Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Bodily Action Shape Spatial Representation? Evidence from Virtual Reality, Sensory Augmentation and Map Learning

Spatial relations can be defined with respect to the body (egocentric) or among environmental objects only (allocentric). Egocentric relations are necessarily transformed through bodily action. To what extent allocentric cognitive representations are shaped by the body remains unclear. In our study, participants navigate a virtual-reality (VR) city over multiple days in one of three embodiment conditions. In two VR conditions, the participants sit on a swivel chair actively changing navigation direction through bodily rotation. In one of these groups the VR participants wear a sensory augmentation belt which indicates the cardinal direction of north through vibration. The third group of participants navigates a two-dimensional map of the city. After each exploration session, participants complete tasks asking for allocentric spatial relations. We find that the performance in the spatial tasks interacts with the duration of exploration time and the embodiment condition. These findings indicate allocentric spatial representations to be structured by bodily action.
BTSP, not STDP, Drives Shifts in Hippocampal Representations During Familiarization

Synaptic plasticity is widely thought to support memory storage in the brain, but the rules determining impactful synaptic changes in-vivo are not known. We considered the trial-by-trial shifting dynamics of hippocampal place fields (PFs) as an indicator of ongoing plasticity during memory formation. By implementing different plasticity rules in computational models of spiking place cells and comparing to experimentally measured PFs from mice navigating familiar and novel environments, we found that Behavioral-Timescale-Synaptic-Plasticity (BTSP), rather than Hebbian Spike-Timing-Dependent-Plasticity, is the principal mechanism governing PF shifting dynamics. BTSP-triggering events are rare, but more frequent during novel experiences. During exploration, their probability is dynamic: it decays after PF onset, but continually drives a population-level representational drift. Finally, our results show that BTSP occurs in CA3 but is less frequent and phenomenologically different than in CA1. Overall, our study provides a new framework to understand how synaptic plasticity shapes neuronal representations during learning.
Neural correlates of crowding in macaque area V4

Visual crowding refers to the phenomenon where a target object that is easily identifiable in isolation becomes difficult to recognize when surrounded by other stimuli (distractors). Extensive psychophysical studies support two alternative possibilities for the underlying mechanisms. One hypothesis suggests that crowding results from the loss of visual information due to pooled encoding of multiple nearby stimuli in the mid-level processing stages along the ventral visual pathway. Alternatively, crowding may arise from limited resolution in decoding object information during recognition and the encoded information may remain inaccessible unless it is salient. To rigorously test these alternatives, we studied the responses of single neurons in macaque area V4, an intermediate stage of the ventral, object-processing pathway, to parametrically designed crowded displays and their texture-statistics matched metameric counterparts. Our investigations reveal striking parallels between how crowding parameters, e.g., number, distance, and position of distractors, influence human psychophysical performance and V4 shape selectivity. Importantly, we found that enhancing the salience of a target stimulus could reverse crowding effects even in highly cluttered scenes and such reversals could be protracted reflecting a dynamical process. Overall, we conclude that a pooled encoding of nearby stimuli cannot explain the observed responses and we propose an alternative model where V4 neurons preferentially encode salient stimuli in crowded displays. Significance Statement Psychophysicists have long studied the phenomena of visual crowding, but the underlying neural mechanisms are unknown. Our results reveal striking correlations between the responses of neurons in mid-level visual cortical area V4 and psychophysical demonstrations, revealing that crowding is influenced not only by the number and spatial arrangement of distractors but also by the similarity of features between target and distractors, as well as among the distractors themselves. Overall, our studies provide strong evidence that the visual system uses strategies to preferentially encode salient features in a visual scene presumably to process visual information efficiently. When multiple nearby stimuli are equally salient, the phenomenon of crowding ensues.
A General Single-Cell Analysis Framework via Conditional Diffusion Generative Models

The fast-growing single-cell analysis community extends the horizon of quantitative analysis to numerous computational tasks. While the tasks hold vastly different targets from each other, existing works typically design specific model frameworks according to the downstream objectives. In this work, we propose a general single-cell analysis framework by unifying common computational tasks as posterior estimation problems. In light of conditional diffusion generative models, we introduce scDiff through the proposed framework and study different conditioning strategies. With data-specific conditions, scDiff achieves competitive performance against state-of-the-art in various benchmarking tasks. In addition, we illustrate the flexibility of scDiff by incorporating prior information through large language models and graph neural networks. Additional few-shot and zero-shot experiments prove the effectiveness of the prior conditioner on scDiff. Our implementation is publicly available at https://github.com/OmicsML/scDiff.
Macroeconomic Policy: Inflation, Wealth and the Exchange Rate

Part 1 Theory: a new Keynesian framework for macroeconomic policy the linkages between financial weapons and financial targets - a comparative static analysis the dynamics of price stabilization. Part 2 Application: a stock-flow model with model-consistent or regressive expectations macroeconomic policy rules for economic stabilization counterfactual simulation with forward-looking expectations a simulation of the cost-push economy the controlled economy with reformed wages. Part 3 Method: the derivation and use of linear model the design of economic policy rules. Part 4 Conclusion: summary of results and conclusions.
Named Entity Recognition and Classification on Historical Documents: A Survey

After decades of massive digitisation, an unprecedented amount of historical documents is available in digital format, along with their machine-readable texts. While this represents a major step forward with respect to preservation and accessibility, it also opens up new opportunities in terms of content mining and the next fundamental challenge is to develop appropriate technologies to efficiently search, retrieve and explore information from this 'big data of the past'. Among semantic indexing opportunities, the recognition and classification of named entities are in great demand among humanities scholars. Yet, named entity recognition (NER) systems are heavily challenged with diverse, historical and noisy inputs. In this survey, we present the array of challenges posed by historical documents to NER, inventory existing resources, describe the main approaches deployed so far, and identify key priorities for future developments.
Implementation of a Portable Nested Data-Parallel Language

This paper gives an overview of the implementation of NESL, a portable nested data-parallel language. This language and its implementation are the first to fully support nested data structures as well as nested data-parallel function calls. These features allow the concise description of parallel algorithms on irregular data structures, such as sparse matrices and graphs. In addition, they maintain the advantages of data-parallel languages: a simple programming model and portability. The current NESL implementation is based on an intermediate language called VCODE and a library of vector routines called CVL. It runs on the Connection Machines CM-2 and CM-5, the Cray C90, and serial workstations. We compare initial benchmark results of NESL with those of machine-specific code on these machines for three algorithms: least-squares linefitting, median finding, and a sparse-matrix vector product. These results show that NESL’s performance is competitive with that of machine-specific code for regular dense data, and is often superior for irregular data.
Zero pollution air powered engine: A Review

Internal-combustion engines pollute the environment seriously, and consume enormous nonrenewable energy so Today the whole world is in search of alternative fuel and There are couples of option of alternative fuel such as solar power, tidal power, geothermal power, etc. and one of them is Compressed Air. The air engine runs on air only so no needs any Fossil fuels and carburetor. The present paper gives a brief description on zero pollution compressed air engine. As we are going to convert the already existing conventional engine into an air powered one, this new technology is easy to adapt and another benefit is that it uses air as fuel which is available abundantly in atmosphere. This technology is cheaper in cost and maintenance and it doesn’t cause any kind of harm to the environment. Thus it is surely a revolutionary mode of transport.
Optimizing thin‐layer mapping through Spectral Inversion: Performance of Genetic Algorithms and Simulated Annealing

In order to increase the resolution of seismic data used to detect thin-beds, we implemented the Spectral Inversion method (Puryear and Castagna, 2008) which examines the Thin-Bed Tuning response in the frequency domain. Our approach included minimization of the objective function through the application of Genetic Algorithms (GA) and Simulated Annealing (SA). We tested our implementation on synthetic and real seismic data from the Barco Formation, deposited in Catatumbo basin of Colombia. Our results indicate that these two algorithms outperform traditional minimization schemes.
A high level simulator integrated with the Mirv compiler

A program’s execution profile is an increasingly important source of information for optimizations. Along with its use for high-level optimizations, profile information can be used to take advantage of advanced ISA features such as branch hint bits. Profile information is collected either by instrumenting an executable with profile collection code or by running the program in a simulator. Instrumentation has the advantage that it is relatively fast, with the disadvantage that the instrumentation code can interfere with the measurements (e.g. branch prediction). Traditional simulation is slower but a more accurate process. However, since most simulators simulate the low level machine code of a program, relating the results to the high-level program structure can be problematic. MirvSim differs from other simulators – such as SimpleScalar – that are used in computer architecture in that instead of simulating a machine code level instruction set, it simulates the Mirv language, a high-level intermediate program representation (IR). This allows the simulator to be closely coupled with the rest of the compiler infrastructure and to communicate information with other compiler passes easily.
Rail Costs and Capital Adjustments in a Quasi-regulated Environment Rail Costs and Capital Adjustments in a Quasi-regulated Environment Rail Costs and Capital Adjustments in a Quasi-regulated Environment

This paper reports on results obtained from estimation of a rail cost function using a pooled time-series cross section of Class I U.S. railroads for the period 1973-1986. Based on the results of this cost function, an analysis is performed of short-run and long-run returns to scale, and adjustments in way and structure capital in the heavily regulated and quasi regulated environments. In general, it is found that there is considerable overcapitalization in the rail industry, and that this has persisted in spite of the regulatory freedom to abandon track and service provided by the Staggers Act.
Genome-wide CRISPR screening reveals genes essential for cell viability and resistance to abiotic and biotic stresses in Bombyx mori

High-throughput genetic screens are powerful methods to interrogate gene function on a genome-wide scale and identify genes responsible to certain stresses. Here, we developed a piggyBac strategy to deliver pooled sgRNA libraries stably into cell lines. We used this strategy to conduct a screen based on genome-wide clustered regularly interspaced short palindromic repeat technology (CRISPR)-Cas9 in Bombyx mori cells. We first constructed a single guide RNA (sgRNA) library containing 94,000 sgRNAs, which targeted 16,571 protein-coding genes. We then generated knockout collections in BmE cells using the piggyBac transposon. We identified 1006 genes that are essential for cell viability under normal growth conditions. Of the identified genes, 82.4% (829 genes) were homologous to essential genes in seven animal species. We also identified 838 genes whose loss facilitated cell growth. Next, we performed context-specific positive screens for resistance to biotic or nonbiotic stresses using temperature and baculovirus separately, which identified several key genes and pathways from each screen. Collectively, our results provide a novel and versatile platform for functional annotations of B. mori genomes and deciphering key genes responsible for various conditions. This study also shows the effectiveness, practicality, and convenience of genome-wide CRISPR screens in nonmodel organisms.
Prognostic Significance of Programmed Cell Death Ligand 1 Expression in High-Grade Serous Ovarian Carcinoma: A Systematic Review and Meta-Analysis

(1) Background: High-grade serous ovarian carcinoma (HGSOC) is an aggressive subtype of ovarian cancer. Recent advances have introduced prognostic markers and targeted therapies. Programmed cell death ligand 1 (PD-L1) has emerged as a potential biomarker for HGSOC, with implications for prognosis and targeted therapy eligibility; (2) Methods: A literature search was conducted on major databases, and extracted data were categorized and pooled. Subgroup analysis was performed for studies with high heterogeneity. (3) Results: Data from 18 eligible studies were categorized and pooled based on PD-L1 scoring methods, survival analysis types, and endpoints. The result showed an association between high PD-L1 expression and a favorable prognosis in progression-free survival (HR = 0.53, 95% CI = 0.35–0.78, p = 0.0015). Subgroup analyses showed similar associations in subgroups of neoadjuvant chemotherapy patients (HR = 0.6, 95% CI = 0.4–0.88, p = 0.009) and European studies (HR = 0.59, 95% CI = 0.42–0.82, p = 0.0017). In addition, subgroup analyses using data from studies using FDA-approved PD-L1 antibodies suggested a significant association between favorable prognosis and high PD-L1 expression in a subgroup including high and low stage data in overall survival data (HR = 0.46, 95% CI = 0.3–0.73, p = 0.0009). (4) Conclusions: This meta-analysis revealed a potential association between high PD-L1 expression and favorable prognosis. However, caution is warranted due to several limitations. Validation via large-scale studies, with mRNA analysis, whole tissue sections, and assessments using FDA-approved antibodies is needed.
Evaluating Labor Market Reforms: a General Equilibrium Approach *

Job security provisions, particularly those regarding workers' layoffs, are commonly invoked to explain the high and persistent European unemployment rates. This belief has led several countries to undertake reforms in the last two decades aimed at reducing firing costs. However, the reduction has typically been applied only to fixed-term contracts, while firing costs for tenured contracts have not been altered. Despite the widespread use of such contracts, there is a lack of quantitative analysis of their impact over the aggregate economy. To fill this gap, we build a general equilibrium model with heterogeneous agents in a firing-cost economy. We calibrate our model to Spanish data using in part parameters estimated with a dynamic partial equilibrium model and a panel of firm-level observations. Spain is a particularly interesting case since its labor regulations are among the most protective in the OECD and its unemployment rates among the highest. We find that the introduction of temporary contracts have small and negative effects on aggregate output and employment. These findings suggest that reforms other than relaxing firing restrictions are needed to reduce European unemployment. for very helpful comments and discussions. C. Alonso-Borrego thanks research funding from the Spanish DGI, Grant BEC 2000-0170. J.E. Galdón-Sánchez thanks financial support from the DGICYT P98-0139 and the European Commission for a TMR Marie Curie fellowship.
On Parallel 𝑘 -Center Clustering

We consider the classic 𝑘 -center problem in a parallel setting, on the low-local-space Massively Parallel Computation (MPC) model, with local space per machine of O( 𝑛 𝛿 ) , where 𝛿 ∈ ( 0 , 1 ) is an arbitrary constant. As a central clustering problem, the 𝑘 -center problem has been studied extensively. Still, until very recently, all parallel MPC algorithms have been requiring Ω ( 𝑘 ) or even Ω ( 𝑘𝑛 𝛿 ) local space per machine. While this setting covers the case of small values of 𝑘 , for a large number of clusters these algorithms require large local memory, making them poorly scalable. The case of large 𝑘 , 𝑘 ≥ Ω ( 𝑛 𝛿 ) , has been considered recently for the low-local-space MPC model by Bateni et al. (2021), who gave an O( loglog 𝑛 ) -round MPC algorithm that produces 𝑘 ( 1 + 𝑜 ( 1 )) centers whose cost has multiplicative approximation of O( logloglog 𝑛 ) . In this paper we extend the algorithm of Bateni et al. and design a low-local-space MPC algorithm that in O( loglog 𝑛 ) rounds returns a clustering with 𝑘 ( 1 + 𝑜 ( 1 )) clusters that is an O( log ∗ 𝑛 ) -approximation for 𝑘 -center.
IMPLEMENTING A SECURED OFFLINE BLOCKCHAIN BASED ELECTRONIC VOTING SYSTEM

Internet penetration is a critical factor in determining the adoption of an internet-based electronic voting system. In Nigeria for instance, the electoral bill which is to provide the legal backing for the transmission of voting data from polling units across the federation had witnessed huge setbacks hinged on lack of internet infrastructure in Nigeria’s remote places. Arguments have been made for and against the possibility of disenfrenching citizens if electronic transmission was allowed in the electoral laws. In this article, we propose an electronic voting system model that allows the secured continuation of voting exercise in offline mode and the transmission of cast votes upon the restoration of internet connectivity using the infrastructures permissible with the blockchain technology. The model is adaptable in any national election, specifically, Nigeria’s national elections.
MKGPC: Multimodal Knowledge Graph Propagation for Recommendation Systems

—With the exponential increase of information available on the Internet, the problem of information overload has become more and more urgent. Recommendation systems have been proposed to solve this problem by recommending a small subset of information from a large pool of data for users to quickly digest. Collaborative ﬁltering has been the main direction of recommendation algorithm research, but it faces challenges such as the cold start problem and sparse interaction data. To address these challenges, researchers have introduced auxiliary information such as point-of-interest, comment, social networks, and contextual information. Knowledge graphs have been introduced as auxiliary information to improve the performance of recommendation systems. Knowledge graphs can fuse multiple data sources, expand the semantic information of data, and infer more potential information of data. However, current knowledge graph-based recommendation systems tend to ignore multimodal information. In this paper, we propose a new model, termed as MKGPC, that utilizes the propagation of information on the multimodal knowledge graph and considers multimodal information fusion. We compare the proposed model with state-of-the-art models. The proposed model shows promising results on two real-world datasets.
Situation variation in consumers' media channel consideration

In this article, the authors investigate consumers’ consideration of media channels during different usage situations. They develop a model that explains consumers’ media channel consideration as a function of the media channel’s perceived benefits. In addition, they hypothesize that the usage situation affects consumers’ media channel consideration and that situation-based benefit requirements moderate the effect of the benefits on their channel consideration. The authors test the hypothesized relationships using survey data from 341 consumers regarding their consideration of 12 different media channels used by manufacturers to communicate product information across three product-related usage situations. The results of the analyses support the proposed model structure and confirm the expected relationships among perceived media channel benefits, usage situations, media channel requirements, and consumers’ media channel consideration.
Structural Equations, Treatment Effects, and Econometric Policy Evaluation 1 Two Anonymous Referees for Helpful Comments on the First round Reports. We Benefited from the Close Reading By

This paper uses the marginal treatment effect (MTE) to unify the nonparametric literature on treatment effects with the econometric literature on structural estimation using a nonparametric analog of a policy invariant parameter; to generate a variety of treatment effects from a common semiparametric functional form; to organize the literature on alternative estimators; and to explore what policy questions commonly used estimators in the treatment effect literature answer. A fundamental asymmetry intrinsic to the method of instrumental variables (IV) is noted. Recent advances in IV estimation allow for heterogeneity in responses but not in choices, and the method breaks down when both choice and response equations are heterogeneous in a general way. EVALUATING THE IMPACTS OF PUBLIC POLICIES, forecasting their effects in new environments, and predicting the effects of policies never tried are three central tasks of economics. The structural approach and the treatment effect approach are two competing paradigms of policy evaluation. The structural approach emphasizes clearly articulated economic models that can be used to accomplish all three tasks under the exogeneity and parameter policy invariance assumptions presented in that literature (see Hansen and Sargent (1981), Hendry (1995)). Economic theory is used to guide the construction of models and to suggest included and excluded variables. The functional form and exogeneity assumptions invoked in this literature are sometimes controversial (see, e.g., Angrist and Krueger (1999)) and the sources of identification of parameters of these models are often not clearly articulated. Because of its co-authorship, this lecture was subject to the usual refereeing practices of Econometrica and has been through two rounds of reviews. on the second draft. We have benefited from a close reading by Jora Stixrud and Sergio Urzua on the third draft. We have also benefited from comments by an anonymous referee on the second draft of this paper. Sergio Urzua provided valuable research assistance in programming the simulations reported in this paper and was assisted by Hanna Lee. Urzua made valuable contributions to our understanding of the random coefficient case and cases with negative weights, and made numerous valuable comments on this draft, as did Weerachart Kilenthong. See our companion paper (Heckman, Urzua, and Vytlacil (2004)), where these topics are developed further.