Miracle-dz/UniFAR-FTUs huggingface dataset:
MotionCtrl: A Unified and Flexible Motion Controller for Video Generation

Motions in a video primarily consist of camera motion, induced by camera movement, and object motion, resulting from object movement. Accurate control of both camera and object motion is essential for video generation. However, existing works either mainly focus on one type of motion or do not clearly distinguish between the two, limiting their control capabilities and diversity. Therefore, this paper presents MotionCtrl, a unified and flexible motion controller for video generation designed to effectively and independently control camera and object motion. The architecture and training strategy of MotionCtrl are carefully devised, taking into account the inherent properties of camera motion, object motion, and imperfect training data. Compared to previous methods, MotionCtrl offers three main advantages: 1) It effectively and independently controls camera motion and object motion, enabling more fine-grained motion control and facilitating flexible and diverse combinations of both types of motion. 2) Its motion conditions are determined by camera poses and trajectories, which are appearance-free and minimally impact the appearance or shape of objects in generated videos. 3) It is a relatively generalizable model that can adapt to a wide array of camera poses and trajectories once trained. Extensive qualitative and quantitative experiments have been conducted to demonstrate the superiority of MotionCtrl over existing methods.
Distribution modeling of Chrysophyllum albidum in South-West Nigeria.

Modeling studies were carried out on Chrysophyllum albidum, an important economic species in Nigeria. This study attempts to estimate the occurrence, and at the same time investigate the major climatic factors responsible for the geographical distribution of the species in South-West part of the country using Maximum Entropy (MAXENT) program. A total of 43 geo-referenced records of Chrysophyllum albidum were assembled from fieldwork and climatic data were acquired from the Worldclim Database. The main variables that contributed towards predicting the species distribution were temperature seasonality, minimum temperature of coldest month and mean temperature of driest quarter . Results suggest that the distribution model was excellent with training AUC value of 0.999 and test AUC value of 1.000 confirming the wide distribution of Chrysophyllum albidum in South-West Nigeria. The testing models correctly predicted most of the test locations in all models and standard deviation is 0.000. In addition, t he model indicated that the presence of Chrysophyllum albidum was mainly associated with minimum temperature of the coldest month (55.6%) and precipitation of the coldest quarter (18.6%). Recommendations for different conservation strategies include in situ conservation in Protected Areas; ex situ conservation in seed banks; and conservation through ‘sustainable utilization’.
Interpretability Illusions in the Generalization of Simplified Models

A common method to study deep learning systems is to use simplified model representations -- for example, using singular value decomposition to visualize the model's hidden states in a lower dimensional space. This approach assumes that the results of these simplified are faithful to the original model. Here, we illustrate an important caveat to this assumption: even if the simplified representations can accurately approximate the full model on the training set, they may fail to accurately capture the model's behavior out of distribution -- the understanding developed from simplified representations may be an illusion. We illustrate this by training Transformer models on controlled datasets with systematic generalization splits. First, we train models on the Dyck balanced-parenthesis languages. We simplify these models using tools like dimensionality reduction and clustering, and then explicitly test how these simplified proxies match the behavior of the original model on various out-of-distribution test sets. We find that the simplified proxies are generally less faithful out of distribution. In cases where the original model generalizes to novel structures or deeper depths, the simplified versions may fail, or generalize better. This finding holds even if the simplified representations do not directly depend on the training distribution. Next, we study a more naturalistic task: predicting the next character in a dataset of computer code. We find similar generalization gaps between the original model and simplified proxies, and conduct further analysis to investigate which aspects of the code completion task are associated with the largest gaps. Together, our results raise questions about the extent to which mechanistic interpretations derived using tools like SVD can reliably predict what a model will do in novel situations.
Evaluating and Mitigating Discrimination in Language Model Decisions

As language models (LMs) advance, interest is growing in applying them to high-stakes societal decisions, such as determining financing or housing eligibility. However, their potential for discrimination in such contexts raises ethical concerns, motivating the need for better methods to evaluate these risks. We present a method for proactively evaluating the potential discriminatory impact of LMs in a wide range of use cases, including hypothetical use cases where they have not yet been deployed. Specifically, we use an LM to generate a wide array of potential prompts that decision-makers may input into an LM, spanning 70 diverse decision scenarios across society, and systematically vary the demographic information in each prompt. Applying this methodology reveals patterns of both positive and negative discrimination in the Claude 2.0 model in select settings when no interventions are applied. While we do not endorse or permit the use of language models to make automated decisions for the high-risk use cases we study, we demonstrate techniques to significantly decrease both positive and negative discrimination through careful prompt engineering, providing pathways toward safer deployment in use cases where they may be appropriate. Our work enables developers and policymakers to anticipate, measure, and address discrimination as language model capabilities and applications continue to expand. We release our dataset and prompts at https://huggingface.co/datasets/Anthropic/discrim-eval
Qualitative endline results of the tipping point project to prevent child, early and forced marriage in Nepal

ABSTRACT Currently, Nepal is not on track to meet Sustainable Development Goal 5.3 – the elimination of harmful practices, including child, early and forced marriage by the year 2030. Evidence on what works to prevent child, early and forced marriage often is inattentive to contextual factors that influence intervention effectiveness. This study presents qualitative results of a mixed-methods evaluation of CARE’s Tipping Point Program to prevent child, early and forced marriage in Nepal, interrogating the perceived benefits of the programme and elucidating contextual features that enhance or detract from programme benefit. Baseline data included interviews with adolescent girls (N = 20), boys (N = 10), adult community leaders (N = 8) and focus group discussions (FGDs) with girls (N = 8 groups; 48 individuals), boys (N = 8 groups; 47 individuals) and parents (N = 16 groups; 95 individuals). Using thematic analysis and structured comparisons by time, gender, district, caste/community, stakeholder type and arm, we found diverse programme participation, but widespread improvements in knowledge across several domains, with behavioural changes concentrated among participants with stronger participation and pre-programme characteristics suggestive of low risk of child marriage. Findings underscore the need to address structural barriers to prevent child marriage and the challenges of attributing programme benefit amidst a dynamic social context.
Non-backtracking eigenvector delocalization for random regular graphs

The non-backtracking operator of a graph is a powerful tool in spectral graph theory and random matrix theory. Most existing results for the non-backtracking operator of a random graph concern only eigenvalues or top eigenvectors. In this paper, we take the first step in analyzing its bulk eigenvector behaviors. We demonstrate that for the non-backtracking operator $B$ of a random $d$-regular graph, its eigenvectors corresponding to nontrivial eigenvalues are completely delocalized with high probability. Additionally, we show complete delocalization for a reduced $2n \times 2n$ non-backtracking matrix $\widetilde{B}$. By projecting all eigenvalues of $\widetilde{B}$ onto the real line, we obtain an empirical measure that converges weakly in probability to the Kesten-McKay law for fixed $d\geq 3$ and to a semicircle law as $d \to\infty$ with $n \to\infty$.
Distinguishing Autochthony, Parautochthony and Allochthony Using Taphofacies Analysis: Can Cold Seep Assemblages Be Discriminated from Assemblages of the Nearshore and Continental Shelf?

Identifying ancient cold seep faunas is intertwined with the problem of distinguishing autochthony from parautochthony. Cold seep faunas are autochthonous assemblages. Taphofacies from an allochthonous microtidal inlet, a parautochthonous inner shelf and an autochthonous upper slope (representing cold seep faunas) were compared to identify their distinguishing characteristics and to facilitate cold seep recognition. The taphonomic signature of the allochthonous inlet assemblage is dominated by abrasion and edge alteration. Bivalves are completely disarticulated
Co-, Post- and Pre(?)-seismic Displacements Associated with the Mw 8.4 Southern Peru Earthquake of 23 June 2001 from Continuous GPS Measurements

On 23 June 2001, at 20:33 UTC, a major earthquake occurred near the coast of southern Peru, about 190 km west of Arequipa (16.15°S, 73.40°W). A revised magnitude of 8.4 was computed for this earthquake by the United States Geological Survey, NEIC (see http://neic.usgs.gov/neis/bulletin/01_EVENTS/0l0623203313/010623203313.html for further information). The earthquake occurred along the west coast of Peru (Figure 1) at the boundary between the Nazca and South American Plates and resulted from thrust faulting along this boundary as the Nazca Plate subducts beneath the South American Plate. The two plates are converging toward each other at a rate of 78 mm/yr (DeMets et al. , 1990). Southwestern Peru has a history of very large earthquakes. The 23 June shock originated just southeast of the source of a magnitude 7.7 earthquake that occurred in November 1996. This area of the plate boundary has not recorded a major rupture since the 1868 earthquake of magnitude approximately 8.5–9 (Dorbath et al. , 1990; Comte and Pardo, 1991). The 23 June shock occurred in a previously identified seismic gap (Nishenko, 1985). The 1868 earthquake was more destructive than the 23 June earthquake and produced a tsunami that killed hundreds of people along the South American coast. The 23 June earthquake is very similar to the 1995 Mw 8.1 Antofagasta earthquake (Ruegg et al. , 1996), which ruptured a zone of about 180 × 70 km2 of the Chilean coast, along which no major historical earthquake has been recognized before. The city of Arequipa, located 190 km east of the main shock epicenter, experienced damage during the 23 June earthquake. At Arequipa several precise positioning systems are permanently operating, including continuous GPS station of the IGS, DORIS beacon, Satellite Laser Ranging. In this study we give a quick report about surface deformation …
Factors affecting the performance of parallel mining of minimal unique itemsets on diverse architectures

Three parallel implementations of a divide‐and‐conquer search algorithm (called SUDA2) for finding minimal unique itemsets (MUIs) are compared in this paper. The identification of MUIs is used by national statistics agencies for statistical disclosure assessment. The first parallel implementation adapts SUDA2 to a symmetric multi‐processor cluster using the message passing interface (MPI), which we call an MPI cluster; the second optimizes the code for the Cray MTA2 (a shared‐memory, multi‐threaded architecture) and the third uses a heterogeneous ‘group’ of workstations connected by LAN. Each implementation considers the parallel structure of SUDA2, and how the subsearch computation times and sequence of subsearches affect load balancing. All three approaches scale with the number of processors, enabling SUDA2 to handle larger problems than before. For example, the MPI implementation is able to achieve nearly two orders of magnitude improvement with 132 processors. Performance results are given for a number of data sets. Copyright © 2009 John Wiley & Sons, Ltd.
Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification

Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions. Drawing from the two-stream hypothesis, our investigation aims to understand whether the channels within the ventral and dorsal streams contain pertinent information for effective model learning regarding object recognition and identification tasks. By utilizing the data we collected during the object recognition and identification experiment, we scrutinized EEGNet models, trained using channels that replicate the two-stream hypothesis pathways, against a model trained using all available channels. The outcomes reveal that the model trained solely using the temporal region delivered a high accuracy level in classifying four distinct object categories. Specifically, the object recognition and object identification models achieved an accuracy of 89% and 85%, respectively. By incorporating the channels that mimic the ventral stream, the model’s accuracy was further improved, with the object recognition model and object identification model achieving an accuracy of 95% and 94%, respectively. Furthermore, the Grad-CAM result of the trained models revealed a significant contribution from the ventral and dorsal stream channels toward the training of the EEGNet model. The aim of our study is to pinpoint the optimal channel configuration that provides a swift and accurate brain-computer interface system for object recognition and identification.
TI-DNS: A Trusted and Incentive DNS Resolution Architecture based on Blockchain

Domain Name System (DNS) is a critical component of the Internet infrastructure, responsible for translating domain names into IP addresses. However, DNS is vulnerable to some malicious attacks, including DNS cache poisoning, which redirects users to malicious websites displaying offensive or illegal content. Existing countermeasures often suffer from at least one of the following weakness: weak attack resistance, high overhead, or complex implementation. To address these challenges, this paper presents TI-DNS, a blockchain-based DNS resolution architecture designed to detect and correct the forged DNS records caused by the cache poisoning attacks in the DNS resolution process. TI-DNS leverages a multi-resolver Query Vote mechanism to ensure the credibility of verified records on the blockchain ledger and a stake-based incentive mechanism to promote well-behaved participation. Importantly, TI-DNS is easy to be adopted as it only requires modifications to the resolver side of current DNS infrastructure. Finally, we develop a prototype and evaluate it against alternative solutions. The result demonstrates that TI-DNS effectively and efficiently solves DNS cache poisoning.
PSYCHOLOGICAL DISTRESS AND ACADEMIC SELF-EFFICACY OF COLLEGE STUDENTS: THE MODERATING ROLE OF ANXIETY AMIDST THE COVID-19 PANDEMIC

The distress and anxiety brought about by the academic demands negatively influences student performance during the abrupt shift to full-online classes. However, the literature indicates that self-efficacy significantly alleviates the negative effect of distress and anxiety.The current study aims to determine the relationship of COVID-19 related distress and academic self-efficacy as well as investigate if anxiety is a moderator that aggravates the distress of Angeles University Foundation college students. A total of 223 college students’ participants were acquired from a voluntary convenience sample and participants answered the questionnaires through Google Forms, namely: The Online Learning Self-Efficacy (OLSE), The Kessler Psychological Distress Scale (K6), and The Coronavirus Pandemic Anxiety Scale (CPAS-11). Additionally, all statistical analyses were conducted using SPSS 23. Moreover, the hierarchical regression was conducted to determine if COVID-19 distress predicted academic self-efficacy (ASE) with anxiety as the moderator. The findings indicate that COVID-19 distress and anxiety negatively affect the beliefs of the students in their capacity to perform academic tasks. However, evidence failed to support the moderating role of anxiety between Covid-19 distress and academic self-efficacy.
Distance Electronic Learning Strategy in Medical Teaching During the COVID-19 Pandemic: Cross-Sectional Survey Study

Background Teaching hospitals have been regarded as the primary settings where doctors teach and practice high-quality medicine, as well as where medical students learn the profession and acquire their initial clinical skills. A percentage of instruction is now done over the internet or via electronic techniques. The present COVID-19 epidemic has pushed distance electronic learning (DEL) to the forefront of education at all levels, including medical institutions. Objective This study aimed to observe how late-stage medical students felt about DEL, which was put in place during the recent COVID-19 shutdown in Jordan. Methods We conducted a prospective, cross-sectional, web-based, questionnaire-based research study during the COVID-19 pandemic lockdown between March 15 and May 1, 2020. During this period, all medical schools in Jordan shifted to DEL. Results A total of 380 students responded to a request to fill out the questionnaire, of which 256 completed the questionnaire. The data analysis showed that 43.6% (n=112) of respondents had no DEL experience, and 53.1% (n=136)of respondents perceived the DEL method as user-friendly. On the other hand, 64.1% (n=164) of students strongly believed that DEL cannot substitute traditional clinical teaching. There was a significant positive correlation between the perception of user-friendliness and the clarity of the images and texts used. Moreover, there was a strong positive correlation between the perception of sound audibility and confidence in applying knowledge gained through DEL to clinical practice. Conclusions DEL is a necessary and important tool in modern medical education, but it should be used as an auxiliary approach in the clinical setting since it cannot replace conventional personal instruction.
SO-NeRF: Active View Planning for NeRF using Surrogate Objectives

Despite the great success of Neural Radiance Fields (NeRF), its data-gathering process remains vague with only a general rule of thumb of sampling as densely as possible. The lack of understanding of what actually constitutes good views for NeRF makes it difficult to actively plan a sequence of views that yield the maximal reconstruction quality. We propose Surrogate Objectives for Active Radiance Fields (SOAR), which is a set of interpretable functions that evaluates the goodness of views using geometric and photometric visual cues - surface coverage, geometric complexity, textural complexity, and ray diversity. Moreover, by learning to infer the SOAR scores from a deep network, SOARNet, we are able to effectively select views in mere seconds instead of hours, without the need for prior visits to all the candidate views or training any radiance field during such planning. Our experiments show SOARNet outperforms the baselines with $\sim$80x speed-up while achieving better or comparable reconstruction qualities. We finally show that SOAR is model-agnostic, thus it generalizes across fully neural-implicit to fully explicit approaches.
A social science research agenda to accelerate public engagement in climate change adaptation

Recent studies find that people in high-income countries now think of climate change impacts, such as flooding or periods of high temperature, as being of immediate relevance and concern. Individuals and households can take adaptation actions to help limit the severity of harm caused when climate impacts occur, for example, they could make adjustments to their homes such as installing flood gates or sun shades, or they could adapt their behavior such as staying indoors during the hottest part of the day. However, so far adaptation is not yet happening at the speed or scale needed to adequately prepare for the climate impacts already being experienced or those projected for the coming decades. Here, we propose an agenda for future social science research that would further our understanding of how best to increase engagement and action in climate change adaptation.
A Machine-Learning-Accelerated Quantum Transport Study on the Effects of Superlattice Disorder and Strain in a Mid-wave Infrared Curved Sensor

An emerging device architecture for infrared imaging is the curved focal-plane array which benefits from several optical advantages over the traditional flat design. However, the curving process introduces additional strain in the active region which must be taken into account. Type-II superlattices, a promising alternative to traditional bulk materials for use in infrared photodetectors, is a candidate material for use in these devices, but the transport properties of these highly heterogeneous materials are not straightforward and can be affected by different material conditions, such as superlattice disorder and external strain. We present a comprehensive study of the internal QE calculated for a curved device that incorporates finite element analysis (FEA) modeling, nonequilibirium Green's functions (NEGF) calculations, and Gaussian Process (GP) regression. FEA is used for predicting the strain configuration throughout the active region induced by the curving procedure of the device. NEGF is used to calculate the vertical hole mobility for a select set of strain configurations, from which the internal quantum efficiency of the device is approximated to predict performance under strained conditions. Then this data set is used to train a GP model that maps the quantum efficiency QE predictions onto the spatial coordinates of the curved device, based on the strain configuration predicted using FEA. This analysis is performed for ideal and disordered SLs to understand both the fundamental and practical limitations of the performance of these materials in curved devices.