Miracle-dz/UniFAR-FTUs huggingface dataset:
Video Dialog as Conversation about Objects Living in Space-Time

It would be a technological feat to be able to create a system that can hold a meaningful conversation with humans about what they watch. A setup toward that goal is presented as a video dialog task, where the system is asked to generate natural utterances in response to a question in an ongoing dialog. The task poses great visual, linguistic, and reasoning challenges that cannot be easily overcome without an appropriate representation scheme over video and dialog that supports high-level reasoning. To tackle these challenges we present a new object-centric framework for video dialog that supports neural reasoning dubbed COST - which stands for Conversation about Objects in Space-Time. Here dynamic space-time visual content in videos is first parsed into object trajectories. Given this video abstraction, COST maintains and tracks object-associated dialog states, which are updated upon receiving new questions. Object interactions are dynamically and conditionally inferred for each question, and these serve as the basis for relational reasoning among them. COST also maintains a history of previous answers, and this allows retrieval of relevant object-centric information to enrich the answer forming process. Language production then proceeds in a step-wise manner, taking into the context of the current utterance, the existing dialog, the current question. We evaluate COST on the DSTC7 and DSTC8 benchmarks, demonstrating its competitiveness against state-of-the-arts.
Non-linear affine processes with jumps

We present a probabilistic construction of $\mathbb{R}^d$-valued non-linear affine processes with jumps. Given a set $\Theta$ of affine parameters, we define a family of sublinear expectations on the Skorokhod space under which the canonical process $X$ is a (sublinear) Markov process with a non-linear generator. This yields a tractable model for Knightian uncertainty for which the sublinear expectation of a Markovian functional can be calculated via a partial integro-differential equation.
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method

This article focuses on the multi-objective optimization of stochastic simulators with high output variance, where the input space is finite and the objective functions are expensive to evaluate. We rely on Bayesian optimization algorithms, which use probabilistic models to make predictions about the functions to be optimized. The proposed approach is an extension of the Pareto Active Learning (PAL) algorithm for the estimation of Pareto-optimal solutions that makes it suitable for the stochastic setting. We named it Pareto Active Learning for Stochastic Simulators (PALS). The performance of PALS is assessed through numerical experiments over a set of bi-dimensional, bi-objective test problems. PALS exhibits superior performance when compared to other scalarization-based and random-search approaches.
The Impact of Cooperation in Bilateral Network Creation

Many real-world networks, like the Internet or social networks, are not the result of central design but instead the outcome of the interaction of local agents that selfishly optimize their individual utility. The well-known Network Creation Game by Fabrikant, Luthra, Maneva, Papadimitriou, and Shenker [23] models this. There, agents corresponding to network nodes buy incident edges towards other agents for a price of α > 0 and simultaneously try to minimize their buying cost and their total hop distance. Since in many real-world networks, e.g., social networks, consent from both sides is required to establish and maintain a connection, Corbo and Parkes [14] proposed a bilateral version of the Network Creation Game, in which mutual consent and payment are required in order to create edges. It is known that this cooperative version has a significantly higher Price of Anarchy compared to the unilateral version. On the first glance this is counter-intuitive, since cooperation should help to avoid socially bad states. However, in the bilateral version only a very restrictive form of cooperation is considered. We investigate this trade-off between the amount of cooperation and the Price of Anarchy by analyzing the bilateral version with respect to various degrees of cooperation among the agents. With this, we provide insights into what kind of cooperation is needed to ensure that socially good networks are created. As a first step in this direction, we focus on tree networks and present a collection of asymptotically tight bounds on the Price of Anarchy that precisely map the impact of cooperation. Most strikingly, we find that weak forms of cooperation already yield a significantly improved Price of Anarchy. In particular, the cooperation of coalitions of size 3 is enough to achieve constant bounds. Moreover, for general networks we show that enhanced cooperation yields close to optimal networks for a wide range of edge prices. Along the way, we disprove an old conjecture by Corbo and Parkes [14].
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning

While many multi-robot coordination problems can be solved optimally by exact algorithms, solutions are often not scalable in the number of robots. Multi-Agent Reinforcement Learning (MARL) is gaining increasing attention in the robotics community as a promising solution to tackle such problems. Nevertheless, we still lack the tools that allow us to quickly and efficiently find solutions to large-scale collective learning tasks. In this work, we introduce the Vectorized Multi-Agent Simulator (VMAS). VMAS is an open-source framework designed for efficient MARL benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of twelve challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface. We demonstrate how vectorization enables parallel simulation on accelerated hardware without added complexity. When comparing VMAS to OpenAI MPE, we show how MPE's execution time increases linearly in the number of simulations while VMAS is able to execute 30,000 parallel simulations in under 10s, proving more than 100x faster. Using VMAS's RLlib interface, we benchmark our multi-robot scenarios using various Proximal Policy Optimization (PPO)-based MARL algorithms. VMAS's scenarios prove challenging in orthogonal ways for state-of-the-art MARL algorithms. The VMAS framework is available at https://github.com/proroklab/VectorizedMultiAgentSimulator. A video of VMAS scenarios and experiments is available at https://youtu.be/aaDRYfiesAY.
Bernstein-Kouchnirenko-Khovanskii with a symmetry

A generic polynomial f(x,y,z) with a prescribed Newton polytope defines a symmetric spatial curve f(x,y,z)=f(y,x,z)=0. We study its geometry: the number, degree and genus of its irreducible components, the number and type of singularities, etc. and discuss to what extent these results generalize to higher dimension and more complicated symmetries. As an application, we characterize generic one-parameter families of complex univariate polynomials, whose Galois group is a complete symmetric group.
ABB-BERT: A BERT model for disambiguating abbreviations and contractions

Abbreviations and contractions are commonly found in text across different domains. For example, doctors’ notes contain many contractions that can be personalized based on their choices. Existing spelling correction models are not suitable to handle expansions because of many reductions of characters in words. In this work, we propose ABB-BERT, a BERT-based model, which deals with an ambiguous language containing abbreviations and contractions. ABB-BERT can rank them from thousands of options and is designed for scale. It is trained on Wikipedia text, and the algorithm allows it to be fine-tuned with little compute to get better performance for a domain or person. We are publicly releasing the training dataset for abbreviations and contractions derived from Wikipedia.
Robot Trajectory Adaptation to Optimise the Trade-off between Human Cognitive Ergonomics and Workplace Productivity in Collaborative Tasks

In hybrid industrial environments, workers' comfort and positive perception of safety are essential requirements for successful acceptance and usage of collaborative robots. This paper proposes a novel human-robot interaction framework in which the robot behaviour is adapted online according to the operator's cognitive workload and stress. The method exploits the generation of B-spline trajectories in the joint space and formulation of a multi-objective optimisation problem to online adjust the total execution time and smoothness of the robot trajectories. The former ensures human efficiency and productivity of the workplace, while the latter contributes to safeguarding the user's comfort and cognitive ergonomics. The performance of the proposed framework was evaluated in a typical industrial task. Results demonstrated its capability to enhance the productivity of the human-robot dyad while mitigating the cognitive workload induced in the worker.
Finite-rate sparse quantum codes aplenty

We introduce a methodology for generating random multi-qubit stabilizer codes based on solving a constraint satisfaction problem (CSP) on random bipartite graphs. This framework allows us to enforce stabilizer commutation, X/Z balancing, finite rate, sparsity, and maximum-degree constraints simultaneously in a CSP that we can then solve numerically. Using a state-of-the-art CSP solver, we obtain convincing evidence for the existence of a satisfiability threshold. Furthermore, the extent of the satisfiable phase increases with the number of qubits. In that phase, finding sparse codes becomes an easy problem. Moreover, we observe that the sparse codes found in the satisfiable phase practically achieve the channel capacity for erasure noise. Our results show that intermediate-size finite-rate sparse quantum codes are easy to find, while also demonstrating a flexible methodology for generating good codes with custom properties. We therefore establish a complete and customizable pipeline for random quantum code discovery.
A new type of results on probabilities of moderate deviations for i.i.d. random variables

Let { X, X n ; n ≥ 1 } be a sequence of i.i. d. non-degenerate real-valued random variables with E X 2 < ∞ . Let n 1. Let g ( · ) : [0 , ∞ ) → [0 , ∞ ) be a nondecreasing regularly varying function with index ρ ≥ 0 and lim t →∞ g ( t ) = ∞ . Let µ = E X and σ 2 = E ( X − µ ) 2 . In this paper, on the scale g (log n ), we study the probabilities of moderate deviations of the form log P and for all x > 0. We obtain precise asymptotic estimates for these probabilities by showing that, for all x > 0, and where λ 1 are λ 1 are determined by the asymptotic behavior of P ( X > t ) as t → ∞ and λ 2 and λ 2 are determined by the asymptotic behavior of P ( X < − t ) as t → ∞ . Therefore, we can see that, unlike those known results in the literature, the moderate deviation results established in this paper depend on both the variance and the asymptotic behavior of the tail distribution of X .
The Frequency of Rh Phenotype and Its Probable Genotype

Aims and objectives: Our goal is to disseminate data on the distribution pattern of Rh antigen, its phenotypes, and the likely genotypes of these genetic variants in the Pakistani population. Methodology: This study was a cross-sectional research project. Patients’ demographic statistics, such as age and gender, were gathered from their medical information. Blood group, disease, RhD, and other antigen frequency, phenotype, and probable genotype were considered variables. All blood samples were phenotyped for Rhesus antigens (D, C, c, E, and e), and the test was carried out using the tubing technique. Results: According to gender distribution, most of the patients were males, with 131 frequencies (57.7%), while females had 42.35%. The most common phenotype was DCCee, with its probable genotype DCe/DCe (R1 R1) (34%), followed by DCcee, with probable genotype DCe/ce (R1 r) (29.1%); the least common phenotype was ddCcee, with its probable genotype Ce/ce (r ' r) (0.4%). Conclusion: It is concluded that the DCCee phenotype was the most common with its probable genotype DCe/DCe, while the least common phenotype was ddCcee with its probable genotype Ce/ce.
Some metric properties of arithmetic quotients of symmetric spaces

A board game is designed around the theme of gaining and losing weight and counting calories and includes a game board having a playing surface defining a travel path with a plurality of separate stations along the path. Manually movable, hollow playing pieces styled as obese persons are provided for movement around the stations on the travel path. Each playing piece has associated therewith a set of stations on the game board representing restaurants and a plurality of weight elements are provided to be inserted into the open mouth of the hollow, playing pieces. Chance dice are provided for determining the number of stations along the path that each playing piece is moved during a turn and for determining a number of weight elements that are to be inserted into the open mouth of a playing piece. On the travel path, another player may be challenged to a weigh in and the playing piece having the heaviest weight then must discharge the weight elements and register the number in a cumulative score. The player ending up with the least number of calories or weight elements is the winner.
OR5_OA_Al maleki_2_5_60C

Background: Trauma is considered as a major epidemic of non-communicable diseases in the current century. Aims: To assess the pattern and characteristics of the trauma in order to plan for injury reduction and control strategy for the future. Methods: A total of 1400 patients were enrolled during the period from 1 June to 30 of December 2015. Interview was done for all the patients who presented to the emergency department of the AL Basrah General Hospital with a history of trauma. Results: The mean age of the patients was 21.81±17.74 years. Nearly one third (33.0%) of the patients were at the age of 1-9 years. Males (68.8%) were more frequently seen than females. The people who finished primary school (33.8%) and those who worked as Laborers (31.5%) were more exposed to trauma. Blunt trauma constituted (74.6%) of the main types and fall from standing (36.9%) was the most common type of blunt trauma followed by RTA (15.5%). Soft tissue injuries presented in (61.8%) of trauma patients and mostly were minor trauma (89.6%). Conclusion: Trauma is a major health problem, especially in the young male with age less than 20 years. Blunt trauma is more frequent than penetrating trauma, fall from standing accounting for the majority. Laborers with low educational level were more commonly exposed to trauma. High population density areas were increasing the rate of accidents.
On the extended Maxwell-Garnett and the extended Bruggeman approaches for dielectric-in-dielectric composites

The Bruggeman formula for dielectric-in-dielectric composites has been extended to include the size parameter. Comparison has been made with the similar extension of the Maxwell-Garnett formula. In both extensions, the dielectric inclusion has been represented solely by an electric dipole, but the Mossotti-Clausius polarizability has been replaced by one that takes into better account the singularity behavior of the free space dyadic Green's function
Circulating microRNAs as Reliable Tumor Biomarkers: Opportunities and Challenges Facing Clinical Application

MicroRNAs (miRNAs) are involved in the development of human malignancies, and cells have the ability to secrete these molecules into extracellular compartments. Thus, cell-free miRNAs (circulating miRNAs) can potentially be used as biomarkers to evaluate pathophysiological changes. Although circulating miRNAs have been proposed as potential noninvasive tumor biomarkers for diagnosis, prognosis, and response to therapy, their routine application in the clinic is far from being achieved. This review focuses on the recent progress regarding the value of circulating miRNAs as noninvasive biomarkers, with specific consideration of their relevant clinical applications. In addition, we provide an in-depth analysis of the technical challenges that impact the assessment of circulating miRNAs. We also highlight the significance of integrating circulating miRNAs with the standard laboratory biomarkers to boost sensitivity and specificity. The current status of circulating miRNAs in clinical trials as tumor biomarkers is also covered. These insights and general guidelines will assist researchers in experimental practice to ensure quality standards and repeatability, thus improving future studies on circulating miRNAs. SIGNIFICANCE STATEMENT Our review will boost the knowledge behind the inconsistencies and contradictory results observed among studies investigating circulating miRNAs. It will also provide a solid platform for better-planned strategies and standardized techniques to optimize the assessment of circulating cell-free miRNAs.
Parkinson's Disease(PD) is the one of the most common neurodegenerative disorders,affecting five million people worldwide. The loss of dopamine neurons in the substantia nigra and the formation of intraneuronal inclusions called Lewy bodies account for t

As literature reported, C57BL/6 A53T mutant human alpha-synuclein under the mice is one successful model related to Parkinson’s disease(PD). In previous studies, over-accumulation of alpha-synuclein which lead to lewy body, were revealed to cause impairment on motor ability in Parkinson’s disease mouse model. Nevertheless, in the open filed test and the rota-rod test, hyperactivity and motor ability improvements at 12 months old was found in our study.
Influence of latent attitudinal factors on the multimodality of post-secondary students in Toronto

The study explores the causal relationships between latent factors and various observed external variables along with their ability to explain multimodal behaviours of post-secondary students in Toronto. Multimodality was measured by the number of unique modes used by any individual for their daily travels. As opposed to using a single mode of transportation, use of multiple modes for different trips indicates the degree and the nature of multimodality. For the empirical investigation, the study uses structural equation modelling and ordered probability modelling for a dataset collected through a large-scale travel diary survey among four major universities in Toronto representing over 180 000 post-secondary students in the region. The results of the empirical investigation reveal that latent attitudes are influential factors in determining the multimodal behaviour of post-secondary students in Toronto. The results also found that mobility tool ownership and land use characteristics have a significant influence on those latent attitudes, and are direct determinants of the degree of multimodality. In particular, the results indicate that smart fare payment cards have a considerable effect on latent attitudes for post-secondary students. These findings could have policy implication from the planning perspective and should warrant further investigations.
Rarity begets rarity: Social and environmental drivers of rare organisms in cities

Abstract Cities are sometimes characterized as homogenous with species assemblages composed of abundant, generalist species having similar ecological functions. Under this assumption, rare species, or species observed infrequently, would have especially high conservation value in cities for their potential to increase functional diversity. Management to increase the number of rare species in cities could be an important conservation strategy in a rapidly urbanizing world. However, most studies of species rarity define rarity in relatively pristine environments where human management and disturbance is minimized. We know little about what species are rare, how many species are rare, and what management practices promote rare species in urban environments. Here, we identified which plants and species of birds and bees that control pests and pollinate crops are rare in urban gardens and assessed how social, biophysical factors, and cross‐taxonomic comparisons influence rare species richness. We found overwhelming numbers of rare species, with more than 50% of plants observed classified as rare. Our results highlight the importance of women, older individuals, and gardeners who live closer to garden sites in increasing the number of rare plants within urban areas. Fewer rare plants were found in older gardens and gardens with more bare soil. There were more rare bird species in larger gardens and more rare bee species for which canopy cover was higher. We also found that in some cases, rarity begets rarity, with positive correlations found between the number of rare plants and bee species and between bee and bird species. Overall, our results suggest that urban gardens include a high number of species existing at low frequency and that social and biophysical factors promoting rare, planned biodiversity can cascade down to promote rare, associated biodiversity.
2018 Wind Technologies Market Report

LBNL-XXXX E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY 2012 Wind Technologies Market Report Primary authors Ryan Wiser, Lawrence Berkeley National Laboratory Mark Bolinger, Lawrence Berkeley National Laboratory With contributions from Galen Barbose, Naim Darghouth, Ben Hoen, Andrew Mills, Samantha Weaver (Berkeley Lab) Kevin Porter, Michael Buckley, Sari Fink (Exeter Associates) Frank Oteri, Suzanne Tegen (National Renewable Energy Laboratory) Environmental Energy Technologies Division July 2013 Berkeley Lab’s contributions to this report were funded by the Wind & Water Power Technology Office, Office of Energy Efficiency and Renewable Energy of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231
Cognitive Estimation of Development Effort, Time, Errors, and the Defects of Software

In the software industry, measuring the effort and time for developing of software is very challenging. Measuring development effort and time comprises several phases, but measuring the effort in each phase creates problems. It is also observed that estimation of the effort required for developing a project may be over-estimated or under-estimated. It can lead to enormous damage to the organization, with respect to budget and schedule. So, to address the aforementioned, a cognitive technique is proposed for measuring the development effort, time, and errors. After measuring the development effort, machine learning techniques: Bayesian Net, Logistic Regression, Multi-perceptron, SMO, and Lib-SVM are applied for software defect prediction. To estimate the software development effort and defects, NASA PROMISE: CM1, KC3, PC1, PC2, and JM1 datasets and devised datasets (proposed cognitive technique parameters of original datasets) are used. The experimental results of both the experiments prove the goodness of the proposed work of this paper.