Miracle-dz/UniFAR-FTUs huggingface dataset:
IMPACT OF ECONOMIC SANCTIONS ON THE FOOD PRODUCTION AND CONSUMPTION IN RUSSIA O.I. Khairullina, Doctor of Economics, Professor of Accounting and Finance Department of the Federal State Budgetary Educational Institution of Higher Education «Perm State Agro-Technological University named after Academi

At the present stage, the study of the impact of sanctions on the economy of various countries is becoming more and more relevant. A retrospective analysis of Russia's international relations with other countries suggests that the first sanctions date back to 1930. Therefore, for Russia, the conditions created are not something new. Modern sanctions are more personalized and have a sectoral focus. In Russia, the introduction of a food embargo has contradictory results. There are both positive and negative sides. By calculating the growth of the minimum food basket cost index and the physiological needs satisfaction rating for the population of the Russian Federation in food and other indicators, it was determined that the sanctions had a negative impact on the economic availability of food for the population, while the volume of domestic production could not fully compensate for the temporarily created deficit. The position of the agrarian sector in the Russian economy remains unimportant, as evidenced by the indicators of the share of agriculture in GDP and its share in total investments. The extensive development path does not allow attracting sufficient investments for technical and technological modernization of the agro-industrial production. As a result, there was a rise in food prices, replacement of major food importers, food quality reduction. The use of decile groups of the population in terms of income has led to the conclusion that the consumption of agricultural and food products has decreased by the first group with the lowest income level, which is approximately 29.4 million people. There is a lack of protein and carbohydrate intake. The turnover of substandard and falsified food products is growing. In such circumstances, the continuation of the food embargo by Russia requires careful study. Attention should be paid to the mechanisms of food distribution and monetary compensation within the framework of domestic food aid to low-income categories of the Russian population.
Endogenous Risk Attitudes

In a model inspired by neuroscience, we show that constrained optimal perception encodes lottery rewards using an S-shaped encoding function and over-samples lowprobability events. The implications of this perception strategy for behavior depend on the decision-maker’s understanding of the risk. The strategy does not distort choice in the limit as perception frictions vanish when the decision-maker fully understands the decision problem. If, however, the decision-maker underrates the complexity of the decision problem, then risk attitudes reflect properties of the perception strategy even for vanishing perception frictions. The model explains adaptive risk attitudes and probability weighting as in prospect theory and, additionally, predicts that risk attitudes are strengthened by time pressure and attenuated by anticipation of large risks. ∗We have benefited from comments of Cary Frydman, Ryan Webb and various seminar audiences. Giorgi Chavchanidze and Pavel Ilinov provided excellent research assistantship. This work was supported by the Alfred P. Sloan Foundation and the NOMIS Foundation (Netzer), by SSHRC grant 435-2013-0426 (Robson) and by ERC grant 770652 (Steiner).
Ethnobotanical study of medicinal plants used by local communities in Sekerak Subdistrict, Aceh Tamiang, Indonesia

Abstract. Navia ZI, Suwardi AB, Baihaqi. 2021. Ethnobotanical study of medicinal plants used by local communities in Sekerak Subdistrict, Aceh Tamiang, Indonesia. Biodiversitas 22: 4273-4281. Local communities in Aceh Tamiang have gained a wealth of medicinal knowledge through practice and experience in their long-term battles with the disease. However, because of a lack of written records and rapid economic development, their traditional medicinal knowledge is under threat. This study investigated medicinal plants and related traditional knowledge of local communities in the Sekerak subdistrict, Aceh Taming, Indonesia. Field surveys, plant collections, and interviews with communities were used in this study. The Snowball Sampling technique was used to select 60 informants for the interviews. A total of 46 medicinal plant species belonging to 40 genera and 26 families were used for medicinal purposes by local communities to treat 28 different diseases. The itch, swelling, cough, and nosebleed were the most commonly treated. With a use-value index of 0.98, Tagetes erecta was the most commonly used medicinal plant by local communities. In addition, local communities identified the leaf (50%) as the most widely used plant part and oral administration (65%) as the most common method of administering traditional medicine. Elders have more knowledge of medicinal plants than younger generations, indicating that traditional knowledge is eroding across generations. However, initiatives to promote and conserve medicinal plants must be improved, particularly among the younger generation. This is required to ensure the availability of medicinal plants and the preservation of traditional knowledge in the future.
The Costs and Benefits of Liquidity Regulations

We find that banks subject to the Liquidity Coverage Ratio (LCR banks) create less liquidity per dollar of assets in the post-LCR period than non-LCR banks by, in part, lending less. However, we also find that LCR banks are more resilient as they contribute less to fire-sale risk, relative to non-LCR banks. We estimate the net after-tax benefits from reduced lending and fire-sale risk to be about 1.4 percent of assets in 2013:Q2-2014 for large banks. Our findings, which we show are unlikely to result from capital regulations, highlight the trade-off between lower liquidity creation and greater resilience from liquidity regulations.
The ECB's Tracker: Nowcasting the Press Conferences of the ECB

This paper proposes an econometric framework for nowcasting the monetary policy stance and decisions of the European Central Bank (ECB) exploiting the ow of conventional and textual data that become available between two consecutive press conferences. Decompositions of the updated nowcasts into variables' marginal contribution are also provided to shed light on the main drivers of the ECB's reaction function at every point in time. In out-of-sample nowcasting experiments, the model provides an accurate tracking of the ECB monetary policy stance and decisions. The inclusion of textual variables contributes significantly to the gradual improvement of the model performance.
Sources of value creation in private equity buyouts of private firms

Despite their prevalence, little is known about sources of value creation in private equity buyouts of private firms. We provide evidence that acquirers disproportionately target two types of private firms – those with the weakest operating profitability, and profitable firms with growth potential that are highly levered and depend on external financing. Target firms – especially those undertaking add-on acquisitions – grow rapidly post-buyout, and profitability increases for both profitable and unprofitable targets. Our evidence suggests PE firms create value by relaxing financing constraints for firms with strong investment opportunities and improving performance of weak firms, while financial engineering plays a limited role.
Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis

Synthesizing high-quality, realistic images from text-descriptions is a challenging task, and current methods synthesize images from text in a multi-stage manner, typically by first generating a rough initial image and then refining image details at subsequent stages. However, existing methods that follow this paradigm suffer from three important limitations. Firstly, they synthesize initial images without attempting to separate image attributes at a word-level. As a result, object attributes of initial images (that provide a basis for subsequent refinement) are inherently entangled and ambiguous in nature. Secondly, by using common text-representations for all regions, current methods prevent us from interpreting text in fundamentally different ways at different parts of an image. Different image regions are therefore only allowed to assimilate the same type of information from text at each refinement stage. Finally, current methods generate refinement features only once at each refinement stage and attempt to address all image aspects in a single shot. This single-shot refinement limits the precision with which each refinement stage can learn to improve the prior image. Our proposed method introduces three novel components to address these shortcomings: (1) An initial generation stage that explicitly generates separate sets of image features for each word n-gram. (2) A spatial dynamic memory module for refinement of images. (3) An iterative multi-headed mechanism to make it easier to improve upon multiple image aspects. Experimental results demonstrate that our Multi-Headed Spatial Dynamic Memory image refinement with our Multi-Tailed Word-level Initial Generation (MSMT-GAN) performs favourably against the previous state of the art on the CUB and COCO datasets.
Multimodal Emotion-Cause Pair Extraction in Conversations

Conversation is an important form of human communication and contains a large number of emotions. It is interesting to discover emotions and their causes in conversations. Conversation in its natural form is multimodal. Many studies have been carried out on multimodal emotion recognition in conversations, yet there is still a lack of work on multimodal emotion cause analysis. In this article, we introduce a new task named Multimodal Emotion-Cause Pair Extraction in Conversations, aiming to jointly extract emotions and the corresponding causes from conversations reflected in multiple modalities (i.e., text, audio and video). We accordingly construct a multimodal conversational emotion cause dataset, Emotion-Cause-in-Friends, which contains 9,794 multimodal emotion-cause pairs among 13,619 utterances in the Friends sitcom. We benchmark the task by establishing two baseline systems including a heuristic approach considering inherent patterns in the location of causes and emotions and a deep learning approach that incorporates multimodal features for emotion-cause pair extraction, and conduct the human performance test for comparison. Furthermore, we investigate the effect of multimodal information, explore the potential of incorporating commonsense knowledge, and perform the task under both Static and Real-time settings.
A horizontal-strip LLT polynomial is determined by its weighted graph

We prove that two horizontal-strip LLT polynomials are equal if the associated weighted graphs defined by the author in a previous paper are isomorphic. This provides a sufficient condition for equality of horizontal-strip LLT polynomials and yields a well-defined LLT polynomial indexed by a weighted graph. We use this to prove some new relations between LLT polynomials and we explore a connection with extended chromatic symmetric functions.
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics

Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change. Such choices affect which changes the algorithms have most power to detect. We introduce an algorithm, Functional Online CuSUM (FOCuS), which is equivalent to running these earlier methods simultaneously for all sizes of window, or all possible values for the size of change. Our theoretical results give tight bounds on the expected computational cost per iteration of FOCuS, with this being logarithmic in the number of observations. We show how FOCuS can be applied to a number of different change in mean scenarios, and demonstrate its practical utility through its state-of-the art performance at detecting anomalous behaviour in computer server data.
Hindsight Network Credit Assignment

Training neural networks with discrete stochastic variables presents a unique challenge. Backpropagation is not directly applicable, nor are the reparameterization tricks used in networks with continuous stochastic variables. To address this challenge, we present Hindsight Network Credit Assignment (HNCA), a novel gradient estimation algorithm for networks of discrete stochastic units. HNCA works by assigning credit to each unit based on the degree to which its output influences its immediate children in the network. We prove that HNCA produces unbiased gradient estimates with reduced variance compared to the REINFORCE estimator, while the computational cost is similar to that of backpropagation. We first apply HNCA in a contextual bandit setting to optimize a reward function that is unknown to the agent. In this setting, we empirically demonstrate that HNCA significantly outperforms REINFORCE, indicating that the variance reduction implied by our theoretical analysis is significant and impactful. We then show how HNCA can be extended to optimize a more general function of the outputs of a network of stochastic units, where the function is known to the agent. We apply this extended version of HNCA to train a discrete variational auto-encoder and empirically show it compares favourably to other strong methods. We believe that the ideas underlying HNCA can help stimulate new ways of thinking about efficient credit assignment in stochastic compute graphs.
Pyramid Correlation based Deep Hough Voting for Visual Object Tracking

Most of the existing Siamese-based trackers treat tracking problem as a parallel task of classification and regression. However, some studies show that the sibling head structure could lead to suboptimal solutions during the network training. Through experiments we find that, without regression, the performance could be equally promising as long as we delicately design the network to suit the training objective. We introduce a novel voting-based classification-only tracking algorithm named Pyramid Correlation based Deep Hough Voting (short for PCDHV), to jointly locate the top-left and bottom-right corners of the target. Specifically we innovatively construct a Pyramid Correlation module to equip the embedded feature with fine-grained local structures and global spatial contexts; The elaborately designed Deep Hough Voting module further take over, integrating long-range dependencies of pixels to perceive corners; In addition, the prevalent discretization gap is simply yet effectively alleviated by increasing the spatial resolution of the feature maps while exploiting channel-space relationships. The algorithm is general, robust and simple. We demonstrate the effectiveness of the module through a series of ablation experiments. Without bells and whistles, our tracker achieves better or comparable performance to the SOTA algorithms on three challenging benchmarks (TrackingNet, GOT-10k and LaSOT) while running at a real-time speed of 80 FPS. Codes and models will be released.
Credit Union Regulations’ Mysterious Hold on Thrifts and Community Banks

The continued operation of credit unions in the 2008-2010 regulatory framework remained a source of debate in the financial sector. Competing thrifts argued for fair regulatory treatment while credit unions argued for the relaxation of restrictions on their scale and scope of operation. We provide a fresh perspective by building on central place theory and offering a family of location models for the U.S. credit unions. Our results showed that credit unions operated in areas with a low concentration of retail banks. This finding was evidence that credit unions serve niche markets and they were not a significant source of direct competition for thrifts and community banks. This may signal an increase in credit union formation in a post-pandemic world.
Certified Patch Robustness via Smoothed Vision Transformers

Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of degraded standard accuracies and slower inference times. We demonstrate how using vision transformers enables significantly better certified patch robustness that is also more computationally efficient and does not incur a substantial drop in standard accuracy. These improvements stem from the inherent ability of the vision transformer to gracefully handle largely masked images.11Our code is available at https://github.com/MadryLab/smoothed-vit.
Phase transitions in when feedback is useful

Sensory observations about the world are invariably ambiguous. Inference about the world's latent variables is thus an important computation for the brain. However, computational constraints limit the performance of these computations. These constraints include energetic costs for neural activity and noise on every channel. Efficient coding is one prominent theory that describes how such limited resources can best be used. In one incarnation, this leads to a theory of predictive coding, where predictions are subtracted from signals, reducing the cost of sending something that is already known. This theory does not, however, account for the costs or noise associated with those predictions. Here we offer a theory that accounts for both feedforward and feedback costs, and noise in all computations. We formulate this inference problem as message-passing on a graph whereby feedback serves as an internal control signal aiming to maximize how well an inference tracks a target state while minimizing the costs of computation. We apply this novel formulation of inference as control to the canonical problem of inferring the hidden scalar state of a linear dynamical system with Gaussian variability. The best solution depends on architectural constraints, such as Dale's law, the ubiquitous law that each neuron makes solely excitatory or inhibitory postsynaptic connections. This biological structure can create asymmetric costs for feedforward and feedback channels. Under such conditions, our theory predicts the gain of optimal predictive feedback and how it is incorporated into the inference computation. We show that there is a non-monotonic dependence of optimal feedback gain as a function of both the computational parameters and the world dynamics, leading to phase transitions in whether feedback provides any utility in optimal inference under computational constraints.
Star-forming Dwarf Galaxies in Filamentary Structures around the Virgo Cluster: Probing Chemical Pre-processing in Filament Environments

It has been proposed that the filament environment is closely connected to the pre-processing of galaxies, where their properties may have been changed by environmental effects in the filament before they fell into the galaxy cluster. We present the chemical properties of star-forming dwarf galaxies (SFDGs) in five filamentary structures (Virgo III, Leo Minor, Leo II A, Leo II B, and Canes Venatici) around the Virgo cluster using the Sloan Digital Sky Survey optical spectroscopic data and Galaxy Evolution Explorer ultraviolet photometric data. We investigate the relationship between stellar mass, gas-phase metallicity, and specific star formation rate (sSFR) of the SFDGs in the Virgo filaments in comparison to those in the Virgo cluster and field. We find that, at a given stellar mass, SFDGs in the Virgo filaments show lower metallicity and higher sSFR than those in the Virgo cluster on average. We observe that SFDGs in the Virgo III filament show enhanced metallicities and suppressed star formation activities comparable to those in the Virgo cluster, whereas SFDGs in the other four filaments exhibit similar properties to the field counterparts. Moreover, about half of the galaxies in the Virgo III filament are found to be morphologically transitional dwarf galaxies that are supposed to be on the way to transforming into quiescent dwarf early-type galaxies. Based on the analysis of the galaxy perturbation parameter, we propose that the local environment represented by the galaxy interactions might be responsible for the contrasting features in chemical pre-processing found in the Virgo filaments.
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models

Knowledge probing is crucial for understanding the knowledge transfer mechanism behind the pre-trained language models (PLMs). Despite the growing progress of probing knowledge for PLMs in the general domain, specialised areas such as the biomedical domain are vastly under-explored. To facilitate this, we release a well-curated biomedical knowledge probing benchmark, MedLAMA, constructed based on the Unified Medical Language System (UMLS) Metathesaurus. We test a wide spectrum of state-of-the-art PLMs and probing approaches on our benchmark, reaching at most 3% of acc@10. While highlighting various sources of domain-specific challenges that amount to this underwhelming performance, we illustrate that the underlying PLMs have a higher potential for probing tasks. To achieve this, we propose Contrastive-Probe, a novel self-supervised contrastive probing approach, that adjusts the underlying PLMs without using any probing data. While Contrastive-Probe pushes the acc@10 to 28%, the performance gap still remains notable. Our human expert evaluation suggests that the probing performance of our Contrastive-Probe is still under-estimated as UMLS still does not include the full spectrum of factual knowledge. We hope MedLAMA and Contrastive-Probe facilitate further developments of more suited probing techniques for this domain. Our code and dataset are publicly available at https://github.com/cambridgeltl/medlama.
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?

Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages. Learning a single model can enhance the low-resource translation by leveraging data from multiple languages. However, the performance of an MNMT model is highly dependent on the type of languages used in training, as transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. In this paper, we propose a Hierarchical Knowledge Distillation (HKD) approach for MNMT which capitalises on language groups generated according to typological features and phylogeny of languages to overcome the issue of negative transfer. HKD generates a set of multilingual teacher-assistant models via a selective knowledge distillation mechanism based on the language groups, and then distils the ultimate multilingual model from those assistants in an adaptive way. Experimental results derived from the TED dataset with 53 languages demonstrate the effectiveness of our approach in avoiding the negative transfer effect in MNMT, leading to an improved translation performance (about 1 BLEU score on average) compared to strong baselines.