Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact Storage for Homomorphic Encryption

Homomorphic encryption ( HE ) is a promising technology for protecting data in use, with considerable recent years progress towards attaining practical runtime performance. However the high storage overhead associated with HE remains an obstacle preventing its large scale adoption. In this work we propose a new storage solution in the two-server model resolving the high storage overhead associated with HE , while preserving data confidentiality. Our solution attains the following desired properties:
EM509 Stochastic Processes Project : Random Walks

First random walks are introduced in the order of 1D (without barriers and with barriers), 2D (without barriers and with barriers). Then Markov property is explained. The Markov property is proved for the 1D simple case and a complicated 2D case with both absorbing and reﬂecting barriers. Image segmentation problem is introduced from an application point of view and converted into a mathematical formulation. Random walker algorithm for image segmentation is introduced and it is proven to be a Markov process. The study is concluded by implementing the Random Walker algorithm and testing it by segmenting a set of images.
The EPOCH Project

The EPOCH (EROS-2 periodic variable star classification using machine learning) project aims to detect periodic variable stars in the EROS-2 light curve database. In this paper, we present the first result of the classification of periodic variable stars in the EROS-2 LMC database. To classify these variables, we first built a training set by compiling known variables in the Large Magellanic Cloud area from the OGLE and MACHO surveys. We crossmatched these variables with the EROS-2 sources and extracted 22 variability features from 28 392 light curves of the corresponding EROS-2 sources. We then used the random forest method to classify the EROS-2 sources in the training set. We designed the model to separate not only δ Scuti stars, RR Lyraes, Cepheids, eclipsing binaries, and long-period variables, the superclasses, but also their subclasses, such as RRab, RRc, RRd, and RRe for RR Lyraes, and similarly for the other variable types. The model trained using only the superclasses shows 99% recall and precision, while the model trained on all subclasses shows 87% recall and precision. We applied the trained model to the entire EROS-2 LMC database, which contains about 29 million sources, and found 117 234 periodic variable candidates. Out of these 117 234 periodic variables, 55 285 have not been discovered by either OGLE or MACHO variability studies. This set comprises 1906 δ Scuti stars, 6607 RR Lyraes, 638 Cepheids, 178 Type II Cepheids, 34 562 eclipsing binaries, and 11 394 long-period variables.
What’s Wrong with Deep Learning for Meaning Understanding

In this paper I will tackle the debated question of whether Deep Neural Networks “understand” Natural Language when they process it either for classification or for generation tasks. I will start by quoting work currently carried out in the field of explainable AI then I will look into the internal procedures adopted by current DNNs to cope with the enormous and untreatable dimension of vocabulary size due to the presence of rare words and the use of subwords or n-gram character sequences. To explain the inability of DNNs to reuse previously discovered knowledge I will deal with the issue of generalization and the lack of systematicity in learning models. I will also propose reasons to motivate the difficulty of generalizing with morphology-rich and weakly configurational languages like Italian. In particular vocabulary size and rare words need a different approach from the one used with English texts. Eventually I will propose some alternative ways to deal with the problems discussed in the paper.
Building Socioemotional Skills by Playing: Experimental Evidence from Brazil

School programs are increasingly promoting the development of socioemotional skills given widely accepted evidence on the importance of these skills for academic performance and employment. Educational systems have also seen an increase in the use of games to promote learning, but there is still limited rigorous evidence of their causal impacts. We conduct a large-scale randomized control trial looking at the impacts of the MindLab program in Brazil, a well-known methodology designed in Israel and adopted in over 21 countries, that uses games to promote learning. We quantify impacts on reasoning, metacognition, socioemotional and academic outcomes focusing on a sample of fifth-grade students. Results show no significant program impacts at the average level, but highlight large heterogeneities across participating municipalities, with some reporting large and positive effects on reasoning and academic outcomes, while others report lower socioemotional results and academic performance. Multiple robustness checks confirm the results. Administrative program data and interviews with school professionals suggest the importance of program design and teacher engagement with the methodology to produce a positive change in students. JEL Classification: H52; I20; I24; I28.
Circles of Global Climate Governance

The working paper examines the UN climate conference (COP26) organised in Glasgow in November 2021 as a transnational mega-event, which constituted not only an important moment in international climate talks, but also a temporary convergence point for a multitude of actors and an arena for conflicts and contestation over framing within a broader global policy space. This perspective allows us to offer a view of the current state of global climate politics more comprehensive than those of analyses focused mainly on the negotiations. Using collaborative event ethnography, over two weeks eight researchers identified the material, spatial and social dimensions of the conference. We identify three circles of climate governance, which framed practices, interactions and debates in Glasgow. These comprise an inner circle of state-led negotiations (‘The In’), an official side programme (‘The Off’) and a relatively heterogeneous wider environment of self-organised events (‘The Fringe’). Each circle is populated by a different set of actors and enacts a distinct representation of ‘the global’. Our analysis of dynamics within each of these circles shows that climate governance has entered a new and contradictory phase, where some boundaries are blurred while others are reaffirmed, and where old conflicts resurface while new dividing lines appear. The Paris architecture for reporting and review has been finalised, but thus far the new approach has failed to close gaps between pledges and objectives for mitigation and climate finance. Global political and corporate elites have seemingly come to acknowledge the climate emergency and the need for a global low-carbon transformation, but the solutions proposed in Glasgow remained partial and fragile, and tightly contained within the dominant horizon of capitalist marketand techno-fixes. The communication strategy of the UNFCCC and the UK Presidency used increasingly radical terms to convey urgency and momentum, which in turn risked emptying activist notions of their content and force. A growing part of the climate movement reacted with critiques of corporate takeover and calls for “real zero” instead of “net zero”. In the conclusion, we examine a series of contentious issues and provide avenues for reflection on the future of climate governance.
Trade Reform, Oligopsony, and Labor Market Distortion: Theory and Evidence

In a heterogeneous-firm model with oligopsonistic local labor markets, this paper shows that opening up to trade can affect distortions in such markets. These distortions arise because firms are large and able to exercise market power over their local workers. Using a panel dataset of Chinese manufacturing firms from 1998-2007, I measure firmlevel labor market distortions and examine their evolution following China’s trade policy reform in 2001. I find that labor market distortions are pervasive and China’s trade policy reforms have led to a substantial net reduction of the distortions, with large effects working through the liberalization of input tariffs. JEL Codes: F12, F14, F16, F61, D43, J42, L13, L22 JEL
TREMOR CLASSIFICATION USING WEARABLE IOT BASED SENSORS

Tremors, a significant symptom of movement disorder, affects a part of the body ranging from slight to severe. These Tremors are symptoms of various neurological diseases such as Parkinson’s disease (PD), Essential tremors (ET), Physiological tremors (PT), Cerebellar tremor, Dystonic tremor, Psychogenic tremor, and many more. Tremor features and types differ for various neurological disorders. During the early stages of the disease, clinical examination of tremors plays a significant role in diagnose management. This work aims to develop a wearable assistive system with an Inertial Measurement Unit (IMU) sensor to classify the tremor of three different neurological disorders based on the tremor position and frequency. This research has been carried out in SRM Medical college and Research Centre with 15 patients. The type of neurodegenerative disease of the subject with tremor is evaluated based on the tremor position and tremor frequency level. The data is collected, transmitted, and processed using the IMU sensor with Internet of things (IoT) and Node MCU board. The decision tree algorithm is used for the classification of tremors. ET, PD, and PT tremors are classified based on the tremor frequency and tremor position. A high rate of accuracy is achieved for the developed system when compared with the Neurologist results. The proposed device quantitatively classified the tremor based on the frequency and position among the three different neurological disorders, i.e., ET, PD, and PT tremors.
The Earth system model CLIMBER-X v1.0 – Part 1: Climate model description and validation​​​​​​​​​​​​​​

Abstract. The newly developed fast Earth system model CLIMBER-X is presented. The climate component of CLIMBER-X consists of a 2.5-D semi-empirical statistical–dynamical atmosphere model, a 3-D frictional–geostrophic ocean model, a dynamic–thermodynamic sea ice model and a land surface model. All the model components are discretized on a regular lat–long grid with a horizontal resolution of 5∘×5∘. The model has a throughput of ∼ 10 000 simulation years per day on a single node with 16 CPUs on a high-performance computer and is designed to simulate the evolution of the Earth system on temporal scales ranging from decades to >100 000 years. A comprehensive evaluation of the model performance for the present day and the historical period shows that CLIMBER-X is capable of realistically reproducing many observed climate characteristics, with results that generally lie within the range of state-of-the-art general circulation models. The analysis of model performance is complemented by a thorough assessment of climate feedbacks and model sensitivities to changes in external forcings and boundary conditions. Limitations and applicability of the model are critically discussed. CLIMBER-X also includes a detailed representation of the global carbon cycle and is coupled to an ice sheet model, which will be described in separate papers. CLIMBER-X is available as open-source code and is expected to be a useful tool for studying past climate changes and for the investigation of the long-term future evolution of the climate.
Impact of Accounting Information Systems on Operational Performance of Nigeria Small Scale Business Enterprises.

The study investigated the Impact of accounting systems on Operational Performance of Nigerian Small Scale Business Enterprises A representative sample of 44 businesses were selected from various categories of small businesses, of which 16 are registered, 12 unregistered, 8 freelanced and 8 mobile businesses at Faringada commercial business area in Jos metropolis of Plateau State, and Bogoro community in Bogoro local Government Area of Bauchi State, Some selected organization has been chosen so as to enable a good work and to allow free-flow of callbacks and for a thorough check of the returned questionnaires, to measure and statistically handle error for quick result. The researcher chose the questionnaire and the interview methods for data collection. The chi-squared and the product moment coefficient of correlation were used for data analysis. The results of the investigation on the hypotheses using the chi-squared were tested at the 5% level of significance using SPSS25. A total number of 15 (fifteen) questions were designed and each questionnaire contain these 15 questions. 44 questionnaires were administered and they were all returned. The likert-scale method of options was used. It was discovered that no matter the size of the business, proper accounting system of record keeping is indispensable, and that accounting system does have a whole lot of influence on the operational performance of small businesses, irrespective of the size of such organization.
Sovateltide Mediated Endothelin B Receptors Agonism and Curbing Neurological Disorders

Neurological/neurovascular disorders constitute the leading cause of disability and the second leading cause of death globally. Major neurological/neurovascular disorders or diseases include cerebral stroke, Alzheimer’s disease, spinal cord injury, neonatal hypoxic-ischemic encephalopathy, and others. Their pathophysiology is considered highly complex and is the main obstacle in developing any drugs for these diseases. In this review, we have described the endothelin system, its involvement in neurovascular disorders, the importance of endothelin B receptors (ETBRs) as a novel potential drug target, and its agonism by IRL-1620 (INN—sovateltide), which we are developing as a drug candidate for treating the above-mentioned neurological disorders/diseases. In addition, we have highlighted the results of our preclinical and clinical studies related to these diseases. The phase I safety and tolerability study of sovateltide has shown it as a safe and tolerable compound at therapeutic dosages. Furthermore, preclinical and clinical phase II studies have demonstrated the efficacy of sovateltide in treating acute ischemic stroke. It is under development as a first-in-class drug. In addition, efficacy studies in Alzheimer’s disease (AD), acute spinal cord injury, and neonatal hypoxic-ischemic encephalopathy (HIE) are ongoing. Successful completion of these studies will validate that ETBRs signaling can be an important target in developing drugs to treat neurological/neurovascular diseases.
CLASSIFICATION OF MOVING OBJECTS FOR INTELLIGENT VIDEO SURVEILLANCE

: Moving object segmentation and classification fromcompressed video plays an important role for intelligent videosurveillance. Compared with H.264/AVC, HEVC introduces ahost of new coding features which can be further exploited formoving object segmentation and classification. In this paper, wepresent a real-time approach to segment and classify movingobject using unique features directly extracted from the HEVCcompressed domain for video surveillance. In the proposedmethod, firstly, motion vector interpolation for intra-codedprediction unit and MV outlier removal are employed forpreprocessing. Secondly, blocks with non-zero motion vectors areclustered into the connected foreground regions using the fourconnectivitycomponent labeling algorithm. Thirdly, object regiontracking based on temporal consistency is applied to the connectedforeground regions to remove the noise regions. The boundary ofmoving object region is further refined by the coding unit size andprediction unit size. Finally, a person-vehicle classification modelusing bag of spatial-temporal HEVC syntax words is trained toclassify the moving objects, either persons or vehicles. Theexperimental results demonstrate that the proposed methodprovides the solid performance and can classify moving personsand vehicles accurately.
Triggers and Barriers to Insight Generation in Personal Visualizations

We report on the ﬁndings from a mixed-methods user study that explores some of the less-studied challenges in designing personal visualizations. We implemented an interface presenting visualiza- tions of the personal data gathered as part of a prior study and conducted a think-aloud study (N=15) of participants’ exploration of their respective data on the interface. We analyzed participant verbal reports and interactions to (i) corroborate the types of insights they gained with the insight types identiﬁed in the literature, (ii) identify contextual information recalled by the participants to interpret their data, and (iii) identify interface design choices that potentially hin-der insight discovery. Our ﬁndings complement prior work and we present design directions for visualizations of personal data, including guidelines for providing baselines for comparison, providing higher explorability for more data-savvy users, and minimizing the need for reading even in more exploratory interfaces.
Support Vector Machine GARCH and Neural Network GARCH Models in Modeling Conditional Volatility: An Application to Turkish Financial Markets

The Turkish version of this paper can be found at: http://ssrn.com/abstract=2222071 The study aims to investigate linear GARCH, fractionally integrated FI-GARCH and Asymmetric Power APGARCH models and their nonlinear counterparts based on Support Vector Regression (SVR) and Neural Network (NN) models. GARCH family models are extended to NN-GARCH architecture of Donaldson and Kamstra (1997) to various NN-GARCH family models (Bildirici and Ersin, 2009) such as NN-APGARCH model. The study aims to introduce a class of extended NN-GARCH and SVR-GARCH family of models with nonlinear augmentations in modeling both the conditional mean and variance. The SVR-GARCH, SVR-APGARCH and SVR-FIAPGARCH and their Multi-Layer Perceptron architecture based counterparts, MLP-GARCH, MLP-APGARCH and MLP-FIAPGARCH are evaluated. An application to daily returns in Istanbul ISE100 stock index is provided. Results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled more efficiently with the models possessing neural network architectures.
Democratic Backsliding and Media Responses to Government Repression: Machine Learning Evidence from Tanzania

One crucial feature of the ongoing global wave of democratic backsliding is that aspiring autocrats seek to inﬂuence the media, oftentimes through legal restrictions on the press and social media. Yet little research has examined how formal and social media respond to those legal restrictions targeting the free ﬂow of information. We develop an original argument linking key characteristics of media sources to the regulatory environment and examine how the content and sentiment of their coverage responds to restrictive media laws. We test our claims using an enormous corpus of electronic media in Tanzania and employ two state-of-the-art neural network models to classify the topics and sentiment of news stories. We then estimate diﬀ-in-diﬀ models exploiting a signiﬁcant legal change that targeted media houses. We ﬁnd that critical news sources censor the tone of their coverage, even as they continue to cover the same issues; we also ﬁnd that international news sources are unable to ﬁll the hole left by a critical domestic press. The paper sheds light on the conditions under which the press can be resilient in the face of legal threats.
Effect of the Management of LGBTT+ Identity on Psychological Wellbeing

The current study, guided by the Minority Stress Model (Meyer, 2003), seeks to examine the effect of sexual orientation or gender identity management (disclosure or concealment) in the relationship between internalized negative ideals (internalized homonegativity and internalized transnegativity) and psychological wellbeing in LGBTT+ people in Puerto Rico. Four instruments were translated from English to Spanish, two of them relating to the management of the minority identity, and two regarding the internalized negative ideals. Additionally, reliability measures were calculated for each instrument. The sample consisted of 203 participants, 165 identified as cisgendered, and 38 identified themselves as trans or another gender. Results suggested that internalized negative ideals predicted the minority identity management in LGBTT+ people. However, the relationship between the internalized negative ideals and psychological wellbeing was only confirmed in terms of sexual orientation and not of gender identity, which suggests that other factors may better explain wellbeing in trans people. A discussion is presented on these results' implications and the study's challenges and limitations due to the Covid-19 pandemic, and suggestions for further studies.
Close correlation between thiolate basicity and certain NMR parameters in cysteine and cystine microspecies

The imbalance between prooxidants and antioxidants in biological systems, known as oxidative stress, can lead to a disruption of redox signaling by the reactive oxygen/nitrogen species and is related to severe diseases. The most vulnerable moiety targeted by oxidant species in the redox signaling pathways is the thiol (SH) group in the cysteine residues, especially in its deprotonated (S−) form. Cysteine, along with its oxidized, disulfide-containing form, cystine, constitute one of the most abundant low molecular weight biological redox couples, providing a significant contribution to the redox homeostasis in living systems. In this work, NMR spectra from cysteine, cystine, and cysteine-containing small peptides were thoroughly studied at the submolecular level, and through the chemical shift data set of their certain atoms it is possible to estimate either thiolate basicity or the also related standard redox potential. Regression analysis demonstrated a strong linear relationship for chemical shift vs thiolate logK of the cysteine microspecies data. The αCH 13C chemical shift is the most promising estimator of the acid-base and redox character.
Gamma band functional connectivity reduction in patients with amnestic mild cognitive impairment and epileptiform activity

Abstract There is growing evidence for neuronal hyperexcitability in Alzheimer’s disease. Hyperexcitability is associated with an increase in epileptiform activity and the disruption of inhibitory activity of interneurons. Interneurons fire at a high rate and are frequently associated with high-frequency oscillations in the gamma frequency band (30–150 Hz). It is unclear how hyperexcitability affects the organization of functional brain networks. A sample of 63 amnestic mild cognitive impairment patients underwent a magnetoencephalography resting-state recording with eyes closed. Twenty (31.75%) mild cognitive impairment patients had epileptiform activity. A cluster-based analysis of the magnetoencephalography functional connectivity revealed a region within the right temporal cortex whose global connectivity in the gamma frequency band was significantly reduced in patients with epileptiform activity relative to those without epileptiform activity. A subsequent seed-based analysis showed that this was largely due to weaker gamma band connectivity of this region with ipsilateral frontal and medial regions, and the upper precuneus area. In addition, this reduced functional connectivity was associated with higher grey matter atrophy across several cortical regions in the patients with epileptiform activity. These functional network disruptions and changes in brain physiology and morphology have important clinical implications as they may contribute to cognitive decline in mild cognitive impairment and Alzheimer’s disease.