Miracle-dz/UniFAR-FTUs huggingface dataset:
Affective and Contextual Embedding for Sarcasm Detection

Automatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection remains a challenging task, due to a lack of any vocal intonation or facial gestures in textual data. To date, most of the approaches to addressing the problem have relied on hand-crafted affect features, or pre-trained models of non-contextual word embeddings, such as Word2vec. However, these models inherit limitations that render them inadequate for the task of sarcasm detection. In this paper, we propose two novel deep neural network models for sarcasm detection, namely ACE 1 and ACE 2. Given as input a text passage, the models predict whether it is sarcastic (or not). Our models extend the architecture of BERT by incorporating both affective and contextual features. To the best of our knowledge, this is the first attempt to directly alter BERT’s architecture and train it from scratch to build a sarcasm classifier. Extensive experiments on different datasets demonstrate that the proposed models outperform state-of-the-art models for sarcasm detection with significant margins.
Adaptive divergence and the evolution of hybrid phenotypes in threespine stickleback

The fitness of hybrids is a critical determinant of gene flow between hybridizing populations. If hybrid phenotypes change predictably as parental populations become increasingly divergent, this could provide insight into general mechanisms linking ecological divergence with reproductive isolation. In this study, we used threespine stickleback fish (Gasterosteus aculeatus L.) to examine how phenotypic divergence between populations drives the evolution of dominance, phenotypic variation, and trait ‘mismatch’ in hybrids. We generated F1 and F2 hybrids between 12 freshwater populations—which ranged from highly planktivorous to highly benthic-feeding—and an anadromous population that is highly planktivorous and resembles the ancestral state of derived freshwater populations. We measured 16 phenotypic traits in hybrids and pure parental individuals raised under common conditions. We found that dominance varied markedly among traits. By contrast, dominance for a given trait was typically consistent among populations except for two traits where dominance was predicted by the phenotype of the freshwater parent. We find that multivariate phenotypic variation is greater in hybrids between more divergent parents. Finally, we demonstrate that the extent to which parental traits are ‘mismatched’ in both F1 and F2 hybrids increases with the phenotypic distance between the parent populations. Critically, this relationship was clearer in F1 hybrids than in F2s—largely due to traits having different dominance coefficients and F1s having relatively little phenotypic variation. Our results demonstrate that some aspects of hybrid phenotypes evolve predictably as parental populations diverge. We also find evidence for a possible general mechanistic link between ecological divergence and reproductive isolation—that more divergent parent populations tend to produce hybrids with novel and potentially deleterious multivariate phenotypes.
The vascular niche controls Drosophila hematopoiesis via fibroblast growth factor signaling

In adult mammals, hematopoiesis, the production of blood cells from hematopoietic stem and progenitor cells (HSPCs), is tightly regulated by extrinsic signals from the microenvironment called “niche”. Bone marrow HSPCs are heterogeneous and controlled by both endosteal and vascular niches. The Drosophila hematopoietic lymph gland is located along the cardiac tube which corresponds to the vascular system. In the lymph gland, the niche called Posterior Signaling Center controls only a subset of the heterogeneous hematopoietic progenitor population indicating that additional signals are necessary. Here we report that the vascular system acts as a second niche to control lymph gland homeostasis. The FGF ligand Branchless produced by vascular cells activates the FGF pathway in hematopoietic progenitors. By regulating intracellular calcium levels, FGF signaling maintains progenitor pools and prevents blood cell differentiation. This study reveals that two niches contribute to the control of Drosophila blood cell homeostasis through their differential regulation of progenitors.
Modelling optically pumped magnetometer interference as a mean (magnetic) field

Here we propose that much of the magnetic interference observed when using optically pumped magnetometers can be modeled spatially as a mean (magnetic) field. We show that this approximation reduces sensor level variability and substantially improves statistical power. This model does not require knowledge of the underlying neuroanatomy nor the sensor positions. It only needs information about the sensor orientation. Due to the model’s low rank there is little risk of removing substantial neural signal. However, we provide a framework to assess this risk for any sensor number, design or subject neuroanatomy. We find that the risk of unintentionally removing neural signal is reduced when multi-axis recordings are performed. We validated the method using a binaural auditory evoked response paradigm and demonstrated that the mean field correction increases reconstructed SNR in relevant brain regions in both the spatial and temporal domain. Considering the model’s simplicity and efficacy, we suggest that this mean field correction can be a powerful preprocessing step for arrays of optically pumped magnetometers.
Ovid: Message-based Automatic Contact Tracing

—The C OVID -19 pandemic created various new challenges for our societies. Quickly discovering new infections using automated contact tracing without endangering privacy of the general public is one of these. Most discussions concerning architectures for contact tracing applications revolved around centralized against decentralized approaches. In contrast, the system proposed in this work builds on the idea of message-based contact tracing to inform users of their risk. Our main contribution is the combination of a blind-signature approach to verify infections with an anonymous postbox service. In our evaluation we analyze all components in our system for performance and privacy, as well as security. We derive parameters for operating our system in a pandemic scenario.
Egr2 induction in Drd1+ ensembles of the ventrolateral striatum supports the development of cocaine reward

Drug addiction develops due to brain-wide plasticity within neuronal ensembles, mediated by dynamic gene expression. Though the most common approach to identify such ensembles relies on immediate early gene expression, little is known of how the activity of these genes is linked to modified behavior observed following repeated drug exposure. To address this gap, we present a broad-to-specific approach, beginning with a comprehensive investigation of brain-wide cocaine-driven gene expression, through the description of dynamic spatial patterns of gene induction in subregions of the striatum, and finally address functionality of region-specific gene induction in the development of cocaine preference. Our findings reveal differential cell-type specific dynamic transcriptional recruitment patterns within two subdomains of the dorsal striatum following repeated cocaine exposure. Furthermore, we demonstrate that induction of the IEG Egr2 in the ventrolateral striatum, as well as the cells within which it is expressed, are required for the development of cocaine seeking. Impact statement VLS ensembles are dynamically recruited by cocaine experiences to mediate cocaine reward.
Integration in or Near Oncogenes Plays Only a Minor Role in Determining the in Vivo Distribution of HIV Integration Sites Before or During Suppressive Antiretroviral Therapy

HIV persists during antiretroviral therapy (ART) as integrated proviruses in cells descended from a small fraction of the CD4+ T cells infected prior to the initiation of ART. To better understand what controls HIV persistence and the distribution of integration sites (IS), we compared about 16,000 and 54,000 IS from individuals pre-ART and on ART, respectively, with approximately 385,000 IS from PBMC infected in vitro. The distribution of IS in vivo is quite similar to the distribution in PBMC, modified by selection against proviruses in expressed genes, by selection for proviruses integrated into one of 6 specific genes, and by clonal expansion. The clones in which a provirus integrated in an oncogene contributed to the survival of the clone comprise only a small fraction of the clones that persist in HIV-infected individuals on ART. Mechanisms that do not involve the provirus, or its location in the host genome, are more important in determining which clones expand and persist. Author Summary In HIV-infected individuals, a small fraction of the infected cells persist and divide. This reservoir persists on ART and can rekindle the infection if ART is discontinued. Because the number of possible sites of HIV DNA integration is very large, each infected cell, and all of its descendants, can be identified by the site where the provirus is integrated (IS). To understand the selective forces that determine the fates of infected cells in vivo, we compared the distribution of HIV IS in freshly-infected cells to cells from HIV-infected donors sampled both before and during ART. We found that, as has been previously reported, integration favors highly-expressed genes. However, over time the fraction of cells with proviruses integrated in highly-expressed genes decreases, implying that they grow less well. There are exceptions to this broad negative selection. When a provirus is integrated in a specific region in one of six genes, the proviruses affect the expression of the target gene, promoting growth and/or survival of the cell. Although this effect is striking, it is only a minor component of the forces that promote the growth and survival of the population of infected cells during ART.
Improved Key Recovery of the HFEv- Signature Scheme

The HFEv-signature scheme is a twenty year old multivariate public key signature scheme. It uses the Minus and the Vinegar modiﬁer on the original HFE scheme. An instance of the HFEv-signature scheme called GeMSS is one of the alternative candidates for signature schemes in the third round of the NIST Post Quantum Crypto (PQC) Standardization Project. In this paper, we propose a new key recovery attack on the HFEv-signature scheme. We show that the Minus modiﬁcation does not enhance the security of cryptosystems of the HFE family, while the Vinegar modiﬁcation increases the complexity of our attack only by a polynomial factor. By doing so, we show that the proposed parameters of the GeMSS scheme are not as secure as claimed. Our attack shows that it is very diﬃcult to build a secure and eﬃcient signature scheme on the basis of HFEv-.
Understanding the role of digital communication and mediation strategies in community-led territorial innovation: a systematic review

This article brings together empirical academic research on community-led territorial innovation initiatives. By engaging in a systematic literature review, the research analyses the role of digital technology in supporting community-led initiatives. Besides identifying the technologies used, this research develops an understanding both on its purpose of use and on its relation to communication and mediation strategies. A clear gap is found not only in terms of research reporting on community-led initiatives, but also on research studying the specific use of digital technology by those communities, highlighting a need for future research in the area. From an initial set of 1312, six articles are identified which meet the inclusion criteria for this review and only five of them report on technology use. Results show that a diversity of digital technologies, from blogs to online repositories, is used by the initiatives analysed, leveraging on the global coverage of the Internet. Besides a key role in supporting community collaboration and cooperation, digital technology also emerges as an important vehicle for community debate and as an enabler of community empowerment and advocacy.
Deep Imitation Learning for Broom-Manipulation Tasks Using Small-Sized Training Data

It is important for robots to learn the usage of tools and support humans in aging societies. It is expected for robots possible to imitate human skills of tool manipulation properly using a deep neural network, although a huge amount of training data may be required. In this paper, a target human-like task of cleaning dust using several types of brooms with a robot arm is considered. A learning system that can reduce the amount of training data is proposed. The novelty of the proposed system is the ability to estimate the initial parameters of a deep neural network based on the shape of the broom and data stored from previous experience. Furthermore, the system changes the number of learning layers in the deep neural network depending on the broom shape. Results of experiences show the effectiveness in reducing the amount of training data.
Removal of petroleum products from water using natural sorbent zeolite

Abstract An experimental investigation on the removal of petroleum products (PP) from wastewater by natural sorbent zeolite was carried out in a laboratory on a pilot‐scaled test bench. The pilot test bench consisted of: a tank in which gasoline (1 portion) and diesel (1 portion) were mixed mechanically with water; a pump for supplying the mixture to a filter model; a filter model filled with a zeolite layer of 0,2 m height. Zeolite used in this study was from a deposit near the village of Sokyrnytsa in the Ukrainian Transcarpathian region. The test zeolite particle size was 2,5–3,0 mm; 1,5–2,0 mm; 0,63–1,0 mm. The concentration of PP in water was measured before and after the filter every hour. The TOG/ TPH analyser was used for concentration measurements. The experimental study showed that the best adsorption results were reached in the filter with 0,63–1,0 mm particle size of zeolite media. There were 2,25 mg/1 of light hydrocarbons left in the filtrate. Such a concentration satisfies the requirements .
A Low Power Wideband Balun-LNA Employing Local Feedback, Modified Current-Bleeding Technique and Balanced Loads

In this paper, a new structure for a noise-canceling and highly-linear wideband LNA with intrinsic balun based on the common-gate common-source (CG-CS) topology is presented for low-voltage and low-power applications. Since unsymmetrical loads make a mismatch in the phase and the gain of the differential output, a modified current-bleeding technique is utilized to let the balun-LNA have a differential output with symmetric loads. Adding this additional branch increases the power consumption. For reducing the power, a local feedback is used to boost the transconductance of the CG stage by the gain provided by the CS stage so the current of the CG stage can be decreased by the same factor and the power consumption is reduced accordingly. Utilizing local feedback improves the NF and the gain of the LNA as well. The linearity in this structure is improved by using the post-distortion technique. The proposed balun-LNA is simulated in 65 nm RF CMOS technology and covers the frequency range of 0.65-8.5 GHz. It has a differential voltage gain of 17 dB, an S11 of less than −13 dB, and an NF of 2.75-3.5 dB across the whole bandwidth. It consumes only 7.5 mA from a 1-V power supply.
EKF-Based Fuzzy Sliding Mode Control Using Neural Networks

This essay addresses an observer-based robust fuzzy sliding mode controller design to maintain stability and better tracking of a nonlinear system. To suppress the chattering phenomenon, a fuzzy sliding mode controller (sf) is used. Also, a neural network is utilized to generate gain of the optimal controller. The performance of the designed controller is evaluated with the Magnetic Levitation System (MLS), which has a sever nonlinearity in its model. Also, the Extended Kalman Filter (EKF) is utilized to approximate the states of this system accurately in the presence of perturbation and environmental noise. The simulation results are reported to show the effectiveness of the proposed method.
Adaptive Task Assignment for Thermal Management in Multi-Core Processing Systems

One efficient approach to control chip-wide thermal distribution in multi-core systems is the optimization of online assignments of tasks to processing cores. Online task assignment, however, faces several uncertainties in real-world systems and does not show a deterministic nature. In this paper, we consider the operation of a thermal-aware task scheduler, dispatching tasks from an arrival queue as well as setting the voltage and frequency of the processing cores to optimize the mean temperature margin of the entire chip (i.e., cores as well as the NoC routers). We model the decision process of the task scheduler as a semi-Markov decision problem (SMDP). Then, to solve the formulated SMDP, we propose a reinforcement learning algorithm that is capable of computing the optimal task assignment policy without requiring the statistical knowledge of the stochastic dynamics underlying the system states. The proposed algorithm also relies on function approximation techniques to handle the infinite length of the task queue as well as the continuous nature of temperature readings. Compared to related research, the simulation results show nearly 6 centigrade reduction in system average peak temperature and 66 milliseconds decrease in mean task service time.
ELM: A Low-Latency and Scalable Memory Encryption Scheme

Memory encryption (ME) with authentication is becoming a key security feature of modern processors, as evident by the adoption of ME by Intel’s SGX. Recently ME is actively studied from the viewpoint of system architecture. This paper studies ME from the viewpoint of symmetric-key cryptographic designs, with a primal focus on latency. A significant progress in such a direction can be observed in the SGX Integrity Tree (<inline-formula> <tex-math notation="LaTeX">$\textsf {SIT}$ </tex-math></inline-formula>). Using a variant of AES-GCM, <inline-formula> <tex-math notation="LaTeX">$\textsf {SIT}$ </tex-math></inline-formula> achieves an excellent latency. However, it has a scalability issue. By carefully examining <inline-formula> <tex-math notation="LaTeX">$\textsf {SIT}$ </tex-math></inline-formula>, we develop a new ME scheme dubbed <inline-formula> <tex-math notation="LaTeX">$\textsf {ELM}$ </tex-math></inline-formula>. We present an AES-based instantiation of <inline-formula> <tex-math notation="LaTeX">$\textsf {ELM}$ </tex-math></inline-formula>, and show that <inline-formula> <tex-math notation="LaTeX">$\textsf {ELM}$ </tex-math></inline-formula> significantly reduces latency from <inline-formula> <tex-math notation="LaTeX">$\textsf {SIT}$ </tex-math></inline-formula> for large memories, and achieves the provable security and equivalent hardware-protected (on-chip) area. We also present preliminary hardware implementations to substantiate our advantages.
Fabrication of Small-Scale Solid-State Nanopores by Dielectric Breakdown

As a tool for next-generation DNA sequencing, solid-state nanopores have significant advantages in terms of stability and process integration. However, traditional manufacturing methods such as FIB and TEM are pretty expensive and require higher-skilled operators. In this paper, we explored the effect of the initial leakage current on the dielectric breakdown time and then demonstrated a low-cost and rapid method of fabricating SiNx nanopores. We prepared nanopores of appropriate size by changing the voltage and cut-off current, and finally realized the preparation of nanopores as small as a few nanometers and as large as tens of nanometers. We also found that the thinned area of silicon nitride film affects the value of the initial leakage current, thereby impacting the time of dielectric breakdown. The foundation of these problems helps us to perfect the fabrication of nanopores by dielectric breakdown.
New Transformer-Less Single Switch Structure for Boost DC-DC Converter with Low Input Current Ripple

This paper is presenting a new transformer-less single switch structure for boost dc-dc converter based on voltage lift (VL) technique. Using single switch, and free and low input current ripple are the proposed converter features. Also, the input capacitor of the proposed converter acts as input filter that adapt to the proposed structure for renewable energies. The VL technique is applied in boost converters. Here, the relations of each device of proposed converter are calculated in continuous current mode (CCM) with details. Also, its output gain is calculated in CCM. Then, the switch stress is analyzed in CCM. To correctness of the theoretical concepts are tested by simulation results using PSCAD/EMTDC.
Balanced Application Scheduling for Process Variation Influenced Chip Multiprocessors

Within-die process variations in chip-multiprocessors cause wide variation in the distribution of frequency and power consumption between similar cores. In such condition, considering variations in task allocation and power management algorithms is crucial. Among various methods proposed to improve performance and reduce power consumption in variation-influenced chip-multiprocessors, two of them are more popular: variation-aware application scheduling and per-core dynamic voltage and frequency scaling (DVFS). However, none of the proposed methods guarantees load balancing in chip-multiprocessors and as a result, appropriate distribution of power in task allocation cannot be achieved by these methods. In this paper we propose an algorithm which intelligently maps (and remaps) applications into cores considering their variations. Moreover, proposed power management algorithm maximizes the overall performance at a given power budget. For power management, low resolution DVFS is used to decrease algorithm overhead. Additionally, the algorithm is “balanced” to avoid hotspots on the chip. The proposed method is tested on a special-purpose simulator based on E3S data-set Experimental results show that the algorithm achieves significant improvement in terms of power distribution and fair application scheduling compared to the other methods.