Miracle-dz/UniFAR-FTUs huggingface dataset:
Macroeconomic and Financial Interactions in Chile: An Estimated DSGE Approach ∗

We set up and estimate a DSGE model of a small open economy that features two types of financial frictions: one in the relationship between depositors and banks (following Gertler and Karadi, 2011) and the other between banks and borrowers (along the lines of Bernanke et al., 1999). We use Chilean data to estimate the model, following a Bayesian approach. The model is used to understand the interaction between macroeconomic and financial factors. In particular, we try to answer the following questions: Is the presence of financial frictions useful in matching the dynamics of observed non-financial variables? Which of the financial frictions is more relevant in explaining the behavior of both financial and non-financial variables? How does the presence of financial frictions change the propagation of the main driving forces of the economy? Additionally, we perform an exercise to asses the benefits of “leaning against the wind” policies (i.e. using the monetary policy rate to smooth the credit cycle). We thank Anella Munro, Claudio Soto, Enrique Mendoza, Gianluca Benigno, Claudio Raddatz and participants at the 17th Annual Conference of the Central Bank of Chile, the First Conference of the BIS CCA Research Network on “Incorporating Financial Stability Considerations into Central Banks Policy Models”, and the Fifth BIS CCA Research Conference “Challenges from Changing International Financial Conditions” for comments. The views and conclusions presented in this paper are exclusively those of the authors and do not necessarily reflect the position of the Central Bank of Chile or its Board members.
Comparison of Fast-Reroute Mechanisms for BIER-Based IP Multicast

IP multicast (IPMC) delivers one-to-many traffic along distribution trees. To that end, conventional IPMC requires state in forwarding devices for each IPMC group. This limits scalability of IPMC because forwarding state in core devices may be extensive and updates are necessary when IPMC groups or the topology change. The IETF introduced Bit Index Explicit Replication (BIER) for efficient transport of IPMC traffic. BIER leverages a BIER header and IPMC-group-independent forwarding tables for forwarding of IPMC packets in a BIER domain. However, legacy devices do not support BIER. In contrary, two SDN-based implementations for OpenFlow an P4 have been published recently. In this paper, we assess BIER forwarding which may be affected by network failures. So far there is no standardized procedure to handle such situations. Two concepts have been proposed. The first approach is based on Loop- Free Alternates. It reroutes traffic to suitable neighbors in the BIER domain to steer traffic around the failure. The second approach is a tunnel-based mechanism that tunnels BIER packets to appropriate downstream nodes within the BIER distribution tree. We explain and compare both approaches, and discuss their advantages and disadvantages.
REAL-TIME FINGER SPELLING AMERICAN SIGN LANGUAGE RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORKS

This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of four convolutional neural networks are trained with a dataset of 25254 images for gesture recognition and a feedback unit called head pose estimation is implemented to validate the correctness of predicted gestures. This entire system was developed using Python programming language and other supporting libraries like OpenCV, Tensor flow and Dlib to perform various image processing and machine learning tasks. This entire application can be deployed as a web application using Flask to make it operating system independent.
Quantifying TPC-H choke points and their optimizations

TPC-H continues to be the most widely used benchmark for relational OLAP systems. It poses a number of challenges, also known as "choke points", which database systems have to solve in order to achieve good benchmark results. Examples include joins across multiple tables, correlated subqueries, and correlations within the TPC-H data set. Knowing the impact of such optimizations helps in developing optimizers as well as in interpreting TPC-H results across database systems. This paper provides a systematic analysis of choke points and their optimizations. It complements previous work on TPC-H choke points by providing a quantitative discussion of their relevance. It focuses on eleven choke points where the optimizations are beneficial independently of the database system. Of these, the flattening of subqueries and the placement of predicates have the biggest impact. Three queries (Q2, Q17, and Q21) are strongly influenced by the choice of an efficient query plan; three others (Q1, Q13, and Q18) are less influenced by plan optimizations and more dependent on an efficient execution engine.
Storing Digital Data Into DNA: A Comparative Study Of Quaternary Code Construction

The exponential increase of digital data that is being generated every year along with the capacity and durability limits of conventional storage devices are raising one of the greatest challenges for the field of data storage. The use of DNA for digital data archiving is a very promising alternative as the biological properties of the DNA molecule allow the storage of a huge amount of information into a very limited volume while also promising data longevity for centuries or even longer. In this paper we present a comparative study of our work with the state of the art solutions, and show that our solution is competitive.
Screening and comparative study of in vitro antioxidant and antimicrobial activities of ethanolic extracts of selected Vietnamese plants

ABSTRACT This study aimed to screen the in vitro antioxidant and antimicrobial activities of ethanolic extracts from 20 plants and three herbal commercial products empirically used for aquaculture improvement in Vietnam. The results of 2,2-diphenyl-1-pycrylhydrazyl (DPPH) radical scavenging assays showed that Phyllanthus amarus extract was the strongest antioxidant, followed by four extracts in the subsequent order: Piper betle > Psidium guajava > Euphorbia hirta > Mimosa pudica. These five plant extracts were very active in a DPPH radical scavenging assay with concentrations needed to scavenge half of the DPPH (IC50) below 30 µg/mL. Seven plant extracts showed an IC50 ranging from 31.9 to 59.7 µg/mL, while eleven extracts showed an IC50 above 70 µg/mL. A positive association was found between phenolic content (expressed as gallic acid equivalents) and antioxidant activity of the plant extracts. Concerning in vitro antimicrobial activities, P. amarus extract showed the highest activity against two different strains of Aeromonas hydrophila as demonstrated by its low minimal inhibitory concentration (MIC) of 156 and 625 µg/mL, respectively; whereas, P. betle displayed a moderate activity against Edwardsiella ictaluri with a MIC value of 625 µg/mL. Tannins were observed as significant factors contributing to antioxidant and antimicrobial properties of the plant extracts tested.
Improving quality and performance of schema matching in large scale

Schema matching is a crucial task to gather information of th e same domain. How- ever, this process is still largely performed manually or se mi-automatically, discouraging the deployment of large-scale mediation systems. Indeed, thes e large-scale scenarii need a solution which ensures both an acceptable matching quality and good p erformance. In this article, we present the BMatch approach to efficiently match a large numb er of schemas. The quality aspect is based on the combination of terminological and contextua l methods. The performance as- pect relies on a B-tree indexing structure to reduce the sear ch space. Finally, experiments with real sets of schemas show that our approach is scalable and ou tperforms the most referenced matching tools both in quality of matches and performance ti RESUME.La decouverte de correspondances entre schemas est une etap e importante lorsque l'on integre des informations d'un meme domaine. Cependant, ce p est encore trop souvent effectue manuellement ou au moyen d'approches semi-automa tiques. Notre approche Bmatch s'appuie sur une combinaison de mesures terminologiques etd'informations contextuelles pour decouvrir des correspondances entre schemas. Par ailleurs , pour etre efficace dans un contexte large echelle, nous nous appuyons sur une structure d'index ation B-tree pour reduire l'espace de recherche. Des experimentations sur des donnees reelles montrent que notre approche passe bien a l'echelle tout en obtenant globalement une meilleurequalite et de meilleures perfor- mances comparativement aux outils de decouverte de corresp ondances de reference.
RESEARCHES ABOUT THE TEMPERATURE OF THE CUTTING EDGE IN TURNING OF UNALLOYED STEEL

Lucrarea prezintă rezultatele cercetărilor autorilor asupra temperaturii muchiei aşchietoare a sculei la prelucrarea oţelurilor de calitate, în funcţie de parametrii procesului de aşchiere cu valori convenţionale. Pentru determinarea valorilor de temperatură ale muchiei aşchietoare s-au utilizat montaje experimentale cu termocupluri naturale şi artificiale, cameră de luat vederi în infraroşu şi pirometru optic. Pentru analiza influenţei variaţei parametrilor de aşchiere asupra temperaturii muchiei aşchietoare s-a utilizat o relaţie analitică determinată prin metoda planelor simetrice de măsurare şi a regresiei matematice.
Biomedical data analysis in translational research: integration of expert knowledge and interpretable models

In various ﬁelds of biomedical research, the availability of electronic data has increased tremendously. Not only is the amount of disease speciﬁc data increasing, but so is its structural complexity in terms of dimensionality, multi-modality and inhomogeneity. Consequently, there is an urgent need for better coordination between bio-medical and computational researchers in order to make an impact on patient care. In any such eﬀort, the integration of expert knowledge is essential. A careful synthesis of good analytical techniques applied to relevant medical questions would make the analysis both accurate and interpretable and facilitate trans-disciplinary collaboration. This article summarizes recent challenges and introduces the contributions to this ESANN special session.
Coherent Selection of Independent Trackers for Real-time Object Tracking

This paper presents a new method for combining several independent and heterogeneous tracking algorithms for the task of online single-object tracking. The proposed algorithm runs several trackers in parallel, where each of them relies on a different set of complementary low-level features. Only one tracker is selected at a given frame, and the choice is based on a spatio-temporal coherence criterion and normalised confidence estimates. The key idea is that the individual trackers are kept completely independent, which reduces the risk of drift in situations where for example a tracker with an inaccurate or inappropriate appearance model negatively impacts the performance of the others. Moreover, the proposed approach is able to switch between different tracking methods when the scene conditions or the object appearance rapidly change. We experimentally show with a set of Online Adaboost-based trackers that this formulation of multiple trackers improves the tracking results in comparison to more classical combinations of trackers. And we further improve the overall performance and computational efficiency by introducing a selective update step in the tracking framework.
The Haemophilus ducreyi Sap transporter contributes to antimicrobial peptide resistance

s and Presentations Mount, K.L. B. , C.A. Townsend, M.E. Bauer. (2007) “Haemophilus ducreyi is Resistant to Human Antimicrobial Peptides. ” American Society for Microbiology General Meeting, Orlando, Florida, 2006 Mount, K.L. B. , C.A. Townsend, M.E. Bauer. (2007) “Haemophilus ducreyi is Resistant to Human Antimicrobial Peptides. ” Midwest Microbial Pathogenesis Meeting, Cincinnati, Ohio, 2006 Mount, K.L. B. , C.A. Townsend, M.E. Bauer. (2009) “The Haemophilus ducreyi Sap Transporter Contributes to Cathelicidin Resistance. ” Midwest Microbial Pathogenesis Meeting, Madison, Wisconsin, 2008 Awards Harold Raidt Teaching Award: 2007 Sigma Xi Research Competition, Second Place: 2006 American Society of Microbiology Travel Award: 2006 Indiana University School of Medicine Travel Award: 2006 Howard Hughes Undergraduate Research Fellowship: 2000
Can Birds Perceive Rhythmic Patterns? A Review and Experiments on a Songbird and a Parrot Species

While humans can easily entrain their behavior with the beat in music, this ability is rare among animals. Yet, comparative studies in non-human species are needed if we want to understand how and why this ability evolved. Entrainment requires two abilities: (1) recognizing the regularity in the auditory stimulus and (2) the ability to adjust the own motor output to the perceived pattern. It has been suggested that beat perception and entrainment are linked to the ability for vocal learning. The presence of some bird species showing beat induction, and also the existence of vocal learning as well as vocal non-learning bird taxa, make them relevant models for comparative research on rhythm perception and its link to vocal learning. Also, some bird vocalizations show strong regularity in rhythmic structure, suggesting that birds might perceive rhythmic structures. In this paper we review the available experimental evidence for the perception of regularity and rhythms by birds, like the ability to distinguish regular from irregular stimuli over tempo transformations and report data from new experiments. While some species show a limited ability to detect regularity, most evidence suggests that birds attend primarily to absolute and not relative timing of patterns and to local features of stimuli. We conclude that, apart from some large parrot species, there is limited evidence for beat and regularity perception among birds and that the link to vocal learning is unclear. We next report the new experiments in which zebra finches and budgerigars (both vocal learners) were first trained to distinguish a regular from an irregular pattern of beats and then tested on various tempo transformations of these stimuli. The results showed that both species reduced the discrimination after tempo transformations. This suggests that, as was found in earlier studies, they attended mainly to local temporal features of the stimuli, and not to their overall regularity. However, some individuals of both species showed an additional sensitivity to the more global pattern if some local features were left unchanged. Altogether our study indicates both between and within species variation, in which birds attend to a mixture of local and to global rhythmic features.
Knowledge Acquisition for Robots Through Mixed Reality Head-Mounted Displays

—A robot can clean a room by performing a series of pick-and-place tasks on relevant items. To accomplish this sequence, the robot must know the original poses of the relevant items, how to grasp these items, and the ﬁnal poses to place each item at. Language and gestures have been shown to successfully enable humans to help a robot acquire this knowledge, but are limited because they do not allow people to express poses precisely. Mixed Reality Head-Mounted Displays (MR-HMDs) have recently gained traction as a means to facilitate human-robot communication because they can visualize 3D graphics on the environment from the perspective of of the user. We hypothesize that MR-HMDs provide an intuitive interface for enabling end-users to help robots acquire grounded knowledge about how to clean up a shared workspace. We propose an MR system that allows a user to communicate to a mobile manipulator what items need to be placed away, how to grab them, where they are located and where they need to be placed. After this information is provided, the robot can autonomously clean a room.
How much does pre-trained information help? Partially re-initializing BERT during ﬁne-tuning to analyze the contribution of layers

Transformer models like BERT are used extensively in NLP tasks. We add to the growing literature on investigating how BERT works by analyzing the contribution of pre-trained knowledge for each layer on downstream tasks. We introduce a method called partial re-initialization to understand the performance gains for each layer from starting with pre-trained parameters and then ﬁne-tuning versus starting from randomly initialized parameters. We compare this performance across three GLUE tasks (SST, QNLI, and CoLA) and test how this performance varies by training data size. We also study how the weights for each layer change from the pre-trained BERT model during ﬁne-tuning. In general, we see that when there is less training data, there is more reliance on pre-trained weights, which leads to large performance gains over a fully supervised model with more training data. As training data size increases, the performance gains from the pre-trained information of each layer are lower, subject to the nature of the task. We also ﬁnd that the parameters of the later layers get overwritten more during ﬁne-tuning, while the earlier layers remain largely invariant. With more training data, the parameters are transformed more.
From STEAM to Machinetta: Evolution of a Teamwork Model

For applications involving heterogeneous agents working together to achieve complex goals, teamwork (Jennings, 1995; Yen, Yin, Ioerger, Miller, Xu, & Volz, 2001; Tambe, 1997) has emerged as the dominant coordination paradigm. For domains as diverse as rescue response, military, space, sports and collaboration between human workmates, flexible, dynamic coordination between cooperative agents needs to be achieved despite complex, uncertain, and hostile environments. There is now emerging consensus in the multiagent arena that for flexible teamwork among agents, each team member is provided with an explicit model of teamwork, which entails their commitments and responsibilities as a team member. This explicit modelling allows the coordination to be robust, despite individual failures and unpredictably changing environments. Building on the well developed theory of joint intentions (Cohen & Levesque, 1991) and shared plans (Grosz & Kraus, 1996), the STEAM teamwork model (Tambe, 1997) was operationalized as a set of domain independent rules that describe how teams should work together. This domain independent teamwork model has been successfully applied to a variety of domains. From combat air missions (Hill, Chen, Gratch, Rosenbloom, & M.Tambe, 1997) to robot soccer (Kitano, Asada, Kuniyoshi, Noda, Osawa, & Matsubara, 1997) to teams supporting human organizations (Pynadath & Tambe, 2003) to rescue response (Scerri, Pynadath, Johnson, P., Schurr, Si, & Tambe, 2003), applying the same set of STEAM rules has resulted in successful coordination between heterogeneous agents. The successful use of the same teamwork model in a wide variety of diverse domains provides compelling evidence that it is the principles of teamwork, rather than exploitation of specific domain phenomena, that underlies the success of teamwork based approaches.
Question Answering with Gated Attention and Multitask Learning - Option 3 (Graded)

Recently, there is a growing interest in the research of machine text comprehension and automatic question answering. Many state-of-the-art QA models consist of two components: recurrent neural networks to encode sequential inputs and structures, and attention mechanisms to deal with long term interactions of context and query words. In this paper, we started with the BiDAF, and included character-level embedding, self attention mechanism with gated residual blocks to enhance performance. Then, we discussed QANet’s non-recurrent architecture, and applied character-level embedding and residual attention moduel to QANet. We also uniﬁed the output layer with single branch to equally learn the start and end positions. To further improve the performance, we propose multi-task learning with ensemble inference. Along with optimization and hyperparameters tuning, we have obtained competitive results on the SQuAD 2.0 dataset.
Efﬁﬁcent Language Modeling of Long-Term Dependencies

The transformer architecture [1] has received signiﬁcant research attention due to its ability to effectively model word-level and global contextual dependencies exclusively using self-attention, thereby enabling language models such as BERT [2] to achieve state-of-the-art performance. However, the quadratic cost of attention and linear memory cost per layer have severely limited the transformer to operate solely on short sequences, a problem partially addressed by the reversible layers and LSH attention proposed in the reformer [3]. In this work, we study and improve properties of the reformer as a language model, introducing k -means clustering for attention and connection tying in reversible layers to improve reformer complexity and representational power. We evaluate our model on masked language modeling as well as selected GLUE benchmarks, and we ﬁnd that our modiﬁcations signiﬁ-cantly improve training times and model capacity. The methods presented in our work are generalizable to other attention-based models and have the potential to drastically improve the efﬁcacy of long-term language dependency modeling.
Compressed SQuAD 2.0 Model With BERT

The goal of our project is to design and train a SQuAD 2.0 model based on BERT that achieves reasonable performance with the smallest possible model size and limited training resources. While context-aware language models like BERT have achieved state-of-the-art performance on many NLP tasks, including SQuAD, their large size makes it difﬁcult to host them in resource-constrained environments like mobile and edge devices for ofﬂine question answering. Starting from BERT BASE , we implemented several size reduction techniques. Our ﬁnal models achieve 96% of the original performance with 90% reduction in storage space, or 92% of the original performance with 95% reduction in storage space. Unlike many other compressed BERT-like models with high training costs, our models can be ﬁne-tuned from BERT in a few hours on a single GPU, making it more accessible and useful to people and organizations who do not have access to large computational resources.
What errors are made by TyDi QA systems?

Everyday people turn to Question Answering systems regularly when they are faced with a question and looking for an answer, however sometimes these QA systems answer the questions incorrectly. Our goal is to understand what errors the QA systems trained on Topologically diverse (TyDi) dataset[1] typically makes for e.g. are these errors due to language structure, or how the question is framed or the nature of passage picked to answer the questions? On a high level our ﬁndings suggest that some of the causes of the errors were 1.the ambiguity in framing the question, 2. complex structure of the answer passage inﬂuences incorrect answers, 3. multiple possible answers. In an attempt to improve the accuracy of the QA systems on TyDi, we built a classiﬁer by ﬁne tuning mbert model.