Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Millimeter-Wave Joint Radar Communications: A signal processing perspective

Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint radar communications (JRC) model has advantages of low cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mmwave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical to the implementation of mm-wave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade off between communications and radar functionalities. Novel multiple-input, multiple-output (MIMO) signal processing techniques are required because mm-wave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mm-wave JRC systems with an emphasis on waveform design.
Net-proton number fluctuations in the presence of the QCD critical point

Event-by-event fluctuations of the net-proton number studied in heavy-ion collisions provide an important means in the search for the conjectured critical end point (CP) in the QCD phase diagram. We propose a phenomenological model in which the fluctuations of the chiral critical mode couple to protons and anti-protons. This allows us to study the behavior of the net-proton number fluctuations in the presence of the CP. Calculating the net-proton number cumulants, $C_n$ with n=1,2,3,4, along the phenomenological freeze-out line we show that the ratio of variance and mean $C_2/C_1$, as well as kurtosis $C_4/C_2$ resemble qualitative properties observed in data in heavy-ion collisions as a function of beam energy obtained by the STAR Collaboration at RHIC. In particular, the non-monotonic structure of the kurtosis and smooth change of the $C_2/C_1$ ratio with beam energy could be due to the CP located near the freeze-out line. The skewness, however, exhibits properties that are in contrast to the criticality expected due to the CP. The dependence of our results on the model parameters and the proximity of the chemical freeze-out to the critical point are also discussed.
Bug Searching in Smart Contract

With the frantic development of smart contracts on the Ethereum platform, its market value has also climbed. In 2016, people were shocked by the loss of nearly $50 million in cryptocurrencies from the DAO reentrancy attack. Due to the tremendous amount of money flowing in smart contracts, its security has attracted much attention of researchers. In this paper, we investigated several common smart contract vulnerabilities and analyzed their possible scenarios and how they may be exploited. Furthermore, we survey the smart contract vulnerability detection tools for the Ethereum platform in recent years. We found that these tools have similar prototypes in software vulnerability detection technology. Moreover, for the features of public distribution systems such as Ethereum, we present the new challenges that these software vulnerability detection technologies face.
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM

Weight quantization is one of the most important techniques of Deep Neural Networks (DNNs) model compression method. A recent work using systematic framework of DNN weight quantization with the advanced optimization algorithm ADMM (Alternating Direction Methods of Multipliers) achieves one of state-of-art results in weight quantization. In this work, we first extend such ADMM-based framework to guarantee solution feasibility and we have further developed a multi-step, progressive DNN weight quantization framework, with dual benefits of (i) achieving further weight quantization thanks to the special property of ADMM regularization, and (ii) reducing the search space within each step. Extensive experimental results demonstrate the superior performance compared with prior work. Some highlights: we derive the first lossless and fully binarized (for all layers) LeNet-5 for MNIST; And we derive the first fully binarized (for all layers) VGG-16 for CIFAR-10 and ResNet for ImageNet with reasonable accuracy loss.
A Review on Dyadic Conversation Visualizations - Purposes, Data, Lens of Analysis

Many professional services are provided through text and voice systems, from voice calls over the internet to messaging and emails. There is a growing need for both individuals and organizations to understand these online conversations better and find actionable insights. One method that allows the user to explore insights is to build intuitive and rich visualizations that illustrate the content of the conversation. In this paper, we present a systematic survey of the various methods of visualizing a conversation and research papers involving interactive visualizations and human participants. Findings from the survey show that there have been attempts to visualize most, if not all, of the types of conversation that are taking place digitally, from speech to messages and emails. Through this survey, we make two contributions. One, we summarize the current practices in the domain of visualizing dyadic conversations. Two, we provide suggestions for future dialogue visualization research.
On-line estimation of inertial parameters using a recursive total least-squares approach

The estimation of the ten inertial parameters of rigid loads, which are attached to manipulators, may benefit several robotics applications, e.g.: force control, object recognition, and pose estimation. These applications require sufficiently accurate, robust, and fast estimation of the inertial parameters. Existing approaches, however, do not allow for robust on-line estimation, since they use standard batch least-squares techniques, which ignore noise in the data matrix. The proposed approach, however, estimates the inertial parameters on-line and very fast (approx. 1.5s), while explicitly considering noise in the data matrix by a total least-squares approach. Apart from estimation equations and estimation approaches, the design of estimation trajectories is addressed in this paper. The performance of the proposed estimation approach is compared with the recursive ordinary least-squares (RLS) and the recursive instrumental variables (RIV) method. Experimental results clearly recommend the proposed recursive total least-squares approach (RTLS).
Immersive Navigation in Visualization Spaces through Swipe Gestures and Optimal Attribute Selection

Exploratory data analysis is an essential step in discovering patterns and relationships in data. However, the exploration may start without a clear conception about what attributes to pick or what visualizations to choose in order to develop an understanding of the data. In this work we aim to support the exploration process by automatically choosing attributes according to an information-theoretic measure and by providing a simple means of navigation through the space of visualizations. The system suggests data attributes to be visualized and the visualization’s type and appearance. The user intuitively modifies these suggestions by performing swiping gestures on a tablet device. Attribute suggestions are based on the mutual information between multiple random variables (MMI). The results of a preliminary user study (N = 12 participants) show the applicability of MMI for guided exploratory data analysis and confirm the system’s general usability (SUS score: 74).
An essential domain of the c-myc protein interacts with a nuclear factor that is also required for E1A-mediated transformation

Cell transformation by nuclear oncogenes such as c-myc presumably involves the transcriptional activation of a set of target genes that participate in the control of cell division. The function of a small evolutionarily conserved domain of the c-myc gene encompassing amino acids 129 to 145 was analyzed to explore the relationship between cell transformation and transcriptional activation. Deletion of this domain inactivated the c-myc oncogene for cell transformation while retaining the ability to activate transcription of either myc consensus binding sites or a GAL4-dependent promoter when the c-myc N-terminus was fused to the GAL4 DNA-binding domain. Point mutations that altered a conserved tryptophan (amino acid 136) within this domain had similar effects. Expression of the wt c-Myc N terminus (amino acids 1 to 262) as a GAL4 fusion was a dominant inhibitor of cell transformation by the c-myc oncogene, and this same domain also inhibited transformation by the adenovirus E1A gene. Surprisingly, deletion of amino acids 129 to 145 eliminated the dominant negative activity of GAL4-Myc on both c-myc and E1A transformation. Expression of the GAL4-Myc protein in Cos cells led to the formation of a specific complex between the Myc N terminus and a nuclear factor, and this complex was absent with the dl129-145 mutant. These results suggest that an essential domain of the c-Myc protein interacts with a specific nuclear factor that is also required for E1A transformation.
Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks

Antenna selection in Multiple-Input Multiple-Output (MIMO) systems has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity. Recently, deep learning based methods have achieved promising performance in many application fields. This paper proposed a deep learning (DL) based antenna selection technique. First, we generated the label of training antenna systems by maximizing the channel capacity. Then, we adopted the deep convolutional neural network (CNN) on the channel matrices to explicitly exploit the massive latent cues of attenuation coefficients. Finally, we used the adopted CNN to assign the class label and then select the optimal antenna subset. Experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based antenna selection.
Dependence of Heterochromatic Histone H3 Methylation Patterns on the Arabidopsis Gene DDM1

The Arabidopsis gene DDM1 is required to maintain DNA methylation levels and is responsible for transposon and transgene silencing. However, rather than encoding a DNA methyltransferase, DDM1 has similarity to the SWI/SNF family of adenosine triphosphate–dependent chromatin remodeling genes, suggesting an indirect role in DNA methylation. Here we show thatDDM1 is also required to maintain histone H3 methylation patterns. In wild-type heterochromatin, transposons and silent genes are associated with histone H3 methylated at lysine 9, whereas known genes are preferentially associated with methylated lysine 4. Inddm1 heterochromatin, DNA methylation is lost, and methylation of lysine 9 is largely replaced by methylation of lysine 4. Because DNA methylation has recently been shown to depend on histone H3 lysine 9 methylation, our results suggest that transposon methylation may be guided by histone H3 methylation in plant genomes. This would account for the epigenetic inheritance of hypomethylated DNA once histone H3 methylation patterns are altered.
Changes in vitellogenin expression caused by nematodal and fungal infections in insects

ABSTRACT This study examined the expression and role of vitellogenin (Vg) in the body of the firebug Pyrrhocoris apterus (Heteroptera, Insecta) during infection elicited by two entomopathogenic organisms, the nematode Steinernema carpocapsae and the fungus Isaria fumosorosea. Infection by S. carpocapsae significantly upregulated Vg mRNA expression in the male body. The corresponding increase in Vg protein expression was also confirmed by electrophoretic and immunoblotting analyses. Remarkably, in females, the opposite tendency was noted. Nematodal infection significantly reduced both Vg mRNA and Vg protein expression levels in fat body and hemolymph, respectively. We speculate that infection of reproductive females reduces Vg expression to a level that is still sufficient for defense, but is insufficient for reproduction. This circumstance reduces energy expenditure and helps the individual to cope with the infection. Importantly, purified Vg significantly inhibited growth of Xenorhabdus spp. , an entomotoxic bacteria isolated from S. carpocapsae. However, the effect of Vg against I. fumosorosea was not so obvious. The fungus significantly stimulated Vg gene expression in males; however, a similar increase was not recapitulated at the protein level. Nevertheless, in females, both mRNA and protein Vg levels were significantly reduced after the fungal infection. The obtained data demonstrate that Vg is probably an important defense protein, possibly with a specific activity. This considerably expands the known spectrum of Vg functions, as its primary role was thought to be limited to regulating egg development in the female body. Highlighted Article: Nematodal infection and fungal infection cause changes in insect vitellogenin mRNA and protein expression: vitellogenin inhibits the growth of entomotoxic bacteria from the nematode Steinernema carpocapsae.
Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts

Collaborative filtering (CF) is one of the most successful approaches for recommendation. In this paper, we propose two hybrid CF algorithms, sequential mixture CF and joint mixture CF, each combining advice from multiple experts for effective recommendation. These proposed hybrid CF models work particularly well in the common situation when data are very sparse. By combining multiple experts to form a mixture CF, our systems are able to cope with sparse data to obtain satisfactory performance. Empirical studies show that our algorithms outperform their peers, such as memory-based, pure model-based, pure content-based CF algorithms, and the content- boosted CF (a representative hybrid CF algorithm), especially when the underlying data are very sparse.
Effect of locally produced complementary foods on fat-free mass, linear growth, and iron status among Kenyan infants: A randomized controlled trial.

The impact of quality complementary food products on infant growth and body composition has not been adequately investigated. This study evaluated the effect on fat-free mass (FFM) accrual, linear growth, and iron status of locally produced complementary food products comparing to a standard product. In a randomized, double-blind trial, 499 infants at 6 months received nine monthly rations of (a) WinFood Classic (WFC) comprising germinated amaranth (71%), maize (10.4%), small fish (3%), and edible termites (10%); (b) WinFood Lite (WFL) comprising germinated amaranth (82.5%), maize (10.2%), and multimicronutrient premix; or (c) fortified corn-soy blend plus (CSB+). Primary outcomes were changes in FFM, length, and plasma ferritin and transferrin receptors (TfR). FFM was determined using deuterium dilution. Analysis was by intention to treat, based on available cases. Compared with CSB+, there were no differences in change from 6 to 15 months in FFM for WFC 0.0 kg (95% CI [-0.30, 0.29]) and WFL 0.03 kg (95% CI [-0.25, 0.32]) and length change for WFC -0.3 cm (95% CI [-0.9, 0.4]) and WFL -0.3 cm (95% CI [-0.9, 0.3]). TfR increased in WFC group 3.3 mg L-1 (95% CI [1.7, 4.9]) and WFL group 1.7 mg L-1 (95% CI [0.1, 3.4]) compared with CSB+. Compared with the increase in Hb in CSB+ group, there was a reduction in Hb in WFC of -0.9 g dl-1 (95% CI [-1.3, -0.5]) and a lower increase in WFL -0.4 g dl-1 (95% CI [-0.8, 0.0]). In conclusion, the tested WinFoods had the same effect on FFM and length as CSB+, whereas Hb and iron status decreased, suggesting inhibited iron bioavailability from the amaranth-based WinFoods.
Creative User-Centered Visualization Design for Energy Analysts and Modelers

We enhance a user-centered design process with techniques that deliberately promote creativity to identify opportunities for the visualization of data generated by a major energy supplier. Visualization prototypes developed in this way prove effective in a situation whereby data sets are largely unknown and requirements open - enabling successful exploration of possibilities for visualization in Smart Home data analysis. The process gives rise to novel designs and design metaphors including data sculpting. It suggests: that the deliberate use of creativity techniques with data stakeholders is likely to contribute to successful, novel and effective solutions; that being explicit about creativity may contribute to designers developing creative solutions; that using creativity techniques early in the design process may result in a creative approach persisting throughout the process. The work constitutes the first systematic visualization design for a data rich source that will be increasingly important to energy suppliers and consumers as Smart Meter technology is widely deployed. It is novel in explicitly employing creativity techniques at the requirements stage of visualization design and development, paving the way for further use and study of creativity methods in visualization design.
The Meiotic Recombination Checkpoint Suppresses NHK-1 Kinase to Prevent Reorganisation of the Oocyte Nucleus in Drosophila

The meiotic recombination checkpoint is a signalling pathway that blocks meiotic progression when the repair of DNA breaks formed during recombination is delayed. In comparison to the signalling pathway itself, however, the molecular targets of the checkpoint that control meiotic progression are not well understood in metazoans. In Drosophila, activation of the meiotic checkpoint is known to prevent formation of the karyosome, a meiosis-specific organisation of chromosomes, but the molecular pathway by which this occurs remains to be identified. Here we show that the conserved kinase NHK-1 (Drosophila Vrk-1) is a crucial meiotic regulator controlled by the meiotic checkpoint. An nhk-1 mutation, whilst resulting in karyosome defects, does so independent of meiotic checkpoint activation. Rather, we find unrepaired DNA breaks formed during recombination suppress NHK-1 activity (inferred from the phosphorylation level of one of its substrates) through the meiotic checkpoint. Additionally DNA breaks induced by X-rays in cultured cells also suppress NHK-1 kinase activity. Unrepaired DNA breaks in oocytes also delay other NHK-1 dependent nuclear events, such as synaptonemal complex disassembly and condensin loading onto chromosomes. Therefore we propose that NHK-1 is a crucial regulator of meiosis and that the meiotic checkpoint suppresses NHK-1 activity to prevent oocyte nuclear reorganisation until DNA breaks are repaired.
The Association of Serum Levels of Brain-Derived Neurotrophic Factor with the Occurrence of and Recovery from Delirium in Older Medical Inpatients

Limited studies of the association between BDNF levels and delirium have given inconclusive results. This prospective, longitudinal study examined the relationship between BDNF levels and the occurrence of and recovery from delirium. Participants were assessed twice weekly using MoCA, DRS-R98, and APACHE II scales. BDNF levels were estimated using an ELISA method. Delirium was defined with DRS-R98 (score > 16) and recovery from delirium as ≥2 consecutive assessments without delirium prior to discharge. We identified no difference in BDNF levels between those with and without delirium. Excluding those who never developed delirium (n = 140), we examined the association of BDNF levels and other variables with delirium recovery. Of 58 who experienced delirium, 39 remained delirious while 19 recovered. Using Generalized Estimating Equations models we found that BDNF levels (Wald χ2 = 7.155; df: 1, p = 0.007) and MoCA (Wald χ2 = 4.933; df: 1, p = 0.026) were associated with recovery. No significant association was found for APACHE II, dementia, age, or gender. BDNF levels do not appear to be directly linked to the occurrence of delirium but recovery was less likely in those with continuously lower levels. No previous study has investigated the role of BDNF in delirium recovery and these findings warrant replication in other populations.
E-Lections: Voting Behavior and the Internet

This paper analyses the effect of information disseminated by the Internet on voting behavior. We address endogeneity in Internet availability by exploiting regional and technological peculiarities of the preexisting voice telephony network that hinder the roll-out of fixed-line broadband infrastructure for high-speed Internet. We find small negative effects of Internet availability on voter turnout, and no evidence that the Internet systematically benefits single parties. Robustness tests including placebo estimations from the pre-Internet era confirm our results. We relate differences in the Internet effect between national and local elections to a crowding out of national but not local newspapers.
Lysosomal Dysfunction in Down Syndrome Is APP-Dependent and Mediated by APP-βCTF (C99)

Lysosomal failure underlies pathogenesis of numerous congenital neurodegenerative disorders and is an early and progressive feature of Alzheimer's disease (AD) pathogenesis. Here, we report that lysosomal dysfunction in Down ayndrome (trisomy 21), a neurodevelopmental disorder and form of early onset AD, requires the extra gene copy of amyloid precursor protein (APP) and is specifically mediated by the β cleaved carboxy terminal fragment of APP (APP-βCTF, C99). In primary fibroblasts from individuals with DS, lysosomal degradation of autophagic and endocytic substrates is selectively impaired, causing them to accumulate in enlarged autolysosomes/lysosomes. Direct measurements of lysosomal pH uncovered a significant elevation (0.6 units) as a basis for slowed LC3 turnover and the inactivation of cathepsin D and other lysosomal hydrolases known to be unstable or less active when lysosomal pH is persistently elevated. Normalizing lysosome pH by delivering acidic nanoparticles to lysosomes ameliorated lysosomal deficits, whereas RNA sequencing analysis excluded a transcriptional contribution to hydrolase declines. Cortical neurons cultured from the Ts2 mouse model of DS exhibited lysosomal deficits similar to those in DS cells. Lowering APP expression with siRNA or BACE1 inhibition reversed cathepsin deficits in both fibroblasts and neurons. Deleting one Bace1 allele from adult Ts2 mice had similar rescue effects in vivo. The modest elevation of endogenous APP-βCTF needed to disrupt lysosomal function in DS is relevant to sporadic AD where APP-βCTF, but not APP, is also elevated. Our results extend evidence that impaired lysosomal acidification drives progressive lysosomal failure in multiple forms of AD. SIGNIFICANCE STATEMENT Down syndrome (trisomy 21) (DS) is a neurodevelopmental disorder invariably leading to early-onset Alzheimer's disease (AD). We showed in cells from DS individuals and neurons of DS models that one extra copy of a normal amyloid precursor protein (APP) gene impairs lysosomal acidification, thereby depressing lysosomal hydrolytic activities and turnover of autophagic and endocytic substrates, processes vital to neuronal survival. These deficits, which were reversible by correcting lysosomal pH, are mediated by elevated levels of endogenous β-cleaved carboxy-terminal fragment of APP (APP-βCTF). Notably, similar endosomal-lysosomal pathobiology emerges early in sporadic AD, where neuronal APP-βCTF is also elevated, underscoring its importance as a therapeutic target and underscoring the functional and pathogenic interrelationships between the endosomal-lysosomal pathway and genes causing AD.