Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Health Care through Housing First

This research was funded by the Robert Wood Johnson Foundation. We are grateful to them and to all our funders, who make it possible for Urban to advance its mission. This study would not be possible without the entire Denver Supportive Housing Social Impact Bond Initiative (Denver SIB) research and project teams. From the research team, the authors thank Nicole DuBois, Cary Lou, and Alyse Oneto for ongoing research assistance and project management support; Sybil Mendonca and Paul Johnson for Medicaid claims analysis; and Lisa Dubay, Victoria Lynch, and Mary Cunningham for research guidance and technical review. From the project team, the authors thank all the Denver SIB partners who made the project possible, including staff at Colorado Coalition for the Homeless, the Mental Health Center of Denver, the Corporation for Supportive Housing, and agencies across the City and County of Denver, as well as staff at Colorado Access and Denver Health and Hospital Authority who supported long-term data collection efforts.
Seeing is Believing: Tourism and Foreign Equity Investments

We examine whether international tourism affects financial market investments. Using data for more than 40 countries, we demonstrate that recreational travel between countries is associated with higher levels of foreign equity investments. Increased foreign investments lead to a reduction in home bias and improved diversification. The impact of tourism is stronger for countries that are farther apart or when home country residents are more risk averse. Using predictors of recreational travel as instruments, we show that the relation between foreign travel and foreign equity investments is causal. Collectively, these results suggest that tourism has positive externalities in financial markets.
Real-World Trajectory Sharing with Local Differential Privacy

Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping n -grams (i.e., contiguous subsequences of length n ) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.
CURRENT SCENARIO OF SARS-CORONAVIRUS 2: EPIDEMIOLOGY; POST-COVID-19 AND GLOBAL IMPACTS

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a highly contagious strain of coronavirus that causes Coronavirus Disease 2019 (COVID-19) infection, which has distressed the world's health and wealth. This Global Pandemic outbreak has affected public health enormously at various customs. The investigation of SARS-CoV-2 is still at infancy; however, based on the available reports, this review gives an overview of the epidemiology, genomic landscape, diversity of SARS-CoV-2, viral genome pathogenic interactions, associating factors for COVID-19 infections, post-COVID-19, disease manifestations with their comorbidities, the major obstacles and the preventive measures along with current vaccine strategies of SARS-CoV-2. This review also summarizes all the relevant evidence of COVID-19 illness, which can provide valuable information on the SARS-CoV-2 genome and its mode of action strategies, thus delivering additional knowledge about COVID-19.
An agroecological Europe by 2050: What impact on land use, trade and global food security?

Because of the reduction in the consumption of animal protein and the relocation of plant protein production, an agroecological EU outperforms today’s system in providing nutrients/calories to the rest of the world, and becomes a net exporter of calories by 12% of what it consumes. Indeed, while today the EU is a major exporter in value terms thanks to high value commodities (ex. spirits, wine, cheese, cigarettes and other high processed commodities) that are not part and parcel of global food security, it is a net importer of calories and proteins by 11% and 26% of what it consumes, respectively. No sustainable agroecological transition can happen in the EU without strong policies that: • Support a great dietary transition towards healthier and less calorie-dense diets with less animal and ultra-processed food products; • Maintain EU price and non-price competitiveness in the domestic and foreign markets through agronomic research, a better coordination between actors and a market segmentation for EU “ecologically intensive” agricultural commodities; • Change current market conditions to improve EU protein autonomy through the reintegration of legumes in rotations. N° 07 July 2021 ST U D Y An agroecological Europe by 2050: What impact on land use, trade and global food security?
Measuring Cybercrime as a Service (CaaS) Offerings in a Cybercrime Forum

The emergence of Cybercrime-as-a-Service (CaaS) is a critical evolution in the cybercrime landscape. A key area of research on CaaS is where and how the supply of CaaS is being matched with demand. Next to underground marketplaces and custom websites, cyber-crime forums provide an important channel for CaaS suppliers to attract customers. Our study presents the first comprehensive and longitudinal analysis of types of CaaS supply and demand on a cybercrime forum. We develop a classifier to identify supply and demand for each type and measure their relative prevalence and apply this to a dataset spanning 11 years of posts on Hack Forums, one of the largest and oldest ongoing English-language cybercrime forum on the surface web. Of 28 known CaaS types, we only found evidence for only 9 of these in the forum. We saw no dramatic shifts in these offerings over time, not even after major underground mar-ketplaces were being seized by law enforcement. Around 16% of first posts of the threads in the ‘Market’ section of the forum offers CaaS, whereas only 3% is focused on product-type criminal offerings. Within the types of CaaS, ‘bot/botnet as a service’, ‘repu-tation escalation as a service’ and ‘traffic as a service’ categories make up the majority (over 60%) for whole period in terms of both supply and demand. At least half of each CaaS offerings directs potential buyers to an instant messaging app or private message for transacting privately. In sum, we find that forums do in fact provide a channel for CaaS supply and demand to meet, but we see only a fraction of the CaaS landscape and there is no evidence in our data for the supposed growth of CaaS over time. We reflect on the implications of our findings for developing
Activity of Disinfecting Biocides and Enzymes of Proteases and Amylases on Bacteria in Biofilms

The presence of microbial biofilms on the surfaces of medical instruments, operating equipment, prostheses, catheters, technological lines in the food industry is a fact that contributes to the infection of the macroorganism and contamination of raw materials and products. The aim of the work was to investigate the eff ect of disinfecting substances Vantocilu TG and Catamine AB and their combination with enzymes on bacteria in biofilms. In the experiments, we used disinfecting substances Vantocil TG (Arch Biocides LTD, Great Britain) and Catamine AB (Intersintez, Ukraine). Enzymes: Everlase 16 L and Termamyl 300 L (Novozymes, Denmark). It was found that bacteria in biofilms withstood the minimum bactericidal concentration of Vantocil and Catamine, which was set on their planktonic forms. From one mL of wash from the biofilm after exposure to Vantocil were isolated from 1.9×103 to 4.3×103 microbial cells, and after treatment with Catamine from 5.6×103 to 1.7×104. At the same time, after treatment of biofilms with Vantocil and Catamine together with enzymes, a decrease in the number of Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa cells was observed, on average by two orders up to 101 CFU/mL, compared with treatment with biocides only. That is, there is a clear synergy of enzymes and biocides, which ultimately has a more detrimental eff ect on bacteria in biofilms.
Some Effects of Temperature and SO2on Wine During Simulated Transport or Storage

White and red table wine samples, under simulated transit or storage conditions, were held at five different temperatures (28°, 32°, 38°, 43°, and 47°C) at low and medium SO 2 levels for 21 days. Changes in composition during the storage period and wine quality afterward were measured and evaluated. The need for the use of SO 2 was verified if quality of the wines was to be maintained. The problems caused by the failure to use SO 2 are increased dramatically by increasing temperature. SO 2 and color changes were related to temperature. Red pigment was lost for the red wine at about the same rate the white wine browned for the low SO 2 wines. The loss in SO 2 was more rapid with increasing temperature for the red wine than for the white.
CogDL: A Toolkit for Deep Learning on Graphs

—Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social and information networks, biological graphs, and molecular graphs. In this paper, we present CogDL—an extensive toolkit for deep learning on graphs—that allows researchers and developers to easily conduct experiments and build applications. In CogDL, we propose a uniﬁed design for the training loop of graph neural network (GNN) models, making it unique among existing graph learning libraries. By utilizing this uniﬁed trainer, we can optimize the GNN training loop with several training techniques such as distributed training and mixed precision training. Moreover, we develop efﬁcient sparse operators for CogDL, enabling it to become the most competitive graph library for efﬁciency. Additionally, another important CogDL feature is its focus on ease of use with the goal of facilitating open, robust, and reproducible graph learning research. We leverage CogDL to report and maintain benchmark results on the fundamental graph tasks such as node classiﬁcation and graph classiﬁcation, which can be reproduced and directly used by the community. Finally, we demonstrate the effectiveness and efﬁciency of CogDL for real-world applications in AMiner—a large-scale academic mining and search system. The CogDL toolkit is available at: https://github.com/thudm/cogdl.
Unveiling non-Markovian spacetime signaling in open quantum systems with long-range tensor network dynamics

Nanoscale devices - either biological or artificial - operate in a regime where the usual assumptions of a structureless, Markovian, bath do not hold. Being able to predict and study the dynamics of such systems is crucial and is usually done by tracing out the bath degrees of freedom, which implies losing information about the environment. To go beyond these approaches we use a numerically exact method relying on a Matrix Product State representation of the quantum state of a system and its environment to keep track of the bath explicitly. This method is applied to a specific example of interaction that depends on the spatial structure of the system. The result is that we predict a non-Markovian dynamics where long-range couplings induce correlations into the environment. The environment dynamics can be naturally extracted from our method and shine a light on long time feedback effects that are responsible for the observed non-Markovian recurrences in the eigen-populations of the system.
LocalGLMnet: interpretable deep learning for tabular data

ABSTRACT Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models. The disadvantage of deep learning models is that their solutions are difficult to interpret and explain, and variable selection is not easily possible because deep learning models solve feature engineering and variable selection internally in a nontransparent way. Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models but provides superior predictive power benefiting from the art of representation learning. This new architecture allows for variable selection of tabular data and for interpretation of the calibrated deep learning model, in fact, our approach provides an additive decomposition that can be related to other model interpretability techniques.
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time

In this paper we prove that Local (S)GD (or FedAvg) can optimize deep neural networks with Rectified Linear Unit (ReLU) activation function in polynomial time. Despite the established convergence theory of Local SGD on optimizing general smooth functions in communication-efficient distributed optimization, its convergence on non-smooth ReLU networks still eludes full theoretical understanding. The key property used in many Local SGD analysis on smooth function is gradient Lipschitzness, so that the gradient on local models will not drift far away from that on averaged model. However, this decent property does not hold in networks with non-smooth ReLU activation function. We show that, even though ReLU network does not admit gradient Lipschitzness property, the difference between gradients on local models and average model will not change too much, under the dynamics of Local SGD. We validate our theoretical results via extensive experiments. This work is the first to show the convergence of Local SGD on non-smooth functions, and will shed lights on the optimization theory of federated training of deep neural networks.
Laplace and Saddlepoint Approximations in High Dimensions

We examine the behaviour of the Laplace and saddlepoint approximations in the high-dimensional setting, where the dimension of the model is allowed to increase with the number of observations. Approximations to the joint density, the marginal posterior density and the conditional density are considered. Our results show that under the mildest assumptions on the model, the error of the joint density approximation is $O(p^4/n)$ if $p = o(n^{1/4})$ for the Laplace approximation and saddlepoint approximation, and $O(p^3/n)$ if $p = o(n^{1/3})$ under additional assumptions on the second derivative of the log-likelihood. Stronger results are obtained for the approximation to the marginal posterior density.
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge

Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality relation between verbs and arguments in isolation, in this paper we take a broader perspective by assessing whether and to what extent computational approaches have access to the information about the typicality of entire events and situations described in language (Generalized Event Knowledge). Given the recent success of Transformers Language Models (TLMs), we decided to test them on a benchmark for the dynamic estimation of thematic fit. The evaluation of these models was performed in comparison with SDM, a framework specifically designed to integrate events in sentence meaning representations, and we conducted a detailed error analysis to investigate which factors affect their behavior. Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge, and their predictions often depend on surface linguistic features, such as frequent words, collocations and syntactic patterns, thereby showing sub-optimal generalization abilities.
Put your money where your mouth is: Using AI voice analysis to detect whether spoken arguments reflect the speaker's true convictions

Customers’ emotions play a vital role in the service industry. The better frontline personnel understand the customer, the better the service they can provide. As human emotions generate certain (unintentional) bodily reactions, such as increase in heart rate, sweating, dilation, blushing and paling, which are measurable, artificial intelligence (AI) technologies can interpret these signals. Great progress has been made in recent years to automatically detect basic emotions like joy, anger etc. Complex emotions, consisting of multiple interdependent basic emotions, are more difficult to identify. One complex emotion which is of great interest to the service industry is difficult to detect: whether a customer is telling the truth or just a story… This research presents an AI-method for capturing and sensing emotional data. With an accuracy of around 98%, the best trained model was able to detect whether a participant of a debating challenge was arguing for or against her/his conviction, using speech analysis. The data set was collected in an experimental setting with 40 participants. The findings are applicable to a wide range of service processes and specifically useful for all customer interactions that take place via telephone. The algorithm presented can be applied in any situation where it is helpful for the agent to know whether a customer is speaking to her/his conviction. This could, for example, lead to a reduction in doubtful insurance claims, or untruthful statements in job interviews. This would not only reduce operational losses for service companies, but also encourage customers to be more truthful.
Nonoscillating vacuum states and the quantum homogeneity and isotropy hypothesis in loop quantum cosmology

We study the compatibility of the quantum homogeneitiy and isotropy hypothesis (QHIH), proposed by Ashtekar and Gupt to restrict the choice of vacuum state for the cosmological perturbations in Loop Quantum Cosmology (LQC), with the requirement that the selected vacuum should lead to a power spectrum that does not oscillate. We inspect in close detail the procedure that these authors followed to construct a set of states satisfying the QHIH, and how a preferred vacuum can be determined within this set. We find a step that is not univocally specified in this procedure, in relation with the replacement of the set of states that was originally allowed by the QHIH with an alternative set that is more manageable. In fact, the first of these sets does not contain the state that has been used in most of the implementations of the QHIH to the analysis of the power spectrum of the perturbations in LQC. We focus our attention on the original set picked out by the QHIH and investigate whether some of its elements may display a non-oscillatory behavior. We show that, to the extent to which the techniques used in this paper apply, this possibility is feasible. Thus, the two aforementioned criteria for the physical restriction of the vacuum state in LQC are compatible with each other and not exclusive.
Spontaneous chiral symmetry breaking in holographic soft wall models

We investigate non-linear extensions of the holographic soft wall model proposed by Karch, Katz, Son and Stephanov [1] including non-minimal couplings in the fivedimensional action. The non-minimal couplings bring a new parameter a0 which controls the transition between spontaneous and explicit symmetry breaking near the limit of massless quarks (the chiral limit). In the physical region (positive quark mass), we show that above a critical value of the parameter a0 the chiral condensate 〈q̄q〉 is finite in the chiral limit, signifying spontaneous chiral symmetry breaking. This result is supported by the lightest states arising in the spectrum of the pseudoscalar mesons, which become massless in the chiral limit and are therefore intrepreted as Nambu-Goldstone bosons. Moreover, the decay constants of the pseudoscalar mesons also support this conclusion, as well as the Gell-Mann-Oakes-Renner (GOR) relation satisfied by the lightest states. We also calculate the spectrum of scalar, vector, and axial-vector mesons with their corresponding decay constants. We describe the evolution of masses and decay constants with the increasing of the quark mass and for the physical mass we compare our results against available experimental data. Finally, we do not find instabilities in our model for the physical region (positive quark mass). ar X iv :2 10 7. 10 98 3v 2 [ he pph ] 1 1 A ug 2 02 1
Hamiltonian structure of the guiding-center Vlasov–Maxwell equations

The complementary Lagrangian and Hamiltonian formulations of dissipationless plasma equations have a rich history in plasma physics [1, 2]. In the Lagrangian formulation for the Vlasov-Maxwell equations, for example, one of several types of variational (Euler, Lagrange, or Euler-Poincaré) principles is used to derive these dissipationless plasma equations [3, 4], while their exact conservation laws can be derived through the Noether method. In the Hamiltonian formulation of dissipationless plasma equations, on the other hand, the equations for a given set of plasma fields ψ are, first, written in Hamiltonian form:
Making Reads in BFT State Machine Replication Fast, Linearizable, and Live

Practical Byzantine Fault Tolerance (PBFT) is a seminal state machine replication protocol that achieves a performance comparable to non-replicated systems in realistic environments. A reason for such high performance is the set of optimizations introduced in the protocol. One of these optimizations is read-only requests, a particular type of client request which avoids running the three-step agreement protocol and allows replicas to respond directly, thus reducing the latency of reads from five to two communication steps. Given PBFT's broad influence, its design and optimizations influenced many BFT protocols and systems that followed, e.g., BFT-SMaRt. We show, for the first time, that the read-only request optimization introduced in PBFT more than 20 years ago can violate its liveness. Notably, the problem affects not only the optimized read-only operations but also standard, totally-ordered operations. We show this weakness by presenting an attack in which a malicious leader blocks correct clients and present two solutions for patching the protocol, making read-only operations fast and correct. The two solutions were implemented on BFT-SMaRt and evaluated in different scenarios, showing their effectiveness in preventing the identified attack.