Miracle-dz/UniFAR-FTUs huggingface dataset:
ConveRT for FAQ Answering

Knowledgeable FAQ chatbots are a valuable resource to any organization. Unlike traditional call centers or FAQ web pages, they provide instant responses and are always available. Our experience running a COVID19 chatbot revealed the lack of resources available for FAQ answering in non-English languages. While powerful and efficient retrieval-based models exist for English, it is rarely the case for other languages which do not have the same amount of training data available. In this work, we propose a novel pretaining procedure to adapt ConveRT, an English SOTA conversational agent, to other languages with less training data available. We apply it for the first time to the task of Dutch FAQ answering related to the COVID19 vaccine. We show it performs better than an open-source alternative in a low-data regime and high-data regime.
STABILITY AND INERTIA RESPONSE IMPROVEMENT OF BOOST CONVERTERS INTERLACED WITH CONSTANT POWER LOADS

In dc microgrids, the distributed sources and loads with different electrical characteristics are typically interconnected to the main bus through power electronic converters. The existence of interfaced converters creates two major problems: 1) the load-side converters and their associated loads, usually considered as constant power loads (CPLs), introduce destabilizing effects into the system; 2) the source-side converters do not possess any inertia or damping properties but reduce the overall inertia of the system. To overcome the stability problems caused by CPLs and low inertia, this paper proposes an active damping strategy based on a linear feedback. The proposed strategy measures the inductor current implemented in source-side boost converter in order to enhance the damping of the dc microgrids with CPLs. The control input-to-output voltage transfer function of a boost converter loaded with a CPL is inherently nonlinear, unstable and a non-minimum phase system that makes its control very difficult. In addition, the synthetic inertia of dc bus is enhanced by adding the virtual inertia control to the inner current control loop that is fast enough to emulate inertia and damping coefficient concept. In order to study the stability of dc MG with CPLs, a comprehensive small-signal model is derived and then, an acceptable range of inertia response parameters is determined by using the system’s root locus analysis. Performance of the proposed control structure is demonstrated through numerical simulations.
Hybrid Surgical and Endoscopic Transjejunal ERCP approach for Intra-Hepatic Biliary Stones Secondary to Recurrent Roux-En-Y Hepaticojejunal Anastomotic Strictures

Hepaticojejunal anastomotic stricture can result in intra-hepatic biliary stones and recurrent cholangitis. In the presence of altered small bowel anatomy, therapeutic ERCP has a low success rate. In patients with gastric bypass, there is increased expertise and efficiency in performing ERCP through transgastric access. Transjejunal access is an alternative in the absence of a gastric remnant. We present a case of a 30-years-old female with recurrent cholangitis and intra-hepatic biliary stones resulting from hepaticojejunal anastomotic stricture. The presence of severe adhesions between jejunal loops resulted in failure of double balloon enteroscopy assisted ERCP. A hybrid surgicalendoscopic approach via transjejunal enterotomy allowed access to hepaticojejunal anastomotic stricture which was dilated with through the scope balloon and subsequent removal of multiple biliary stones. A hybrid surgical-endoscopic transjejunal ERCP is a feasible approach after failure of other conventional endoscopic approaches to manage patients with hepaticojejunal anastomotic strictures.
The Refugee Crisis and Right-Wing Populism: Evidence from the Italian Dispersal Policy

RÉSUMÉ This paper examines how the 2014-2017 ‘refugee crisis’ in Italy affected voting behavior and the rise of right–wing populism in national Parliamentary elections. We collect unique administrative data and leverage exogenous variation in refugee resettlement across Italian municipalities induced by the Dispersal Policy. We find a positive and significant effect of the share of asylum seekers on support for radical-right anti-immigration parties. The effect is heterogeneous across municipality characteristics, yet robust to dispersal policy features. We provide causal evidence that the anti–immigration backlash is not rooted in adverse economic effects, while it is triggered by radical–right propaganda.
FPGA Implementation of Complex Floating Point Vedic Multiplier with Minimum Path Delay

: Digital media and signal processing applications are usually dominated by integer addition and multiplication. Hence, developing faster, smaller and power efficient Functional Units (FUs) is critical to implement efficient circuits. While partial carry-save adders are easily designed by splitting them into several fragments working in parallel, the design of partial carry-save multipliers is more challenging. Prior approaches have proposed several solutions based on the radix-4 recoding. This technique makes it possible to diminish the height of a multiplier by half, this being the most widespread option when designing multipliers, as only easy multiples are required. In the proposed system, design of low power 8 Bit VEDIC multiplier is being implemented using adjustable path selection routine that enable the Multiplier to save the carry path adjustable and finally combines both the result in the sum part. The proposed algorithm uses reconfigurable nature of VLSI systems.The proposed algorithm is executed in the Spartan-3E FPGA. The proposed structure utilizes low power and that would be compared in terms of power, path delay,efficiency. with array multiplier and existing multiplier.
Nanoparticles and their Potential affect as Antimicrobials in dentistry

The aim of the current article is to evaluate the antiadherent and antibacterial properties of surfacemodified different orthodontic brackets with silver nanoparticles against Streptococcus mutans and Streptococcus sobrinus, using radiomarker. To measure the current study, a quantitative method was applied to the adherence of Streptococci to orthodontic brackets, 70 samples of orthodontic brackets were selected and classified in to 10 groups as follow: GVn (Radiance plus-Roth), GVI (InVuRoth), GVII (SystemAlexanderLTS), GVIII (Gemini-Roth), GIX (NuEdge-Roth), GIIIn (Gemini-Roth), GIVn (NuEdge-Roth), GX (Radiance plus-Roth), GIn (InVu-Roth), and GIIn (SystemAlexanderLTS). The study focused on patients’ gender while considering sample for the present article. To codify and measure the bacterium, a radioactive marker (3 H) was applied. The brackets were then immersed in a radiolabeled solution, and the radiation was measured. The ANOVA test (Sheffè post hoc) was used to calculate the statistical analysis. The findings revealed that there were substantial disparities between the groups. GIIIn was the lowest score for both bacteria, whereas GIX and GVI were the highest for Streptococcus mutans and Streptococcus sobrinus, respectively. In conclusion, the surface of orthodontic brackets can be modified with silver nanoparticles to reduce the accumulation of dental plaque and the development of dental caries during orthodontic treatment. Keywords— Silver nanoparticles, Orthodontic brackets, White spot lesion, Dentistry
Short Communication Pathway Analysis of Single-Nucleotide Polymorphisms Potentially Associated with Glioblastoma Multiforme Susceptibility Using Random Forests

Glioma is a complex disease that is unlikely to result from the effect of a single gene. Genetic analysis at the pathway level involving multiple genes may be more likely to capture gene-disease associations than analyzing genes one at a time. The current pilot study included 112 Caucasians with glioblastoma multiforme and 112 Caucasian healthy controls frequency matched to cases by age and gender. Subjects were genotyped using a commercially available (ParAllele/Affymetrix) assay panel of 10,177 nonsynonymous coding singlenucleotide polymorphisms (SNP) spanning the genome known at the time the panel was constructed. For this analysis, we selected 10 pathways potentially involved in gliomagenesis that had SNPs represented on the panel. We performed random forests (RF) analyses of SNPs within each pathway group and logistic regression to assess interaction among genes in the one pathway for which the RF prediction error was better than chance and the permutation P < 0.10. Only the DNA repair pathway had a better than chance classification of case-control status with a prediction error of 45.5% and P = 0.09. Three SNPs (rs1047840 of EXO1, rs12450550 of EME1, and rs799917 of BRCA1) of the DNA repair pathway were identified aspromising candidates for further replication. In addition, statistically significant interactions (P < 0.05) between rs1047840 of EXO1 and rs799917 or rs1799966 of BRCA1 were observed. Despite less than complete inclusion of genes and SNPs relevant to glioma and a small sample size, RF analysis identified one important biological pathway and several SNPs potentially associated with the development of glioblastoma. (Cancer Epidemiol Biomarkers Prev 2008;17(6):1368–73)
In situ ruminal degradability and fermentation characteristics of novel mixtures of winter cereal and Italian ryegrass plus winter cereal silages

This study was conducted using three multiparous non-lactating rumen-cannulated Holstein-Friesian dairy cows, with the objective of evaluating the in situ ruminal degradability and fermentation characteristics of novel mixtures of winter cereal and Italian ryegrass (Lolium multiflorum Lam.) plus winter cereal silages (mixture A: triticale, oats, barley and wheat; mixture B: triticale, barley and wheat; mixture C: Italian ryegrass and oats; mixture D: Italian ryegrass, oats, triticale, barley and wheat). The rumen fermentation study was conducted replacing the ensiled mixtures (experimental diets) with vetch-triticale haylage in a total mixed ration (control diet). It was found that the effective protein degradability at 0.08 rumen outflow rates was 80.6% (mixture A), 66.2% (mixture B), 79.7% (mixture C) and 79.3% (mixture D). The effective neutral detergent fibre (NDF) and acid detergent fibre (ADF) effective degradability at 0.08 rumen outflow rates was 18.0% and 17.7% (mixture A), 19.7% and 20.5% (mixture B), 19.1% and 17.0% (mixture C), and 15.2% and 14.6% (mixture D), respectively. Different dietary treatments did not change (P > 0.05) the rumen fermentation characteristics as there was no difference (P > 0.05) between control and experimental diets, and the inclusion of 40–55% Italian ryegrass (mixture C and D) did not cause any difference. These results suggest that the mixture of winter cereals and Italian ryegrass plus winter cereal-based silages had good potentially degradable dry matter, effective dry matter and effective protein degradability at 0.01, 0.05 and 0.08 rumen outflow rates without affecting the rumen environment maintaining neutral pH. The ensiled mixtures had a moderate level of potentially degradable NDF and ADF fraction.
E XTRAPOLATABLE R ELATIONAL R EASONING W ITH C OMPARATORS IN L OW -D IMENSIONAL M ANIFOLDS

While modern deep neural architectures generalise well when test data is sampled from the same distribution as training data, they fail badly for cases when the test data distribution differs from the training distribution even along a few dimensions. This lack of out-of-distribution generalisation is increasingly manifested when the tasks become more abstract and complex, such as in relational reasoning. In this paper we propose a neuroscience-inspired inductively biased module that can be readily amalgamated with current neural network architectures to improve outof-distribution (o.o.d) generalisation performance on relational reasoning tasks. This module learns to project high-dimensional object representations to lowdimensional manifolds for more efficient and generalisable relational comparisons. We show that neural nets with this inductive bias achieve considerably better o.o. d generalisation performance for a range of relational reasoning tasks, thus more closely models human ability to generalise even when no previous examples from that domain exist. Finally, we analyse the proposed inductive bias module to understand the importance of lower dimensional projection, and propose an augmentation to the algorithmic alignment theory to better measure algorithmic alignment with generalisation.
Climate-driven desertification and its implications for the ancient Silk Road trade

Abstract. The ancient Silk Road played a crucial role in cultural exchange and commercial trade between western and eastern Eurasia during the historical period. However, the exchanges were interrupted in the early 16th century CE, during the Ming dynasty. Various causes for the decline of the ancient Silk Road have been suggested. Unlike social factors, natural factors have not been adequately addressed. In this study, we use evidence from a sedimentary site (Xishawo, XSW) in Dunhuang oasis, together with analysis of historical archives, to demonstrate the occurrence of extreme droughts and desertification events in the Dunhuang area post ∼ 1450 CE, which persisted at least for decades. The desertification may be closely associated with the accessibility of the ancient Silk Road in the area, which was responsible for a steep fall in the volume of trade, as well as political chaos and mass migrations. Therefore, besides socio-economic factors, climate change may have played an important role in trade exchange between the Ming government and the West and may have even influenced the rise and decline of the ancient Silk Road.
An Analysis Design for Betel-Nut Business by Using Unified Modeling Language (UML)

This system made for the convenience of the seller (owner) and buyer (customer) in betel-nut business by using UML. UML is a standard way to draw different perspectives of software andbecoming more valuable in the market.In the market competition aggravating, it becomes necessary for market players to adopt a business model which can adopt dynamic business changes. This paper presents designing for Betel-Nut Business by using the StarUML software (version 5.0) to draw the diagrams. Requirements of the system are analyzed by Use Case diagram and are designed by Class diagram to generate codes. This system can be used in any Betel-Nut business start-up, especially any business that uses UML tools including use case and class diagram.
Automatic Fire Detection and Control Using Solar Panel

All agricultural farms are at risk of fire and each year there are over one thousand fires in agricultural buildings, many of them housing livestock. Most fires on farms can be prevented by providing the farmers and stockpersons a timely alert and a mechanism to recognize and remedy any hazards which do occur. This project examines the fire dangers which are commonly found on farms and proposes a simple sensor based system in which risks can be reduced by timely alert and taking sensible measures. Although principally concerned with farm animal welfare, such measures are equally important to human safety. In this project basically a low cost fire detection and control system based on heat detection is proposed. It is comprised of a combination of electrical/electronic devices/equipment working together to detect the presence of fire and alert people through audio or visual medium after detection. The whole system is powered by using solar panels. These system may be activated from heat detectors which, when detects fire. Then, it automatically operates a relay which can be used to send Short Message Service (SMS) to the registered mobile numbers and switch on a motor pump to spray water using sprinkler irrigation system. Fire outbreak is a major concern at homes, offices, industries etc. It is dangerous and requires high security and control to avoid destruction of lives and property. One of the preventive measures to avoid the danger is to install an automatic fire detector at vulnerable locations, hence the Arduino based fire alarm detection and control system was proposed. It is capable of automatically detecting heat in a given environment, displaying on LCD, and switch on the motor pump automatically and also spray water to reduce the intensity of fire.
Detailed overview of the phenotyping program

Metabolic function · Air displacement plethysmography (BodPod) x □ □ □ □ · Advanced Glycylation Endproducts (AGE) x □ □ □ □ · Indirect Calorimetry x □ □ □ □ · Meal load vs. physical challenge (randomized 1:1) x □ □ □ □ · 14d continuous glucose monitoring x □ □ □ □ □ · Mediterranean Diet Score x □ □ □ □ · Three Factor Eating Questionnaire (TFQ) x □ □ □ □ · Food Frequency Questionnaire (FFQ) x □ □ □ □
Towards an eﬃcient evaluation method of generative theories using gradient data and regression analysis

With Linear Optimality Theory (LOT), Keller (2000) introduced a framework that allows to account for gradient acceptability of syntactic structures. Each factor that is relevant for the acceptability of a sentence is formulated as an Optimality Theoretical constraint associated with a numerical weight. The sum of weights of all constraints that a given sentence violates then maps directly to its grammaticality, which is assumed to be measurable directly in terms of acceptability. The severity of the constraint is estimated by a statistical algorithm. Like Standard Optimality Theory (Prince & Smolensky 1993), LOT also involves a generating and an evaluating component. Keller provides a detailed proposal for the latter and leaves the former unspecified, except for the statement that the generating component must be able to produce permutations of constituents. It is thus tempting to combine LOT with an explicit theory of grammatical structure-building, such as the Minimalist Framework (Chomsky 1995 and further work), to attain a more comprehensive theory as well as a way to test specific Minimalist proposals empirically. However, some non-trivial issues arise with this attempt. One of them is that it is a core assumption in Keller’s work that grammaticality is gradient. In contrast, in Minimalism (and more generally, in generative grammatical models), by design, there is a categorical distinction between structures that can be derived by the set of structure-building operations/rules, and those that cannot be derived. I want to argue that this issue can be resolved in the following way. Sentences that are derivable by a Minimalist grammar and are therefore grammatical by definition within that framework can still involve interpretative, prosodic, or processing-related problems to a varying extent. If a theory predicts such a problem for a specific type of structure, a systematic acceptability decrease should be found for it consistently across the data. Under this view, it is possible to conceptualize a generative theory in terms of weighted constraints, and to make it compatible with an LOT-inspired empirical evaluation: A constraint weight estimation algorithm (e.g. multiple regression) can be used to estimate the severity of the constraint violation and to evaluate how consistently the acceptability decrease is really found where the theory predicts it to occur. In the talk, I will show in detail how to apply the idea to a data set of gradient acceptability judgments concerning word order variation in Czech. In sum, by establishing a link between generative grammar and the LOT framework, I hope to provide a powerful evaluation method of generative linguistic theories which can be used for a broad range of approaches.
A Deep Value-based Policy Search Approach for Real-world Vehicle Repositioning on Mobility-on-Demand Platforms

We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (also known as mobility-on-demand, MoD) platforms. Our approach learns the driver-perspective state-value function using a batch training algorithm with spa-tiotemproal deep value networks. The optimal repositioning action is generated on-demand through value-based policy search, which combines planning and boot-strapping with the value networks. We benchmark our algorithm with baselines in an MoD simulation environment to demonstrate its superiority in improving the income rate. We have also designed and run a real-world experiment program with regular drivers on a major MoD platform. We have observed positive results on key metrics comparing our method with a group drivers who performed idle-time repositioning based on their own experience and knowledge.
Erythrocyte Aggregability Enables the Distinction between Negative and Depressive Symptoms among Schizophrenia and Schizoaffective Disorder Patients

Objective: Based on the membrane-phospholipid hypothesis of schizophrenia, the authors tested the assumption that erythrocyte aggregation is differentially associated with negative and depressive symptoms of schizophrenia and schizoaffective disorder. Methods: A cell flow properties analyzer was used to measure erythrocyte aggregation levels in 68 in patients with schizophrenia and schizoaffective disorder between ages 18 and 60 years and 30 normal comparison subjects without known neuropsychiatric disorders, proportionally matched for age and gender. Positive, negative and general psychopathological symptoms were quantified with the Positive and Negative Syndrome Scale (PANSS). Multiple regression analysis was used to examine the association of erythrocyte aggregation with clinical symptoms. Results: There were no significant differences in erythrocyte aggregation levels between schizophrenic and schizoaffective disorder patients and normal control subjects. The erythrocyte aggregability directly and strongly correlated with the severity of negative syndrome, but inversely with affective components of the disorders. These findings were unrelated to gender, age at testing, age at onset and duration of the illnesses, body mass index, serum cholesterol and fibrinogen levels, smoking, and current medication. Conclusion: The findings provide evidence that erythrocyte aggregation may serve as a potential endophenotype marker to distinguish negative and depressive features in schizophrenia and schizoaffective disorder patients.