Miracle-dz/UniFAR-FTUs huggingface dataset:
RETBLEED: Arbitrary Speculative Code Execution with Return Instructions

Modern operating systems rely on software defenses against hardware attacks. These defenses are, however, as good as the assumptions they make on the underlying hardware. In this paper, we invalidate some of the key assumptions behind retpoline , a widely deployed mitigation against Spectre Branch Target Injection (BTI) that converts vulnerable indirect branches to protected returns. We present R ETBLEED , a new Spectre-BTI attack that leaks arbitrary kernel memory on fully patched Intel and AMD systems. Two insights make R ETBLEED possible: ﬁrst, we show that return instructions behave like indirect branches under certain microarchitecture-dependent conditions, which we reverse engineer. Our dynamic analysis framework discovers many exploitable return instructions inside the Linux kernel, reach-able through unprivileged system calls. Second, we show how an unprivileged attacker can arbitrarily control the predicted target of such return instructions by branching into kernel memory. R ETBLEED leaks privileged memory at the rate of 219 bytes/s on Intel Coffee Lake and 3.9 kB/s on AMD Zen 2. Abstract Speculative execution attacks that exploit branch target injection (Spectre-BTI) have so far been limited to indirect branch instructions. R ETBLEED extends Spectre-BTI to return instructions. During our research into R ETBLEED , we discovered that certain AMD CPUs are also vulnerable to a new class of speculative execution attacks that we refer to as P HANTOM JMP S — incorrect branch target prediction in the absence of a corresponding branch instruction. Our investiga-tion so far shows that P HANTOM JMP S signiﬁcantly increases the attack surface of Spectre-BTI, but it is more difﬁcult to exploit under real-world conditions. This addendum brieﬂy discusses our ﬁndings and directions for future research.
A Pipeline for Tailored Sampling for Progressive Visual Analytics

Progressive Visual Analytics enables analysts to interactively work with partial results from long-running computations early on instead of forcing them to wait. For very large datasets, the ﬁrst step is to divide that input data into smaller chunks using sampling, which are then passed down the progressive analysis pipeline all the way to their progressive visualization in the end. The quality of the partial results produced by the progression heavily depends on the quality of these chunks, that is, chunks need to be representative of the dataset. Whether or not a sampling approach produces representative chunks does however depend on the particular analysis scenario. This stands in contrast to the common use of random sampling as a “one-size-ﬁts-most” approach in PVA. In this paper, we propose a sampling pipeline and its open source implementation which can be used to tailor the used sampling method for an analysis scenario at hand. This pipeline consists of three conﬁgurable steps – linearization, subdivision, and selection – and for each, we propose exemplar operators. We then demonstrate its utility by providing tailored samplings for three distinct scenarios.
Income Polarization in the USA (1983-2016): what happened to the middle class?

The rise in inequality in the US over the last few decades has been well documented. However, the effects of inequality on polarization and middle class in particular are less well studied and understood. We employ Relative Distribution tools (Handcock and Morris, 1998) on the well known PSID longitudinal dataset in order to provide a detailed analysis of the US income distribution during the period 1983 to 2015. The main result shows a hollowing out of the mid-range deciles with a corresponding fattening of the highest ones. Thus the analysis confirms the hollowing out of the middle class in the US over the last few decades---starting with the age of Reaganomics. Analyzing further the “pure distribution” effect ( i.e. , depurated by the growth one), we find that this typical polarization profile emerged over time but especially in the 1990s and the 21st century. The typical hollowing out of the central deciles of the US income distribution has been indeed accompanied by “fattening” of both tails of income distribution with greater weight going increasingly to the bottom deciles. There are even some signs of acceleration of this trend recently. We also find some preliminary evidence for intersectionality , i.e. race, class and gender working together in a vicious cycle.Policies to counteract these tendencies must be anti-polarization policies along with those of relatively more egalitarian growth. However, the current policies including tax policies are headed in exactly the opposite direction.
A New Hybrid Stock Index Trend Price Prediction Model

Predicting stock price tendencies is considered to be one of the most challenging tasks in stock market mining. A number of factors inﬂuence stock market performance, such as political events, general economic conditions and trader expectations. Market traders rely heavily on di ↵ erent types of intelligent systems to help them to make decisions and to deal with stocks and futures [1]. However, up to date these available systems have limited ability and capability. However, to date these available systems have had limited ability and capability. More-over, ﬁnancial experts also ﬁnd it di � cult to make accurate predictions, due to the uncertainties involved and the characteristics of market trends. Generally, among market experts there is no consensus on the e ↵ ectiveness of predicting a ﬁnancial time series.
Cluster-guided Image Synthesis with Unconditional Models - Supplementary material

As shown in [4], maximizing the likelihood of the data (equivalent to minimizing DKL(pdata|pmodel )) penalizes assigning low probability to data samples, while the alternative DKL(pmodel|pdata ) penalizes implausible samples, which can lead to modedropping in GANs [1]. This is detrimental when modeling the conditional distribution of latent codes (Fig.1b). On the other hand, [4] show that under mild assumptions IMLE maximizes the likelihood of the data. To test our model choice, we replace IMLE with GAN and get a reduced facial pose accuracy of 87%. We also test VAE as an alternative likelihood-based model, which also drops accuracy to 90%. Contrary to IMLE, VAE and GAN focus on a subset of codes z and fail to maintain attribute consistency.
Climate change and food system activities: a review of emission trends, climate impacts and the effects of dietary change

The IFAD Research Series has been initiated by the Strategy and Knowledge Department in order to bring together cutting-edge thinking and research on smallholder agriculture, rural development and related themes. As a global organization with an exclusive mandate to promote rural smallholder development, IFAD seeks to present diverse viewpoints from across the development arena in order to stimulate knowledge exchange, innovation, and commitment to investing in rural people. Abstract Food system activities are inextricably linked with climate and weather, environmental resources and human health. This article reviews the contribution of food system activities to climate change, the positive feedback on food systems and the effects of dietary change on food system outcomes. It combines a systematic literature review with analyses of publicly available international data. The article shows that whereas emissions from food production continue to increase in most regions, emissions from land use change have been decreasing. Despite these decreasing trends, emissions from land use change are huge and in some regions are greater than emissions from food production. Climate change also affects food system activities and there is strong scientific consensus that it negatively affects food production, especially in Africa and Asia. However, from the available scientific evidence, the impacts of climate change on post-production activities are unclear. The article also shows that dietary change as a transformation strategy has large potential for reduction of greenhouse gas emissions but, regionally, is associated with substantial environmental trade-offs. Despite its potential, the costs and feasibility associated with dietary change are not well understood and require further research. Strategies to reduce emissions should focus on further reduction in land use change or stopping land use change altogether, because the current rate of reduction is inadequate to achieve a targeted reduction in greenhouse gas emissions. Strategies must also address meat consumption in those world regions and social systems where it is excessive, to usher in dietary changes away from animal products and towards consumption of plant products instead. (PSC); and replacing all animal-sourced foods with fruits and vegetables (VGN). The study spanned 2010 and 2030 and used IPCC’s Shared Socio-economic Pathways (SSPs; O’Neill et al., 2014). These pathways assume different future socio-economic developments but no climate policies. For our analysis, we reaggregated the results from Springmann et al. (2018b) for the five IFAD regions and computed their relative changes. This approach facilitated the identification of suitable dietary change strategies for each IFAD region.
New Intervention Meditation Combining Heritage and Psychology

: In this paper, a new Intervention Meditation approach is outlined by the current author. To overcome lack of clarity in defining meditation, a new universal Operational definition of meditation is proposed with elaborative explanation. The new meditation approach has multitude of deeper concepts from Bhagavad Gita and modern psychology and the expert / Guru of the proposed method must be a qualified and experienced before getting trained on this approach specifically. This paper elevates meditation from a casual approach level to a separate systematic branch of psychology level
LARGE: Latent-Based Regression through GAN Semantics - Supplementary Materials

We provide the URL to the project’s Github repository containing our source code: https://github.com/ YotamNitzan/LARGE. Additionally, we have made an effort throughout the paper towards making the results reliable and reproducible. Specifically, as noted in Section 4, whenever feasible we repeated experiments a thousand times and report mean and standard deviation in Figures 3, 4, 1 to 3, 6 and 19. Additionally, we report detailed information on subtle aspects of the model and data in Section E.
Evaluation of Monocular and Stereo Depth Data for Geometry-Assisted Learning of 3D Pose

—The estimation of depth cues from a single image has recently emerged as an appealing alternative to depth estimation from stereo image pairs. The easy availability of these dense depth cues naturally triggers research questions, how depth images can be used to infer geometric object and view attributes. Furthermore, the question arises how the quality of the estimated depth data compares between different sensing modalities, especially given the fact that monocular methods rely on a learned correlation between local appearance and depth, without the notion of a metric scale. Further motivated by the ease of synthetic data generation, we propose depth computation on synthetic images as a training step for 3D pose estimation of rigid objects, applying models on real images and thus also demonstrating a reduced synth-to-real gap. To characterize depth data qualities, we present a comparative evaluation involving two monocular and one stereo depth estimation schemes. We furthermore propose a novel and simple two-step depth-ground-truth generation workﬂow for a quantitative comparison. The presented data generation, evaluation and exemplary pose estimation pipeline are generic and applicable to more complex geometries.
GENETIC VARIABILITY AND REPRODUCTIVE CHARACTERISTICS OF ZEBRAFISH ( Cyprinidae Danio rerio ) STOCKS Variabilidad genética y características reproductivas de poblaciones de pez cebra ( Cyprinidae Danio rerio )

Specimens of cultured zebrafish acquired from different fish farms in Brazil may show genetic variability and alteration in allele frequency due to genetic drift and selective pressure in a captive environment, resulting in the differentiation of productive and reproductive characteristics. The aim of this study was to evaluate the genetic variability and reproductive characteristics of 180 zebrafish specimens from six Brazilian fish farms. A deviation from the Hardy-Weinberg equilibrium was observed in all evaluated stocks. Differentiation among stocks was observed in the amount of genetic variability with respect to observed heterozygosity and the inbreeding coefficient (F IS ). Genetic distance between stocks was determined through the F st index, and the formation of four distinct groups was observed by plotting the dendrogram based on Nei’s genetic distance. Differences were observed among reproductive parameters, such as the average number of eggs per female and hatchability. This second parameter proved to be related to the level of inbreeding of the population, whereas this effect was not observed for spawning frequency. We conclude that zebrafish stocks from the 6 different Brazilian fish farms present significant genetic and phenotypic variability. The genetic structure affects fecundity and should be considered when carrying out work where reproductive rates are evaluated.
Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things

The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes.
DnaC F231S W233l 12302015revised

The DnaB-DnaC complex binds to the unwound DNA within the Escherichia coli replication origin in the helicase loading process, but the biochemical events that lead to its stable binding are uncertain. This study characterizes the function of specific C-terminal residues of DnaC. Genetic and biochemical characterization of proteins bearing F231S and W233L substitutions of DnaC reveals that their activity is thermolabile. As the mutants remain able to form a complex with DnaB at 30C and 37C, their thermolability is not explained by an impaired interaction with DnaB. Photocrosslinking experiments and biosensor analysis show an altered affinity of these mutants compared with wild type DnaC for singlestranded DNA, suggesting that the substitutions affect DNA binding. Despite this difference, their activity in DNA binding is not thermolabile. The substitutions also drastically reduce the affinity of DnaC for ATP as measured by the binding of a fluorescent ATP analogue (MANT-ATP) and by UV-crosslinking of radiolabeled ATP. Experiments show that an elevated temperature substantially inhibits both mutants in their ability to load the DnaB-DnaC complex at a DnaA box. As a decreased ATP concentration exacerbates their thermolabile behavior, we suggest that the F231S and W233L substitutions are thermolabile in ATP binding, which correlates with defective helicase loading at an elevated temperature. INTRODUCTION DNA replication in E. coli starts at the chromosomal replication origin (oriC) through a series of discrete biochemical events. First, DnaA bound to ATP recognizes specific DNA sequences within oriC to form a DnaA oligomer, which leads to the unwinding of an AT-rich region near the left border (reviewed in (1)). At the step of helicase loading, DnaA then directs the binding of the DnaB-DnaC complex onto each unwound DNA strand. In vitro, the DnaB-DnaC complex not bound to DNA contains as many as six DnaC molecules per DnaB molecule, a hexamer of six identical subunits that in the absence of DnaC is a toroid (2-5). It acts as the replicative DNA helicase to unwind the parental duplex DNA so that each DNA strand can be copied by the cellular replicase, DNA polymerase III holoenzyme. Studies show that DnaB by itself is able to interact with single-stranded DNA (ssDNA; (6,7)). Other work indicates that one of two DNA strands of an artificial replication fork passes through the interior of the toroid (8,9). Despite the ability of DnaB to interact with ssDNA, the conundrum is that loading of the helicase at oriC requires that it is complexed to DnaC. Considering that DnaC also interacts with ssDNA (see below), it is unclear if this activity of DnaC participates during the events that lead to the stable binding of the DnaB-DnaC complex at oriC. http://www.jbc. org/cgi/doi/10.1074/jbc.M115.708586 The latest version is at JBC Papers in Press. Published on January 4, 2016 as Manuscript M115.708586 Copyright 2016 by The American Society for Biochemistry and Molecular Biology, Inc. by gest on M arch 4, 2020 hp://w w w . jb.org/ D ow nladed from
COPD_A_266612 2071..2079

1Department of Stomatology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Medical Research Center, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA; 4Department of Ophthalmology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China Purpose: To evaluate clinical periodontal status and microbiologic pathogens in patients with chronic obstructive pulmonary disease (COPD) and periodontitis. Patients and Methods: We conducted a case–control study of 60 periodontitis patients with COPD (case group) and 60 periodontitis patients with normal pulmonary function (control group). Their periodontal status and respiratory function were clinically examined. Real-time polymerase chain reaction assays were used to measure five dental pathogens and four respiratory pathogens in subgingival dental plaque. Spearman’s rank correlation coefficients (r) were calculated to assess correlations of pathogens. Principal component analysis (PCA) was employed to assess the similarity of bacterial diversity between the two groups. Logistic regression was performed to examine the associations of periodontal variables and pathogens with COPD risk. Results: COPD patients had fewer remaining teeth, higher plaque index (PLI), and more severe site percentages of clinical attachment level (CAL) than the controls. Although COPD patients tended to have relatively higher ranked means of Porphyromonas gingivalis, Tannerella forsythensis, Treponema denticola, and Haemophilus influenza than control participants, the differences were not significant. Some periodontal pathogens and respiratory pathogens were positively correlated with each other (r =0.29 to 0.47, all P < 0.05). The PCA graph showed that the distributions of pathogens were more dispersed but less discriminated in the COPD group than those in the control group. PLI (P = 0.045) and CAL ≥ 5mm site percentages (P = 0.01) were significantly associated with an increased risk of COPD, while pathogens were not associated with COPD. Conclusion: Our results from this study do not indicate periodontal pathogens as potential predictors of COPD risk, despite significantly poor periodontal status associated with COPD.
Attribute-Based Access To Multimedia In Cloud-Assisted Online Content Sharing

— Cloud computing, is the significant computing paradigm which allows the users to store their data into the cloud. This paper presents an Attribute-Based access to the media in the cloud where it uses a multiple message cipher-text policy Attribute-Based Encryption (CP-ABE) technique to create an access control structure. By using the advance algorithms for implementing the access policy; the user attributes are used so as to generate a public key. Thus the sharable data is encrypted and a secret key consisting of user attributes to decrypt the data and is used as an access policy in order to restrict the access of the user. This requires flexible and accessible cryptographic key management to support difficult access policies. The policy that is to be used for assigning a key to each user is based on a novel Multi-message Ciphertext Policy Attribute-Based Encryption (MCP-ABE) technique. The scheme allows a content provider to specify an access policy and encrypt multiple messages within one Cipher ext such that only the users whose attributes satisfy the access policy can decrypt the Ciphertext. Moreover, the technique shows how to support resource-limited mobile devices by offloading computational intensive operations to cloud servers while without compromising data privacy.