Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving S&P stock prediction with time series stock similarity

Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock. In this paper, we enriched the stock data with related stocks just as a professional trader would have done to improve the stock prediction models. We tested five different similarities functions and found co-integration similarity to have the best improvement on the prediction model. We evaluate the models on seven S&P stocks from various industries over five years period. The prediction model we trained on similar stocks had significantly better results with 0.55 mean accuracy, and 19.782 profit compare to the state of the art model with an accuracy of 0.52 and profit of 6.6.
Harvesting Ambient RF for Presence Detection Through Deep Learning

This article explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using Wi-Fi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious preprocessing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments, such as timing or frequency offset. Addressing these challenges, the proposed learning system uses preprocessing to preserve human motion-induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf Wi-Fi devices, the proposed deep-learning-based RF sensing achieves near-perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. A comparison with existing RF-based human presence detection also demonstrates its robustness in performance, especially when deployed in a completely new environment. The learning-based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.
What is the function of inter-hemispheric inhibition?.

It is widely supposed that following unilateral brain injury, there arises an asymmetry in inter-hemispheric inhibition which has an adverse influence upon motor control. I argue that this 'inter-hemispheric imbalance' model arises from a fundamental misunderstanding of the roles played by inter-hemispheric (callosal) projections in mammalian brains. Drawing upon a large body of empirical data, derived largely from animal models, and associated theoretical modeling, it is demonstrated that inter-hemispheric projections perform contrast enhancing and integrative functions via mechanisms such as surround/lateral inhibition. The principal functional unit of callosal influence comprises a facilitatory centre and a depressing peripheral zone, that together shape the influence of converging inputs to pyramidal neurons. Inter-hemispheric inhibition is an instance of a more general feature of mammalian neural systems, whereby inhibitory interneurons act not simply to prevent over-excitation but to sculpt the output of specific circuits. The narrowing of the excitatory focus that occurs through crossed surround inhibition is a highly conserved motif of transcallosal interactions in mammalian sensory and motor cortices. A case is presented that the notion of 'inter-hemispheric imbalance' has been sustained, and clinical interventions derived from this model promoted, by erroneous assumptions concerning that revealed by investigative techniques such as transcranial magnetic stimulation (TMS). The alternative perspective promoted by the present analysis, also permits the basis of positive (e.g. post stroke) associations between the structural integrity of transcallosal projections and motor capability to be better understood.
Using Simulation to Analyze Interrupted Time Series Designs

We are sometimes forced to use the Interrupted Time Series (ITS) design as an identification strategy for potential policy change, such as when we only have a single treated unit and cannot obtain comparable controls. For example, with recent county- and state-wide criminal justice reform efforts, where judicial bodies have changed bail setting practices for everyone in their jurisdiction in order to reduce rates of pre-trial detention while maintaining court order and public safety, we have no natural and available comparison group other than the past. In these contexts, it is imperative to model pre-policy trends with a light touch, allowing for structures such as autoregressive departures from any pre-existing trend, in order to accurately and realistically assess the uncertainty of our projections. We aim to provide a methodological approach rooted in commonly understood and used modeling tools to achieve this. We quantify uncertainty with simulation, generating a distribution of plausible counterfactual trajectories to compare to the observed; this approach naturally allows for incorporating seasonality and other time-varying covariates, and provides confidence intervals along with point estimates for the potential impacts of policy change. We find simulation provides a natural framework to capture and show uncertainty in the ITS designs. It also allows for easy extensions such as nonparametric smoothing in order to handle multiple post-policy time points.
Compensation of Linear Attacks to Cyber Physical Systems through ARX System Identification

Cyber-Physical Systems (CPSs) are vastly used in today's cities critical infrastructure. The cyber part of these systems usually has a network component through which cyber attacks can be launched. In this paper, we first design an intrusion detection system (IDS) by identifying the plant. We assume the initial operation period of the CPS is attack-free and learn the plant model. Then, we compare the expected output found via the identifier with the real one coming through the feedback link. Any difference greater than a threshold is deemed to be an anomaly. To compensate, once the IDS flags a change in the loop, we restart the system identification to find the new transfer function. With the estimation of the new transfer function at hand, a new controller is designed to keep the system stable. To test the idea, we took a DC motor as the plant and employed ARX identifier. MATLAB Simulink environment was used to test the proposed intrusion detection and compensation framework. We applied a set of deception attacks to the forward channel in our experiments. The obtained results prove that our detection strategy works well and timely reacts to anomalies. Moreover, they show that the compensation strategy is also effective and keeps the system stable under such attacks.
A Data Efficient End-to-End Spoken Language Understanding Architecture

End-to-end architectures have been recently proposed for spoken language understanding (SLU) and semantic parsing. Based on a large amount of data, those models learn jointly acoustic and linguistic-sequential features. Such architectures give very good results in the context of domain, intent and slot detection, their application in a more complex semantic chunking and tagging task is less easy. For that, in many cases, models are combined with an external a language model to enhance their performance.In this paper we introduce a data efficient system which is trained end-to-end, with no additional, pre-trained external module. One key feature of our approach is an incremental training procedure where acoustic, language and semantic models are trained sequentially one after the other. The proposed model has a reasonable size and achieves competitive results with respect to state-of-the-art while using a small training dataset. In particular, we reach 24.02% Concept Error Rate (CER) on MEDIA/test while training on MEDIA/train without any additional data.
A Prompt Report on the Performance of Intel Optane DC Persistent Memory Module

In this prompt report, we present the basic performance evaluation of Intel Optane Data Center Persistent Memory Module (Optane DCPMM), which is the first commercially-available, byte-addressable non-volatile memory modules released in April 2019. Since at the moment of writing only a few reports on its performance were published, this letter is intended to complement other performance studies. Through experiments using our own measurement tools, we obtained that the latency of random read-only access was approximately 374 ns. That of random writeback-involving access was 391 ns. The bandwidths of read-only and writeback-involving access for interleaved memory modules were approximately 38 GB/s and 3 GB/s, respectively.
A Numerical Model for the Multiwavelength Lightcurves of PSR J0030+0451

Recent modeling of Neutron Star Interior Composition Explorer (NICER) observations of the millisecond pulsar PSR J0030+0451 suggests that the magnetic field of the pulsar is non-dipolar. We construct a magnetic field configuration where foot points of the open field lines closely resemble the hotspot configuration from NICER observations. Using this magnetic field as input, we perform force-free simulations of the magnetosphere of PSR J0030+0451, showing the three-dimensional structure of its plasma-filled magnetosphere. Making simple and physically motivated assumptions about the emitting regions, we are able to construct the multiwavelength lightcurves that qualitatively agree with the corresponding observations. The agreement suggests that multipole magnetic structures are the key to modeling this type of pulsar, and can be used to constrain the magnetic inclination angle and the location of radio emission.
Current and future development of the photoionization code Cloudy

The gas that is present in the interstellar medium is usually very far removed from (local) thermodynamic equilibrium, and in some cases may also not be in a steady-state equilibrium with its surroundings. The physics of this material is complex and one needs a sophisticated numerical code to study it. For this purpose the open-source photoionization code Cloudy was created. It models the physical state of the gas and predicts the spectrum that it emits. Cloudy is continually being developed to improve the treatment of the microphysical processes and the database of fundamental data that it uses. In this paper we will discuss how we are developing the code to improve our high-density predictions by implementing better collisional-radiative models for all ions. We will also briefly discuss the experimental mode in Cloudy to model gas that is not in steady-state equilibrium and present a preliminary model of recombining gas in a planetary nebula that is on the cooling track. We finish with a short discussion of how we are speeding up the code by using parallelization.
Economic complexity of prefectures in Japan

Every nation prioritizes the inclusive economic growth and development of all regions. However, we observe that economic activities are clustered in space, which results in a disparity in per-capita income among different regions. A complexity-based method was proposed by Hidalgo and Hausmann [PNAS 106, 10570-10575 (2009)] to explain the large gaps in per-capita income across countries. Although there have been extensive studies on countries’ economic complexity using international export data, studies on economic complexity at the regional level are relatively less studied. Here, we study the industrial sector complexity of prefectures in Japan based on the basic information of more than one million firms. We aggregate the data as a bipartite network of prefectures and industrial sectors. We decompose the bipartite network as a prefecture-prefecture network and sector-sector network, which reveals the relationships among them. Similarities among the prefectures and among the sectors are measured using a metric. From these similarity matrices, we cluster the prefectures and sectors using the minimal spanning tree technique. The computed economic complexity index from the structure of the bipartite network shows a high correlation with macroeconomic indicators, such as per-capita gross prefectural product and prefectural income per person. We argue that this index reflects the present economic performance and hidden potential of the prefectures for future growth.
An Unusual MHC Molecule Generates Protective CD8+ T Cell Responses to Chronic Infection

The CD8+ T cell response to the intracellular parasite Toxoplasma gondii varies dramatically between mouse strains, resulting in differences in control of the parasite. Protection in BALB/c mice can be attributed to an unusually strong and protective MHC-1 Ld-restricted CD8+ T cell response directed against a peptide derived from the parasite antigen GRA6. The MHC-1 Ld molecule has limited peptide binding compared to conventional MHC molecules such as Kb or Db, which correlates with polymorphisms associated with “elite control” of HIV in humans. To investigate the link between the unusual MHC-1 molecule Ld and the generation of “elite controller” CD8+ T cell responses, we compared the GRA6-Ld specific T cell response to the well-studied OVA-Kb specific response, and demonstrated that GRA6-Ld specific T cells are significantly more protective and resistant to exhaustion in chronic T. gondii infection. To further investigate the connection between limited peptide presentation and robust T cell responses, we used CRISPR/Cas9 to generate mice with a point mutation (W97R) in the peptide-binding groove of Ld that results in broader peptide binding. We investigated the effect of this Ld W97R mutation on another robust Ld-restricted response against the IE1 peptide during Murine Cytomegalovirus (MCMV) infection. This mutation leads to an increase in exhaustion markers in the IE1-Ld specific CD8+ T cell response. Our results indicate that limited peptide binding by MHC-1 Ld correlates with the development of robust and protective CD8+ T cell responses that may avoid exhaustion during chronic infection.
High-pressure silica phase transitions: Implications for deep mantle dynamics and silica crystallization in the protocore

Abstract The subsolidus phase diagram of silica in the 80–220 GPa pressure range was determined by density functional theory (DFT). The transition pressures calculated using the generalized gradient approximation (GGA) in the static limit (at 0 K, without zero point vibrational energy) for the β-stishovite (CaCl2- structure) to seifertite and the seifertite to pyrite-type transitions are 95 and 213 GPa, respectively. These are in good agreement with those calculated using hybrid functionals, giving transition pressures of 96 and 215 GPa. This indicates that previous local density approximation (LDA) results underestimate the transition pressure by 10–15 GPa. Density functional perturbation theory calculations, carried out using GGA within the quasi-harmonic approximations, give Clapeyron slopes of 5.4 and –2.8 MPa/K for the β-stishovite to seifertite and seifertite to pyrite-type transitions, respectively. This suggests that the seifertite-forming transition occurs at 109 GPa (470 km above the core-mantle boundary, CMB) at an ambient mantle geotherm, whereas the pyrite-type transition occurs at 200 GPa (620 km below the CMB) at 4700 K, which is close to the core adiabat. We also calculate the equation of state and show that the stability of seifertite in the lowermost mantle contributes negative buoyancy to recycled oceanic crust, although not as much as in some previous studies. Nevertheless, the increased density of seifertite over β-stishovite may lead to layers with elevated proportions of basaltic material within the large low S-wave velocity provinces. The seifertite to pyrite-type silica transition in the outer core will affect the silica liquidus surface in the system Fe-Si-O and forms a basis for further investigations of silica crystallization in the protocore.
Characterization of Upper Limb Impairments at Body Function, Activity, and Participation in Persons With Multiple Sclerosis by Behavioral and EMG Assessment: A Cross-Sectional Study

Background: Multiple sclerosis (MS) is a chronic inflammatory demyelinating and disabling disease which primarily affects individuals in their early life between 20 and 40 years of age. MS is a complex condition, which may lead to a variety of upper limb (UL) dysfunctions and functional deficits. Objective: To explore upper limb impairments at body function, activity, and participation in persons with MS (PwMS) and severe hand dexterity impairment by behavioral and surface electromyography (sEMG) assessments. Methods: This observational cross-sectional study involved 41 PwMS with severe hand dexterity impairment stratified according to the Expanded Disability Status Scale (EDSS) into mild–moderate (n = 17; EDSS, 1–5.5), severe ambulant (n = 15; EDSS, 6–6.5), and severe nonambulant (n = 9; EDSS, 7–9.5). Behavioral outcome measures exploring body function, activity, and participation were administered. The sEMG activity of six upper limb muscles of the most affected side was measured during a reaching task. Results: The most severe group was significantly older and more affected by secondary progressive MS than the other two groups. Positive significant associations between UL deterioration and impairments at different International Classification of Functioning, Disability, and Health domains were noted in the most severe group. The progressive decline in manual dexterity was moderately to strongly associated with the deterioration of the overall UL activity (ρ = 0.72; p < 0.001) and disuse (amount of use ρ = 0.71; p < 0.001; quality of movement ρ = 0.77; p < 0.001). There was a low correlation between manual dexterity and UL function (ρ = 0.33; p = 0.03). The muscle activation pattern investigated by sEMG was characterized by a decrease in modularity and timing delay in the wrist extensor muscles activation in the severe ambulant patients (EDSS, 6–6.5). Similar impairments were observed in the proximal muscles (anterior deltoid) in the more advanced stages (EDSS ≥ 7). Conclusion: Behavioral assessment, together with measures of muscle activation patterns, allows investigating the pathophysiology of UL impairments in PwMS across progressive neurological disability severity to implement task-specific rehabilitation interventions.
The Irony of Humanization: Alemtuzumab, the First, But One of the Most Immunogenic, Humanized Monoclonal Antibodies

Alemtuzumab was designed to reduce the immunogenicity of the parent CD52-specific rat immunoglobulin. Although originally marketed for use in cancer (Mabcampath®), alemtuzumab is currently licensed and formulated for the treatment of relapsing multiple sclerosis (Lemtrada®). Perhaps due to its history as the first humanized antibody, the potential of immunogenicity of the molecule has been considered inconsequential, and anti-drug antibodies (ADA) responses were similarly reported as being clinically insignificant. Nonetheless, despite humanization and depletion of peripheral T and B cells, alemtuzumab probably generates the highest frequency of binding and neutralizing ADA of all humanized antibodies currently in clinical use, and they occur rapidly in a large majority of people with MS (pwMS) on alemtuzumab treatment. These ADA appear to be an inherent issue of the biology of the molecule—and more importantly, the target—such that avoidance of immunogenicity-related effects has been facilitated by the dosing schedule used in clinical practice. At the population level this enables the drug to work in most pwMS, but in some individuals, as we show here, antibody neutralization appears to be sufficiently severe to reduce efficacy and allow disease breakthrough. It is therefore imperative that efficacy of lymphocyte depletion and the anti-drug response is monitored in people requiring additional cycles of treatment, notably following disease breakthrough. This may help inform whether to re-treat or to switch to another disease-modifying treatment.
Public Knowledge, Attitude, and Practice on Herbal Remedies Used During Pregnancy and Lactation in West Bank Palestine

The use of herbal products by pregnant and lactating mothers without awareness of their harmful effects may expose both mother and fetus or infant to great dangers, such as abortion, premature delivery, uterine bleeding, and physical and mental retardation of the fetus. Thus, the aim of this study was to investigate the extent to which herbal product treatment is used and the reason for which such products are used and to ensure that these reasons are correct. An ethnopharmacological survey (cross-sectional observational design study) using a pre-piloted questionnaire was undertaken on herbal products used by pregnant and lactating women in the West Bank area of Palestine. A questionnaire was distributed to 350 pregnant and lactating women. The informed consent forms, ethics, and aims of the present study were reviewed and approved by the Institutional Review Board (IRB) at An-Najah National University. To identify the most important species used, the use value (UV) index was employed, while the SPSS program was used to analyze the data. Collected data revealed that 13 medicinal plants are utilized, while 12 plants are not used during pregnancy. Moreover, 15 plants are utilized and 9 plants are not used during lactation for treating and dealing with various problems. The most commonly used plants belonged to 14 families, including Lamiaceae, Apiaceae, Leguminosae, and Rubiaceae. The plants most used during pregnancy were sage (Salvia fruticosa), anise (Pimpinella anisum), and peppermint (Mentha × piperita). Castor (Ricinus communis) oil, ginger (Zingiber officinale), saffron (Crocus sativus), and senna (Senna alexandrina) mostly were not used by pregnant women. Moreover, cinnamon (Cinnamomum verum), anise (P. anisum), peppermint (M. piperita), and sage (S. fruticosa) were mostly used during lactation. Castor (R. communis) oil, ginger (Z. officinale), garlic (Allium sativum), and aloe (Aloe vera) mostly were not used during lactation. This study is of great importance in order to decrease the possibility of endangering the lives of fetuses and infants. A combined effort among researchers, scientists, lactating women, and pregnant women may help in changing wrong uses and thoughts about medicinal plants and help to improve the overall health of both mother and fetus.