bursts of high-frequency electroencephalographic (EEG) activity (? seizures) in 38 per cent of control animals (16/42, all arrest times combined). Ten of these animals died as a result of severe neurologic injuries. By contrast, only 12 per cent of treated cats (3/26) developed similar EEG patterns (P < 0.05) and there were no neurologic deaths in the thiopental groups. The differences in the incidence of neurologic deaths (control vs. thiopental) was significant (P < 0.02). The change in overall mortality did not quite reach significance (36 per cent vs. 21 per cent), and treatment had no effect on the incidence of deaths due to cardiovascular causes (e.g., myocardial infarctions). In spite of the effects on mortality, treatment had no effect on the neurologic function of survivors (assessed by NDS). These findings suggest that thiopental improved survival rates by suppressing an unusual post-arrest EEG pattern (? anticonvulsant effect), but had no additional cerebral protective effects.
Navigating in Webs of Trust: Finding Short Trust Chains in Unstructured Networks without Global Knowledge

Authentication of participants is fundamental for secure communication systems. Many traditional systems rely on central components (like certification authorities). These have to be powerful and well-protected, making them expensive, and require trust in a central authority. Conventional decentralized solutions based on local trust between participants only (like PGP) are cheaper but either must be supported by central components, or they are prone to e.g. Sybil attacks. We present a decentralized solution without any trusted central components for finding paths of trust between arbitrary participants in an unstructured network: In an initialization phase a hierarchical overlay structure is constructed, providing each participant with certificate chains to predefined reference nodes. These certificate chains can be used later to find short trust chains between nodes. Monte-Carlo simulations clearly demonstrate the feasibility and efficiency of our approach for social networks. Further features like merging of networks, limited trust relationships between participants and revocation of trust can be integrated easily.
Increasing the Potency of an Alhydrogel-Formulated Anthrax Vaccine by Minimizing Antigen-Adjuvant Interactions

ABSTRACT Aluminum salts are the most widely used vaccine adjuvants, and phosphate is known to modulate antigen-adjuvant interactions. Here we report an unexpected role for phosphate buffer in an anthrax vaccine (SparVax) containing recombinant protective antigen (rPA) and aluminum oxyhydroxide (AlOH) adjuvant (Alhydrogel). Phosphate ions bind to AlOH to produce an aluminum phosphate surface with a reduced rPA adsorption coefficient and binding capacity. However, these effects continued to increase as the free phosphate concentration increased, and the binding of rPA changed from endothermic to exothermic. Crucially, phosphate restored the thermostability of bound rPA so that it resembled the soluble form, even though it remained tightly bound to the surface. Batches of vaccine with either 0.25 mM (subsaturated) or 4 mM (saturated) phosphate were tested in a disease model at batch release, which showed that the latter was significantly more potent. Both formulations retained their potency for 3 years. The strongest aluminum adjuvant effects are thus likely to be via weakly attached or easily released native-state antigen proteins.
On-line aggregation of POIs from Google and Facebook

In the last decade, social media have become widely exploited sources of information concerning every aspect of life. The reason is that they permit to create and widely share information, even in mobility by exploiting mobile apps. Information available on social media are often related to public places (restaurants, museums, etc.), and users often look for interesting public places. Currently, various social media own and publish huge and independently-built corpora of geo-located data about public places, which are not linked each other. In particular, the main players are Google and Facebook. Users searching for a public place of interest (POI) might wish to get all available information from social media. Therefore, they need an on-line aggregation engine for public places that returns an aggregated view of a place, retrieving data concerning the same place from various sources. The on-line approach is suggested by continuous variations in data within the on-line corpora, that demands for a technique that cannot rely on off-line processing. In this paper, we address the problem by devising a novel technique to aggregate geo-located data about public places; the application context is to associate data provided by Google Places with Facebook pages concerning public places; the Klondike software tool implements this technique. Tests were conducted on a data set containing about 300 public places in Manchester (UK).
Recent further research regarding the theory of inertial centers ( unpublished )

Our geodesic equation is given by (see (72) of [1]) 0 = U a ∇ a U b To find solutions, we start by expressing this equation in terms of Minkowski coordinates (T, X, Y, Z), where the spatial coordinate origin of this system is located at the position of our inertial center (i.e.
A 40 nm 222 mW H.264 Full-HD Decoding, 25 Power Domains, 14-Core Application Processor With x512b Stacked DRAM

In this paper we introduce a 14-core application processor for multimedia mobile applications, implemented in 40 nm, with a 222 mW H.264 full high-definition (full-HD) video engine, a 124 mW 40 M-polygons/s 3D/2D graphics engine, and a video/audio multiprocessor for various Codecs and image processing. The application processor has 25 power domains to achieve coarse-grain power gating for adjusting to the required performance of wide range of multimedia applications. The simple on-chip power switch circuits perform less than 1 μs switching while reducing rush current. Furthermore, the Stacked Chip SoC (SCS) technology enables rewiring to the DRAM chip during assembly/packaging phase using a wire with 10 μm minimum pitch on Re-Distribution Layer (RDL) using electroplating. The peak memory bandwidth is 10.6 GB/s with an x512b SCS-DRAM interface, and the power consumption of this interface is 3.9 mW at 2.4 GB/s workload.
Glutamate receptor phosphorylation and trafficking in pain plasticity in spinal cord dorsal horn

Glutamate is the major excitatory neurotransmitter in the central nervous system. Considerable evidence suggests that both ionotropic and metabotropic glutamate receptors are involved in pain hypersensitivity. However, glutamate receptor‐based therapies are limited by side‐effects because the activities of glutamate receptors are essential for many important physiological functions. Here, we review recent key findings in molecular and cellular mechanisms of glutamate receptor regulation and their roles in triggering and sustaining pain hypersensitivity. Targeting these molecular mechanisms could form the basis for new therapeutic strategies for the treatment of chronic pain.
A design and implementation wind farm real-time simulator with various types of wind turbine considering wake effect

As the capacity of wind farm increases, the power of wind farm is increasingly affected by the grid. Therefore, the importance of wind farm control system is increasing. The wind farm control system must be validated with sufficient testing before actual operation. In order to demonstrate the control system, there are many practical constraints such as cost and stability. Therefore, the control system can be verified through a simulator simulating real wind farm in real time. In order to effectively replicate wind farm, various types of wind turbines must be located in the wind farm. It should also be able to deliver the wind with wake effect to the wind turbine in real time. In this paper, we design and implement a real-time wind farm simulator using common data model of wind turbine, Ainslie 's Eddy Viscosity wake model, and parallel processing technology.
A time-predictable branch predictor

Long pipelines need good branch predictors to keep the pipeline running. Current branch predictors are optimized for the average case, which might not be a good fit for real-time systems and worst-case execution time analysis. This paper presents a time-predictable branch predictor co-designed with the associated worst-case execution time analysis. The branch predictor uses a fully-associative cache to track branch outcomes and destination addresses. The fully-associative cache avoids any false sharing of entries between branches. Therefore, we can analyze program scopes that contain a number of branches lower than or equal to the number of branches in the prediction table. Experimental results show that the worst-case execution time bounds of programs using the proposed predictor are lower than using static branch predictors at a moderate hardware cost.
Subspace clustering for situation assessment in aquatic drones

We propose a novel methodology based on subspace clustering for detecting, modeling and interpreting aquatic drone states in the context of autonomous water monitoring. It enables both more informative and focused analysis of the large amounts of data collected by the drone, and enhanced situation awareness, which can be exploited by operators and drones to improve decision making and autonomy. The approach is completely data-driven and unsupervised. It takes unlabeled sensor traces from several water monitoring missions and returns both a set of sparse drone state models and a clustering of data samples according to these models. We tested the methodology on a real dataset containing data of six different missions, two rivers and four lakes in different countries, for about 5.5 hours of navigation. Results show that the methodology is able to recognize known states "in/out of the water", "upstream/downstream navigation" and "manual/autonomous drive", and to discover meaningful unknown states from their data-based properties, enabling novelty detection.
Mind Your Own Business: A Longitudinal Study of Threats and Vulnerabilities in Enterprises

—Enterprises own a signiﬁcant fraction of the hosts connected to the Internet and possess valuable assets, such as ﬁnancial data and intellectual property, which may be targeted by attackers. They suffer attacks that exploit unpatched hosts and install malware, resulting in breaches that may cost millions in damages. Despite the scale of this phenomenon, the threat and vulnerability landscape of enterprises remains under-studied. The security posture of enterprises remains unclear, and it’s unknown whether enterprises are indeed more secure than consumer hosts. To address these questions, we perform the largest and longest enterprise security study up to date. Our data covers nearly 3 years and is collected from 28K enterprises, belonging to 67 industries, which own 82M hosts and 73M public-facing servers. Our measurements comprise of two parts: an analysis of the threat landscape and an analysis of the enterprise vulnerability patching behavior. The threat landscape analysis ﬁrst classiﬁes low reputation ﬁles observed in enterprise hosts into families. Then, it measures, among others, that 91%–97% of the enterprises, 13%–41% of the enterprise hosts, encountered at least one malware or PUP ﬁle over the length of our study; that enterprises encounter malware much more often than PUP; and that some industries like banks and consumer ﬁnances are doing notoriously better, achieving signiﬁcantly lower malware and PUP encounter rates than the most-affected industries. The vulnerability analysis examines the patching of 12 client-side and 112 server-side applications in enterprise hosts and servers. It measures, among others, that it takes over 6 months on average to patch 90% of the population across all vulnerabilities in the 12 client-side applications; that enterprise computers are faster to patch vulnerabilities compared to consumer hosts; and that the patching of server applications is much worse than the patching of client-side applications.
The Sequence Alteration Associated with a Mutational Hotspot in p53 Protects Cells From Lysis by Cytotoxic T Lymphocytes Specific for a Flanking Peptide Epitope

A high proportion of tumors arise due to mutation of the p53 tumor suppressor protein. A p53 hotspot mutation at amino acid position 273 from R to H, flanking a peptide epitope that spans residues 264–272, renders cells resistant to killing by human histocompatibility leukocyte antigen (HLA)-A*0201–restricted cytotoxic T lymphocytes (CTLs) specific for this epitope. Acquisition of the R to H mutation at residue 273 of the human p53 protein promotes tumor growth in vivo by selective escape from recognition by p53.264–272 peptide-specific CTLs. Synthetic 27-mer p53 polypeptides covering the antigenic nonamer region 264–272 of p53 were used as proteasome substrates to investigate whether the R to H mutation at the P1′ position of the COOH terminus of the epitope affects proteasome-mediated processing of the protein. Analysis of the generated products by tandem mass spectrometry and the kinetics of polypeptide processing in conjunction with CTL assays demonstrate that the R to H mutation alters proteasomal processing of the p53 protein by inhibiting proteolytic cleavage between residues 272 and 273. This prevents the release of the natural CTL epitope that spans flanking residues 264–272 as well as a putative precursor peptide. These results demonstrate that mutation of p53 not only leads to malignant transformation but may also, in some instances, affect immune surveillance and should be considered in the design of cancer vaccines.
Total Recall: Persistence of Passwords in Android

A good security practice for handling sensitive data, such as passwords, is to overwrite the data buffers with zeros once the data is no longer in use. This protects against attackers who gain a snapshot of a device’s physical memory, whether by inperson physical attacks, or by remote attacks like Meltdown and Spectre. This paper looks at unnecessary password retention in Android phones by popular apps, secure password management apps, and even the lockscreen system process. We have performed a comprehensive analysis of the Android framework and a variety of apps, and discovered that passwords can survive in a variety of locations, including UI widgets where users enter their passwords, apps that retain passwords rather than exchange them for tokens, old copies not yet reused by garbage collectors, and buffers in keyboard apps. We have developed solutions that successfully fix these problems with modest code changes.
Out-of-context fine-grained multi-word entity classification: exploring token, character n-gram and NN-based models for multilingual entity classification

In this paper, we present a number of experiments on the construction of fine-grained and out-of-context multi-word entity classification models. These models exploit a large BabelNet-derived multilingual Named Entity corpus of 49 languages from 7 different scripts, which is also presented in this work. In particular, we compare SVM-based character and token n-gram models with neural network-based ones and also explore language-specific variants against multilingual models. The various models have been evaluated on additional external Named Entity resources to gain further insight into the quality and re-usability of the trained models. The language-independent character n-gram SVM-based model outperforms the corresponding token n-gram SVM-based model for a large majority of tested languages and obtained a 95.7% average precision. When applied to a number of external resources, we did see a slight drop in performance, but still achieved an average precision of 84.7% in all experiments, demonstrating the applicability of the proposed model in a range of contexts. Finally, the experiments applying a neural network model show comparable results for language-specific and language-independent approaches.
Analysis of architectural variants for auditable blockchain-based private data sharing

Many applications by design depend on costly trusted third-party auditors. One such example is the industrial application case of federated multi-disciplinary optimization (MDO), in which different organizations contribute to a complex engineering design effort. Although blockchain and distributed ledger technology (DLT) has strong potential in reducing the dependence on such intermediaries, the architectural complexity involved in designing a solution is daunting. In this paper, we analyze the architectural variants for decentralized private data sharing while guaranteeing auditability and non-repudiation of data access operations, as well availability of the shared data. The architectural variants analyzed focus on attaining: (i) confidential data exchange, (ii) governing access to the shared data, (iii) providing data access auditability, and (iv) data validation or conflict resolution. We systematically enumerate architectural decisions at the levels of: storage, policy-based file access control, data encryption methods, and auditability mechanisms for private data. The main contribution of this work is a comprehensive overview of architectural variants for decentralized control of private encrypted data, and the involved trade-offs in terms of performance, storage overhead, auditable trust and security. These findings are validated in the context of the aforementioned industry case that involves federated multi-disciplinary optimization (MDO).
Real-Time Detection of Impulsive Sounds for Audio Surveillance Systems

The monitoring of dangerous audio events is very important in surveillance systems. One of the most significant phase in audio surveillance is the detection of impulsive sounds (IS). It is considered as a preprocessing stage prior to the recognition phase. We propose in this paper an indoor audio monitoring software to detect IS in real-time. It is composed of three main stages: (i) audio acquisition, (ii) preprocessing module and (iii) sound detector. We have used MEMS microphone to acquire the audio data. The preprocessing stage aims at tuning the microphone sensitivity. It is used to mask the non-desired frequency components of the environment by adding white noise. The detection of IS is conducted using a thresholding scheme based on normalized form of power sequences. The proposed prototype is running under Windows 7 on an ordinary laptop. The results we have obtained are very promising.