The Conceptual Politics of Democracy Promotion: The Venezuela Case

In 1970 Giovanni Sartori articulated what he saw as the greatest challenge to political science in an increasingly globalized worldconceptual stretching. Sartori was referring to the traveling of western concepts eastward and proposed the use of a conceptual ladder to help inform the decisions political scientists make regarding the concepts they wish to travel. This paper seeks to push the boundaries of Sartori’s critique beyond academia to include policy; a subject where a dynamic and informative relationship between academia and policy should exist, but are instead faced with a one-dimensional arrangement. In that vein, this paper consists of three main parts. The first employs a brief historiography of the conceptual debate between, largely, Schumpeterian and Dahlian definitions of democracy where two main schools of thought will be sketched. The second evokes Venezuela's recent political history to illustrate how the United States Government has, at different times, employed various definitions, and standards, of democracy to describe the Venezuelan regime. The third seeks to establish how American oscillation between standards erodes the value reference point of democracy and draw out the implications of this. In particular, the third part unpacks what this erosion should mean moving forward for academics engaged in the conceptual politics of democracy. In sum, the instrumentalizing of the ambiguity of the concept—democracy—by oscillating between Schumpeterian and Dahlian standards devalues the concept. And unless the academic debate regarding democratic theory begins to account for this devaluation, democracy may well be emptied of its substance.

Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each clinical application, and the available multimodal medical datasets. We followed the PRISMA-ScR guidelines. We searched Embase, PubMed, Scopus, and Google Scholar to retrieve relevant studies. We extracted data from 34 studies that fulfilled the inclusion criteria. In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). We found that multimodality fusion models outperformed traditional single-modality models for the same task. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective.Comment: Accepted in Nature Scientific Reports. 20 page

Interplay between innate immunity and the viral oncoproteins Tax and HBZ in the pathogenesis and therapeutic response of HTLV-1 associated adult T cell leukemia

The Human T-cell Leukemia virus type 1 (HTLV-1) causes an array of pathologies, the most aggressive of which is adult T-cell leukemia (ATL), a fatal blood malignancy with dismal prognosis. The progression of these diseases is partly ascribed to the failure of the immune system in controlling the spread of virally infected cells. HTLV-1 infected subjects, whether asymptomatic carriers or symptomatic patients are prone to opportunistic infections. An increasing body of literature emphasizes the interplay between HTLV-1, its associated pathologies, and the pivotal role of the host innate and adoptive immune system, in shaping the progression of HTLV-1 associated diseases and their response to therapy. In this review, we will describe the modalities adopted by the malignant ATL cells to subvert the host innate immune response with emphasis on the role of the two viral oncoproteins Tax and HBZ in this process. We will also provide a comprehensive overview on the function of innate immunity in the therapeutic response to chemotherapy, anti-viral or targeted therapies in the pre-clinical and clinical settings

A Real-Time Video-Streaming System for Monitoring Demining

The most deployed detection technology for landmine clearance is the metal detector (MD).1 Other detection technologies exist, such as ground penetrating radar,2 chemical sensors,3 biological sensors,4 and infrared imaging,5 to name a few. However, despite their widespread use, MDs suffer from high false-alarm (FA) rates since they cannot differentiate between the metal components in a landmine and harmless metal clutter. Deminers using MDs usually rely on their personal experience to differentiate between the sounds emitted by the MD when scanning a landmine or an item of clutter. Usually, they continue to excavate on a large number of occasions and end up finding a harmless piece of metal. For each found single landmine, it is estimated that a hundred to a thousand false positives are encountered.6 The high FA rate substantially slows the demining process and increases costs. This delays the recovery of contaminated land and the resumption of everyday activities around the affected areas

Controversies in Targeted Therapy of Adult T Cell Leukemia/Lymphoma: ON Target or OFF Target Effects?

Adult T cell leukemia/lymphoma (ATL) represents an ideal model for targeted therapy because of intrinsic chemo-resistance of ATL cells and the presence of two well identified targets: the HTLV-I retrovirus and the viral oncoprotein Tax. The combination of zidovudine (AZT) and interferon-alpha (IFN) has a dramatic impact on survival of ATL patients. Although the mechanism of action remains unclear, arguments in favor or against a direct antiviral effect will be discussed. Yet, most patients relapse and alternative therapies are mandatory. IFN and arsenic trioxide induce Tax proteolysis, synergize to induce apoptosis in ATL cells and cure Tax-driven ATL in mice through specific targeting of leukemia initiating cell activity. These results provide a biological basis for the clinical success of arsenic/IFN/AZT therapy in ATL patients and suggest that both extinction of viral replication (AZT) and Tax degradation (arsenic/IFN) are needed to cure ATL

Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study

Several high-resource Text to Speech (TTS) systems currently produce natural, well-established human-like speech. In contrast, low-resource languages, including Arabic, have very limited TTS systems due to the lack of resources. We propose a fully unsupervised method for building TTS, including automatic data selection and pre-training/fine-tuning strategies for TTS training, using broadcast news as a case study. We show how careful selection of data, yet smaller amounts, can improve the efficiency of TTS system in generating more natural speech than a system trained on a bigger dataset. We adopt to propose different approaches for the: 1) data: we applied automatic annotations using DNSMOS, automatic vowelization, and automatic speech recognition (ASR) for fixing transcriptions' errors; 2) model: we used transfer learning from high-resource language in TTS model and fine-tuned it with one hour broadcast recording then we used this model to guide a FastSpeech2-based Conformer model for duration. Our objective evaluation shows 3.9% character error rate (CER), while the groundtruth has 1.3% CER. As for the subjective evaluation, where 1 is bad and 5 is excellent, our FastSpeech2-based Conformer model achieved a mean opinion score (MOS) of 4.4 for intelligibility and 4.2 for naturalness, where many annotators recognized the voice of the broadcaster, which proves the effectiveness of our proposed unsupervised method

Nonlinear Control Systems Simulation Using Spreadsheets

In this paper, a method for simulating nonlinear control systems using spreadsheets is presented. Various nonlinear blocks are simulated using graphics and cell formulas, and are generated by clicking on specially developed toolbar buttons. These blocks can be connected to one another using a simple and intuitive procedure again based on graphics and toolbar buttons. A complete nonlinear system can thus be created by generating and connecting its constituting basic blocks, using the simple graphics interface provided. The corresponding data may then be entered in the familiar manner as illustrated, and finally the system can be simulated literally at the click of a button. Such a system can be analyzed by calculating its time response to any input signal or by using other methods such as phase-plane trajectories. The simulation is characterized by its availability, flexibility, and simplicity. The paper provides several examples to illustrate the simulation capabilities available. The first example considers a servo with a dead-zone and a saturation amplifier, the second illustrates the steps required to obtain a phase-plane trajectory, and the third example considers a nonlinear system having a PI controller and nonlinearity consisting of soft saturation. The final example illustrates a relay-controlled servo system