Miracle-dz/UniFAR-FTUs huggingface dataset:
Busted Sanctions

Powerful countries like the United States regularly employ economic sanctions as a tool for promoting their foreign policy interests. Yet this foreign policy tool has an uninspiring track record of success, with economic sanctions achieving their goals less than a third of the time they are imposed. The costs of these failed sanctions policies can be significant for the states that impose them, their targets, and the other countries they affect. Explaining economic sanctions' high failure rate therefore constitutes a vital endeavor for academics and policy-makers alike.Busted Sanctions seeks to provide this explanation, and reveals that the primary cause of this failure is third-party spoilers, or sanctions busters, who undercut sanctioning efforts by providing their targets with extensive foreign aid or sanctions-busting trade. In quantitatively and qualitatively analyzing over 60 years of U.S. economic sanctions, Bryan Early reveals that both types of third-party sanctions busters have played a major role in undermining U.S. economic sanctions. Surprisingly, his analysis also reveals that the United States' closest allies are often its sanctions' worst enemies. The book offers the first comprehensive explanation for why different types of sanctions busting occur and reveals the devastating effects it has on economic sanctions' chances of success.
Effect of transport duration, access to feed and water during transportation on growth performance and organ development of broilers

Amac: Bu calismanin amaci bir gunluk etlik civcivlerde tasima suresi ve tasima sirasinda yem ve su verilmesinin buyume performansi, sari kese agirligi, sindirim ve bagisiklik organlarinin gelismesi ve karkas verimine etkisini belirlemektir. Materyal ve Metot: Ticari bir kuluckahaneden 256 adet Cobb etlik civciv temin edilmistir. Civcivler rasgele 4 gruba ayrilmis ve 1.5 saat (DS) veya 6.0 saat (DL) boyunca yem ve su saglanmis (AFW) ya da saglanmamis (NFW) olarak tasimaya maruz birakilmislardir. Tasimadan sonra her grup 4 adet yer bolmesine yerlestirilmis (toplam 16 bolme) ve 42. gune kadar buyutulmustur. Bulgular: Sonuclar AFW uygulamasinin NFW’e kiyasla 42. gun canli agirligi iyilestirdigini, gogus eti, yem tuketimi ve 2. gun sari kese agirligini artirdigini, erken yasta (7 gun) bagisiklik ve sindirim organlarinin gelisimini iyilestirdigini gostermistir. Calisma ayni zamanda DL uygulamasinin DS’e gore cikis gunu canli agirligi dusurdugunu ve DL grubunun DS’e kiyasla ilk hafta yem tuketimini artirarak telafi edici buyume cabasi icinde olduguna isaret etmektedir. Ancak, DL grubu kesim yasinda DS grubunun canli agirligina ulasamamis ve daha kotu FCR elde edilmistir. Sonuc: Calisma,tasima suresinden bagimsiz olarak gunluk civcivlere tasima sirasinda yem ve su saglanmasinin etlik pilic performansini olumlu etkiledigini gostermistir.
Effect of defects and surface anchoring on the phase behavior of chiral smectic liquid crystals

ABSTRACT The influence of defects and surface effects on the stability of phenomena accompanying phase transitions, mainly coexistence phenomenon, has been studied. Experimental measurements were carried out by varying the cell thickness and using simple experimental on chiral smectic liquid crystal product. The analysis of the results with different cell thickness leads us to interpret the anomaly shown by our product related to impact of defects and surface effects. Moreover, the phase transition is affected by the trace of defects for thick cell and by the surface effect for thin cell. These effects have the same role as an electric local field which can induce phase transition.
Performance, organ growth and economics of finisher broiler fed neem (Azadirachta indica) leaf meal as replacement for maize

A 28-day feeding trial was conducted to evaluate the effects of dietary inclusion of neem (Azadirachta indica) leaf meal on the performance, organ growth and economics of finishing broilers. The harvested neem leaves were chopped and air dried under room temperature for ten days until they became crispy while retaining the greenish colouration. The dry leaves were hammer milled with 2mm sieve to produce leaf meal before being incorporated into broiler finisher diets. Four isonitrogenous finisher diets were formulated; containing 0%, 5%, 10% and 15% levels of neem leaf meal (NLM) as partial replacement for maize in the diets. A total of one hundred and eight (108) 4- week old birds were randomly selected, weighed and assigned to the four treatments in a complete randomised design. The four treatments were replicated three times with nine (9) birds per replicate. The birds were fed ad libitum with their respective diets and freely supplied with water. Results showed that the neem leaf levels significantly (P<0.05) affected all the performance indicators. Birds on 5% NLM had similar average daily weight gain, average daily feed intake and feed conversion ratio (FCR) with birds on 0% neem leaf meal (control) but significantly (P<0.05) superior to birds on 10% and 15% neem leaf meal levels. On the relative organ weight, except for the heart, all the organs were not significantly (P>0.05) affected by the varying levels of neem leaf meal. The g/kgbody weight of the heart reduces as the level of NLM increases; with 15% NLM having the least heart weight (7.50±0.47g/kgbody weight). The cost analysis of replacing maize with neem leaf meal in the broiler finisher diets revealed that 15% level yielded least cost of feed consumed per bird (? 178.69), cost of feed consumed per kg body weight per day (? 6.38) and cost of feed per kg weight (? 55.84). It is concluded that 5% replacement level of maize will enhance optimum performance and organ growth while 10% NLM brings about the optimum cost benefit in feeding finisher broilers with neem leaf meal.
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

Deep learning has brought great progress for the sequential recommendation (SR) tasks. With advanced network architectures, sequential recommender models can be stacked with many hidden layers, e.g., up to 100 layers on real-world recommendation datasets. Training such a deep network is difficult because it can be computationally very expensive and takes much longer time, especially in situations where there are tens of billions of user-item interactions. To deal with such a challenge, we present StackRec, a simple, yet very effective and efficient training framework for deep SR models by iterative layer stacking. Specifically, we first offer an important insight that hidden layers/blocks in a well-trained deep SR model have very similar distributions. Enlightened by this, we propose the stacking operation on the pre-trained layers/blocks to transfer knowledge from a shallower model to a deep model, then we perform iterative stacking so as to yield a much deeper but easier-to-train SR model. We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets. Extensive experiments show that StackRec achieves not only comparable performance, but also substantial acceleration in training time, compared to SR models that are trained from scratch. Codes are available at https://github.com/wangjiachun0426/StackRec.
From Forced Working-From-Home to Working-From-Anywhere: Two Revolutions in Telework

The COVID-19 outbreak has admittedly caused a major disruption worldwide. The interruptions to production, transportation, and mobility have clearly had a significant impact on the well-functioning of the global supply and demand chain. But what happened to the companies developing digital services, such as software. Were they interrupted as much or at all? And how has the enforced Working-From-Home mode impacted their ability to continue to deliver software? We hear that some managers are concerned that their engineers are not working effectively from home, or even lack the motivation to work in general, that teams lose touch and that managers do not notice when things go wrong. In this article, we share our findings from monitoring the situation in an international software company with engineers located in Sweden, USA, and the UK. We analyzed different aspects of productivity, such as developer satisfaction and well-being, activity, communication and collaboration, efficiency and flow based on the archives of commit data, calendar invites, and Slack communication, as well as the internal reports of WFH experiences and 18 interviews. We find that company engineers continue committing code and carry out their daily duties without significant disruptions, while their routines have gradually adjusted to the new norm with new emerging practices and various changes to the old ones. In a way, our message is that there is no news, which is good news. Yet, the experiences gained with the WFH of such scale have already made significant changes in the software industry's future, work from anywhere being an example of major importance.
Exploiting the Hidden Tasks of GANs: Making Implicit Subproblems Explicit

We present an alternative perspective on the training of generative adversarial networks (GANs), showing that the training step for a GAN generator decomposes into two implicit subproblems. In the first, the discriminator provides new target data to the generator in the form of"inverse examples"produced by approximately inverting classifier labels. In the second, these examples are used as targets to update the generator via least-squares regression, regardless of the main loss specified to train the network. We experimentally validate our main theoretical result and demonstrate significant improvements over standard GAN training made possible by making these subproblems explicit.
Fe xii and Fe xiii Line Widths in the Polar Off-limb Solar Corona up to 1.5 R ⊙

The nonthermal broadening of spectral lines formed in the solar corona is often used to seek evidence of Alfvén waves propagating in the corona. To have a better understanding of the variation of line widths at different altitudes, we measured the line widths of the strong Fe xii 192.4, 193.5, and 195.1 Å and Fe xiii 202.0 Å in an off-limb southern coronal hole up to 1.5 R ⊙ observed by the Extreme Ultraviolet Spectrometer on board the Hinode satellite. We compared our measurements to the predictions from the Alfvén Wave Solar Model (AWSoM) and the SPECTRUM module. We found that the Fe xii and Fe xiii line widths first increase monotonically below 1.1 R ⊙ and then keep fluctuating between 1.1 and 1.5 R ⊙. The synthetic line widths of Fe xii and Fe xiii below 1.3 R ⊙ are notably lower than the observed ones. We found that the emission from a streamer in the line of sight significantly contaminates the coronal hole line profiles even up to 1.5 R ⊙ both in observations and simulations. We suggest that either the discrepancy between the observations and simulations is caused by insufficient nonthermal broadening at the streamer in the AWSoM simulation or the observations are less affected by the streamer. Our results emphasize the importance of identifying the origin of the coronal EUV emission in off-limb observations.
Antibody ligation of carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), CEACAM3, and CEACAM6, differentially enhance the cytokine release of human neutrophils in responses to Candida albicans

Invasive candidiasis, often caused by Candida albicans, is an important healthcare-associated fungal infection that results in a high mortality rate of up to 40%. Neutrophils are the first line of defense during Candida infections. They can initiate various killing mechanisms and release cytokines to attract further immune cells to the site of infection. These responses are tightly controlled, since they can also lead to severe tissue/organ damage. We hypothesized that the regulation of C. albicans-specific neutrophil functions by the immunoregulatory C. albicans receptors CEACAM1, CEACAM3, and CEACAM6 are involved in the immune pathology of candidemia. Here, we analyzed the effects of the specific antibodies B3-17, 308/3-3, and 1H7-4B, respectively, targeting the three CEACAM receptors on C. albicans-induced neutrophil responses. We show that ligation of CEACAM6 significantly enhanced the response to C. albicans, as evidenced by the increased CXCL8/IL-8 secretion. By assessing the transcriptional responses, we found that CEACAM6 ligation and to some extent CEACAM1 ligation, but not CEACAM3 ligation, resulted in altered gene regulation of the C. albicans-stimulated neutrophils. Genes that were differentially regulated by the different CEACAM-targeting antibodies were analyzed for affected cellular processes and signaling pathways using various bioinformatics methods, including integrated network analyses and dynamic simulations of signaling cascades. Predicted changes in cellular pathways and cellular functions included CEACAM-specific alterations in apoptosis and cytokine secretion. In particular, we verified predicted changes in IL-1β/IL-6 expression in response to the antibody ligation of all three targeted CEACAMs and apoptosis induction by anti-CEACAM6 antibody treatment in presence of C. albicans stimulation. Specifically, CEACAM6 ligation by 1H7-4B enhanced neutrophil apoptosis and increased immediate and long-term cytokine release in responses to C. albicans. CEACAM1 ligation by B3-17 mainly enhanced the immediate secretion of pro-inflammatory cytokines, and CEACAM3 ligation by 308/3-3 increased the long-term release of pro-inflammatory cytokines. Taken together, we demonstrated for the first time that CEACAM receptors have an important and differential impact on the regulation of C. albicans-induced immune functions in human neutrophils.
More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks

Topics in conversations depend in part on the type of interpersonal relationship between speakers, such as friendship, kinship, or romance. Identifying these relationships can provide a rich description of how individuals communicate and reveal how relationships influence the way people share information. Using a dataset of more than 9.6M dyads of Twitter users, we show how relationship types influence language use, topic diversity, communication frequencies, and diurnal patterns of conversations. These differences can be used to predict the relationship between two users, with the best predictive model achieving a macro F1 score of 0.70. We also demonstrate how relationship types influence communication dynamics through the task of predicting future retweets. Adding relationships as a feature to a strong baseline model increases the F1 and recall by 1% and 2%. The results of this study suggest relationship types have the potential to provide new insights into how communication and information diffusion occur in social networks.
Evolutionary instability of selfish learning in repeated games

Abstract Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one’s own success. However, when two such “selfish” learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner’s dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness.
Low Complexity Secure State Estimation Design for Linear System with Non-derogatory Dynamics

We consider the problem of estimating the state of a linear Gaussian system in the presence of integrity attacks. The attacker can compromise p out of m sensors, the set of which is fixed and unknown to the system operator, and manipulate the measurements arbitrarily. Under the assumption that all the eigenvalues of system matrix A have geometric multiplicity 1 (A is non-derogatory), we propose a secure estimation scheme that is resilient to integrity attack as long as the system is 2p-sparse observable. In the absence of attack, the proposed estimation coincides with Kalman estimation with a certain probability that can be adjusted. Furthermore, our proposed estimator is computational efficient during the security condition checking in the designing phase and during the estimation computing in the online operating phase. A numerical example is provided to corroborate the results and illustrate the performance of the proposed estimator.
A Deep Polarimetric Study of the Asymmetrical Debris Disk HD 106906

Located in the Lower Centaurus Crux group, HD 106906 is a young, binary stellar system. This system is unique among discovered systems in that it contains an asymmetrical debris disk, as well as an 11 M Jup planet companion, at a separation of ∼735 au. Only a handful of other systems are known to contain both a disk and a directly imaged planet, where HD 106906 is the only one in which the planet has apparently been scattered. The debris disk is nearly edge-on and extends to roughly >500 au, where previous studies with the Hubble Space Telescope have shown the outer regions to have high asymmetry. To better understand the structure and composition of the disk, we have performed a deep polarimetric study of HD 106906's asymmetrical debris disk using newly obtained H-, J-, and K1-band polarimetric data from the Gemini Planet Imager. An empirical analysis of our data supports a disk that is asymmetrical in surface brightness and structure, where fitting an inclined ring model to the disk spine suggests that the disk may be highly eccentric (e ≳ 0.16). A comparison of the disk flux with the stellar flux in each band suggests a blue color that also does not significantly vary across the disk. We discuss these results in terms of possible sources of asymmetry, where we find that dynamical interaction with the planet companion, HD 106906b, is a likely candidate.
Evaluating Rotation Periods of M Dwarfs across the Ages

In this work we examine M dwarf rotation rates at a range of ages to establish benchmarks for M dwarf gyrochronology. This work includes a sample of 713 spectroscopically classified M0–M8 dwarfs with new rotation rates measured from K2 light curves. We analyze data and recover rotation rates for 179 of these objects. We add these to rotation rates for members of clusters with known ages (5–700 Myr), as well as objects assumed to have field ages (≳1 Gyr). We use Gaia DR2 parallax and (G–GRP) photometry to create color–magnitude diagrams to compare objects across samples. We use color–period plots to analyze the period distributions across age, as well as incorporate Hα equivalent width and tangential velocity where possible to further comment on age dependence. We find that the age of transition from rapid to slow rotation in clusters, which we define as an elbow in the period–color plots, depends on spectral type. Later spectral types transition at older ages: M4 for Praesepe at ≈700 Myr, one of the oldest clusters for which M dwarf rotation rates have been measured. The transition from active to inactive Hα equivalent width also occurs at this elbow, as objects transition from rapid rotation to the slowly rotating sequence. Redder or smaller stars remain active at older ages. Finally, using Gaia kinematics we find evidence for rotation stalling for late Ms in the field sample, suggesting the transition happens much later for mid- to late-type M dwarfs.
Sufficient reasons for classifier decisions in the presence of constraints

Recent work has unveiled a theory for reasoning about the decisions made by binary classifiers: a classifier describes a Boolean function, and the reasons behind an instance being classified as positive are the prime-implicants of the function that are satisfied by the instance. One drawback of these works is that they do not explicitly treat scenarios where the underlying data is known to be constrained, e.g., certain combinations of features may not exist, may not be observable, or may be required to be disregarded. We propose a more general theory, also based on prime-implicants, tailored to taking constraints into account. The main idea is to view classifiers in the presence of constraints as describing partial Boolean functions, i.e., that are undefined on instances that do not satisfy the constraints. We prove that this simple idea results in reasons that are no less (and sometimes more) succinct. That is, not taking constraints into account (e.g., ignored, or taken as negative instances) results in reasons that are subsumed by reasons that do take constraints into account. We illustrate this improved parsimony on synthetic classifiers and classifiers learned from real data.
Global assessment of oil and gas methane ultra-emitters

Methane emissions from oil and gas (O&G) production and transmission represent a considerable contribution to climate change. These emissions comprise sporadic releases of large amounts of methane during maintenance operations or equipment failures not accounted for in current inventory estimates. We collected and analyzed hundreds of very large releases from atmospheric methane images sampled by the TROPOspheric Monitoring Instrument (TROPOMI) between 2019 and 2020. Ultra-emitters are primarily detected over the largest O&G basins throughout the world. With a total contribution equivalent to 8 to 12% (~8 million metric tons of methane per year) of the global O&G production methane emissions, mitigation of ultra-emitters is largely achievable at low costs and would lead to robust net benefits in billions of US dollars for the six major O&G-producing countries when considering societal costs of methane. Description Ultra smart Methane emissions from oil and gas production and transmission make a significant contribution to climate change. Lauvaux et al. used observations from the satellite platform TROPOMI to quantify very large releases of atmospheric methane by oil and gas industry ultra-emitters (see the Perspective by Vogel). They calculate that these sources represent as much as 12% of global methane emissions from oil and gas production and transmission and note that mitigation of their emissions can be done at low cost. This would be an effective strategy to economically reduce the contribution of this industry to climate change. —HJS Reducing methane emissions from ultra-emitters is an economical way to mitigate their contribution to climate change.
Causally motivated shortcut removal using auxiliary labels

Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning. We study a flexible, causally-motivated approach to training robust predictors by discouraging the use of specific shortcuts, focusing on a common setting where a robust predictor could achieve optimal \emph{iid} generalization in principle, but is overshadowed by a shortcut predictor in practice. Our approach uses auxiliary labels, typically available at training time, to enforce conditional independences implied by the causal graph. We show both theoretically and empirically that causally-motivated regularization schemes (a) lead to more robust estimators that generalize well under distribution shift, and (b) have better finite sample efficiency compared to usual regularization schemes, even when no shortcut is present. Our analysis highlights important theoretical properties of training techniques commonly used in the causal inference, fairness, and disentanglement literatures. Our code is available at https://github.com/mymakar/causally_motivated_shortcut_removal