Miracle-dz/UniFAR-FTUs huggingface dataset:
OpenStreetMap for multi-faceted climate risk assessments

Natural hazards pose significant risks to human lives, infrastructure, and ecosystems. Understanding risks along all these dimensions is critical for effective adaptation planning and risk management. However, climate risk assessments mostly focus on population, economic asset values, and road or building infrastructure, because publicly available data on more diverse exposures are scarce. The increasing availability of crowd-sourced geospatial data, notably from OpenStreetMap, opens up a novel means for assessing climate risk to a large range of physical assets. To this end, we present a stand-alone, lightweight, and highly flexible Python-based OpenStreetMap data extraction tool: OSM-flex. To demonstrate the potential and limitations of OpenStreetMap data for risk assessments, we couple OSM-flex to the open-source natural hazard risk assessment platform CLIMADA and compute winter storm risk and event impacts from winter storm Lothar across Switzerland to forests, UNESCO heritage sites, railways, healthcare facilities, and airports. Contrasting spatial patterns of risks on such less conventional exposure layers with more traditional risk metrics (asset damages and affected population) reveals that risk hot-spots are inhomogeneously and distinctly distributed. For instance, impacts on forestry are mostly expected in Western Switzerland in the Jura mountain chain, whereas economic asset damages are concentrated in the urbanized regions around Basel and Zurich and certain train lines may be most often affected in Central Switzerland and alpine valleys. This study aims to highlight the importance of conducting multi-faceted and high-resolution climate risk assessments and provides researchers, practitioners, and decision-makers with potential open-source software tools and data suggestions for doing so.
Convex Relaxation for Community Detection with Covariates

Community detection in networks is an important problem in many applied areas. In this paper, we investigate this in the presence of node covariates. Recently, an emerging body of theoretical work has been focused on leveraging information from both the edges in the network and the node covariates to infer community memberships. However, so far the role of the network and that of the covariates have not been examined closely. In essence, in most parameter regimes, one of the sources of information provides enough information to infer the hidden cluster labels, thereby making the other source redundant. To our knowledge, this is the first work which shows that when the network and the covariates carry "orthogonal" pieces of information about the cluster memberships, one can get asymptotically consistent clustering by using them both, while each of them fails individually.
Short-term impact of increasing the value of unconditional and conditional incentives in Understanding Society

In longitudinal studies, panel attrition and wave nonresponse threaten the quality of survey estimates. Previous research has found that some groups are more prone to attrition than others – for instance, people on lower incomes or ethnic minorities – which endangers the quality of the estimates produced with the survey data. Also, a continuous loss of participants in a longitudinal study reduces the sample size available for the analysis, harming the precision of the survey estimates and the possibility of studying small subgroups in the population.
Reconstructing and Querying ML Pipeline Intermediates

In order to study and increase the reliability and correctness of the predictions made by machine learning (ML) applications, various data debugging techniques have been developed in the last years. These methods aim to reveal dependencies between the inputs (features) and outputs of anMLmodel. Examples include Slice Finder [2], Gopher [4], Fairtest [6] or Fairlearn [1], which identify subsets of the data where a model performs poorly or which negatively impact a model’s fairness. Unfortunately, the majority of these methods are not directly applicable to real-world ML applications with complex data preparation pipelines, as they assume a single, static input dataset with attributes to slice the data, aligned with features and predictions in matrix form. Therefore data scientists typically have to manually construct an appropriate evaluation dataset by rewriting the pipeline. This is tedious, as data scientists already spent the majority of their time writing data wrangling code [8], and also poses the danger of accidentally introducing hard-to-find ML-related bugs into the analysis [7].
Fairness in Job Recommendation under Quantity Constraints

Job recommendation platforms such as LinkedIn play an important role in effectively matching job postings and job seekers. The problem of recommending jobs to potential candidates is essentially different from traditional recommendation problems such as recommending movies or music to users since a job posting usually targets for hiring a limited number of employees, which means that quantity constraints are imposed on recommendation conversions. Therefore, recommending a job to an unnecessarily large number of users will not only cost companies excessive efforts in the process of reviewing and interviewing, but also result in intense competition among job seekers on the same job. To solve this problem in a more efficient way, we model the job recommendation task as a resource allocation problem where jobs are recommended under certain quantity constraints. Moreover, to enhance the fairness of users in the job recommendation task, we expect that the recommendation results are consistent to users of different groups after the allocation process. To this end, we propose a framework based on a re-ranking approach with quantity and fairness constraints that generates high-quality recommendations while mitigating the disparity of recommendation results between different user groups. In experiments, we focus on a practical case of user fairness in job recommendation to improve salary fairness over gender. The experimental results on a real-world dataset with various representative recommendation algorithms show that our approach not only generates better distribution of job postings in recommendation lists, but also improves user group fairness while maintaining competitive recommendation qualities.
On the Variation of Column $ O/N \ textsubscript { 2 } $ in the upper atmosphere using Principal Component Analysis in 2-dimensional images

Day-to-day variability in thermospheric composition is driven by solar, geomagnetic and meteorological drivers. The ratio of the column density of atomic oxygen and molecular nitrogen (O/N \ textsubscript { 2 } ) is a useful parameter for quantifying this variability that has been shown to exhibit close correspondence to F-region electron density, total electron content and upper atmospheric transport. Therefore, understanding the variability in O/N \ textsubscript { 2 } gives an insight into the geophysical variability of other relevant ionospheric and thermospheric parameters. The relative contributions of these drivers for thermospheric variability is not well known. Here we report a new analysis of the variability in O/N \ textsubscript { 2 } to identify the sources of variability in a 55-day time period. Principal Component Analysis (PCA) was performed on thermospheric O/N \ textsubscript { 2 } column density ratio from days 81 to 135 of 2020 from NASA’s Global-scale Observations of the Limb and Disk (GOLD) mission. We ﬁnd that geomagnetic activity is the major source of variability in O/N \ textsubscript { 2 } column density ratio, followed by solar-driven transport and meteorological driving from the lower atmosphere. The ﬁrst component (PC1) showed a strong correlation to Kp index and IMF, and geomagnetic storm eﬀects are seen in the wavelet analysis of PC1’s weights. The ﬁfth component (PC5) showed a strong quasi-6-day oscillation(Q6DO). The higher explained variance ratio of PC1 suggests a stronger eﬀect of geomagnetic activity relative to meteorological forcing from planetary scale waves. The methodology of the present study also demonstrates how PCA can be used to isolate and rank diﬀerent sources of variability in other IT parameters.
Structured World Models from Human Videos

We tackle the problem of learning complex, general behaviors directly in the real world. We propose an approach for robots to efficiently learn manipulation skills using only a handful of real-world interaction trajectories from many different settings. Inspired by the success of learning from large-scale datasets in the fields of computer vision and natural language, our belief is that in order to efficiently learn, a robot must be able to leverage internet-scale, human video data. Humans interact with the world in many interesting ways, which can allow a robot to not only build an understanding of useful actions and affordances but also how these actions affect the world for manipulation. Our approach builds a structured, human-centric action space grounded in visual affordances learned from human videos. Further, we train a world model on human videos and fine-tune on a small amount of robot interaction data without any task supervision. We show that this approach of affordance-space world models enables different robots to learn various manipulation skills in complex settings, in under 30 minutes of interaction. Videos can be found at https://human-world-model.github. io
Nested Quantum Search Model on Symmetric Ciphers and Its Applications

This paper puts forward a multi-round quantum key search model for symmetric ciphers. We turn an unstructured key search for symmetric ciphers into a nested structured quantum search. By the preimage of punctured ciphertexts (or keystream), we get a convergent sequence of subspaces of the full key space. In each round, our search is performed only in a subspace containing the real key, while the rest part is removed from search space. We ﬁnd out several parameters, the length s of the punctured ciphertext (or keystream), the iteration number r , and the error δ in the search algorithm, which can inﬂuence the resulting complexity. The query complexity of our search model is ˜ O (2 α n · n ) , where α n is much smaller than 12 . Speciﬁcally, the query complexity is ˜ O ( r 2 n 2( r − 1) ) (ignore constant δ ) better than Grover’s ˜ O (2 n 2 ) , while parameter r increases as n increases. Our search model can be applied to symmetric ciphers. And it has been shown that doubling the key length is not an eﬀective way anymore to resist the quantum search attacks. Even if the key length is increased by r − 1 times, symmetric ciphers still struggle to obtain desired security for a re-selected value of r .
Analysis of Maximal Extractable Value on the Algorand Blockchain

This research provides a first analysis of the Maximal Extractable Value (MEV) dynamics on the Algorand blockchain. While MEV has been extensively studied in dynamic gas-price blockchains like Ethereum, there is limited understanding of MEV in blockchains with fixed gas-price mechanisms. Our research involves analyzing theoretical arbitrage-related MEV as an upper-bound estimate that can potentially be exploited through decentralized exchange (DEX) to centralized exchange (CEX) arbitrage. We identify potential opportunities for profitable cross-DEX arbitrage transactions, thereby highlighting the presence of MEV. However, after analyzing arbitrage-related transactions of market participants in the Algorand decentralized finance (DeFi) ecosystem, we find no indication of systematic cross-DEX related MEV exploitation in the analyzed time-period.
Framework for sense disambiguation of mathematical expressions

Mathematical expressions are indispensable for describing mathematical concepts or models. Although these expressions are formal representations, they contain ambiguity, i.e., a single expression could be interpreted as having multiple meanings. This feature prevents the flexible use of mathematical expressions in computation. In this paper, we focus on symbol-level ambiguity and consider the problem of labeling semantic information to each symbol as a classification problem. Then we propose a framework for solving the problem with supervised learning.
PCEP Requirements for WSON Routing and Wavelength Assignment

This memo provides application-specific requirements for the Path Computation Element communication Protocol (PCEP) for the support of Wavelength Switched Optical Networks (WSON). Lightpath provisioning in WSONs requires a routing and wavelength assignment (RWA) process. From a path computation perspective, wavelength assignment is the process of determining which wavelength can be used on each hop of a path and forms an additional routing constraint to optical light path computation. Additionally, optical impairments may add further constraints on the paths available for use.
RANDOM TESTING OF WEBASSEMBLY IMPLEMENTATIONS USING SEMANTICALLY VALID PROGRAMS

WebAssembly is a relatively new language designed to be low-level and portable. Designed primarily for web browsers, its compact representation is meant to be directly executed by a browser, enabling high-performance applications on the web. Since implementations are both complex and browser-dependent, the language is a good target for differential random testing. This thesis introduces Wasmlike, a random generator of semantically valid WebAssembly programs. By using semantically valid programs with random differential testing, the goal is to penetrate past syntax and semantic validation, and test WebAssembly implementations for defects that cause programs to produce incorrect results. Wasmlike has found five significant semantics defects in WebAssembly implementations.
Olaf: a lightweight, portable audio search system

The dataflow in Olaf closely resembles the flow depicted in Figure 1. Audio is transformed to features, which are grouped in recognizable fingerprints. The fingerprints are compared with a database of reference fingerprints. If a match is found, it is reported or, in case of a true negative, the system reports that the audio is not present in the database. The properties of the acoustic fingerprinting system mainly depend on the selection of features, the information captured by the fingerprints, and the performance of the matching step.
Hector: Multi-level Paradigm in Hardware Synthesis

In recent years, hardware intermediate representation (IR) has become a popular topic. An appropriate IR can give enough expressivity and avoid redundant engineering efforts. Hector IR fits well in the hardware synthesis flow, which brings the opportunity of building new synthesis tools with minimal effort. Besides, the multi-level paradigm also enables automatic verification in the synthesis flow.
SPR mega-benchmark shows surprisal tracks construction- but not item-level difficulty

The surprisal account of syntactic disambiguation difficulty holds that word-level unpredictability is the sole determinant of processing difficulty in garden path (GP) sentences [1]. This entails that surprisal should predict processing difficulty both across different constructions and across individual items. We test this by looking at construction- and item-level GP effects (GPEs) in a large-scale self-paced-reading (SPR) benchmark we introduce: the Syntactic Ambiguity Processing (SAP) Benchmark, which has orders of magnitude more data than a standard reading experiment. We focus on a subset of the constructions in this dataset: MV/RR (1a), NP/S (1b), and NP/Z (1c)). The SAP benchmark uses a standard within-item factorial design to estimate GPEs. We created twenty-four sentence triplets as in (1). Each participant saw 4 ambiguous and 4 unambiguous instances of each construction. These sentences were intermixed with other sentence constructions from the benchmark and 30 diverse filler sentences. Participants answered a comprehension question following each sentence; only those whose accuracy on fillers was 80% or higher were analyzed (N=2000; recruited on Prolific). There were 220–440 datapoints per item, yielding more precise item-level estimates of GPEs compared to prior work. First, to empirically probe the relative difficulty among the GP constructions, we ran three Bayesian maximally-structured mixed-effect models, each for the disambiguating verb, the first spillover word and the second spillover word. At the disambiguating verb, NP/Z had the largest GPE, followed by MV/RR and then NP/S (Fig. 1a). For all constructions, GPEs peaked at the first spillover position, but the peak was much higher in the MV/RR construction compared to
Proceedings of the Society for Computation in Linguistics Proceedings of the Society for Computation in Linguistics

In early stages of language acquisition, children often make inflectional errors on regular verbs, e.g., Spanish-speaking children produce –a (present-tense 3rd person singular) when other inflections are expected. Most previous models of morphology learning have focused on later stages of learning relating to production of irregular verbs. We propose a computational model of Spanish inflection learning to examine the earlier stages of learning and present a novel data set of gold-standard inflectional annotations for Spanish verbs. Our model replicates data from Spanish-learning children, capturing the acquisition order of different inflections and correctly predicting the substitution errors they make. Analyses show that the learning trajectory can be explained as a result of the gradual acquisition of inflection-meaning associations. Ours is the first computational model to provide an explanation for this acquisition trajectory in Spanish, and represents a theoretical advance more generally in explaining substitution errors in early morphology learning.
A Lasting Impact: Using Second-Order Dynamics to Customize the Continuous Emotional Expression of a Social Robot

Robots are increasingly being developed as assistants for household, education, therapy, and care settings. Such robots need social skills to interact warmly and effectively with their users, as well as adaptive behavior to maintain user interest. While complex emotion models exist for chat bots and virtual agents, autonomous physical robots often lack a dynamic internal affective state, instead displaying brief, fixed emotion routines to promote or discourage specific user actions. We address this need by creating a mathematical emotion model that can easily be implemented in a social robot to enable it to react intelligently to external stimuli. The robot’s affective state is modeled as a second-order dynamic system analogous to a mass connected to ground by a parallel spring and damper. The present position of this imaginary mass shows the robot’s valence, which we visualize as the height of its displayed smile (positive) or frown (negative). Associating positive and negative stimuli with appropriately oriented and sized force pulses applied to the mass enables the robot to respond to social touch and other inputs with a valence that evolves over a longer timescale, capturing essential features of approach-avoidance theory. By adjusting the parameters of this emotion model, one can modify three main aspects of the robot’s personality, which we term disposition, stoicism, and calmness.
A School Connectedness Program for Student Social-emotional Knowledge and Skills

School connectedness, student perception of how much school staff care about their academic and social success, is a protective factor found to decrease risk for student behavioral problems that disrupt learning. A small group of elementary-aged students in an afterschool program participated in six weekly lessons of a school connectedness curriculum that focused on social-emotional learning knowledge and skills. Before and after the curriculum intervention, student participant social skill knowledge was assessed. In addition, afterschool staff members observed and reported student participant level of positive social behavior exhibited. Student knowledge scores significantly improved, however, observed pro-social behaviors did not significantly differ pre-post program. Many previous school connectedness studies have concentrated on secondary-level students, and more studies in the non-school setting were suggested. The current study contributes to the literature as it examined elementary-level students and occurred in the out-of-school time setting.
The Research on the Relationship between Internal Control Quality and Earnings Management of Manufacturing Enterprises

The occurrence of earnings management adversely affects the development of enterprises and investors' decisions. As an important part of China's economic development, manufacturing enterprises are highly sensitive to the impact of internal and external environment due to their industry nature, such as large capital demand and complex production process. Therefore, in order to strengthen enterprises' attention to the construction of internal control norms, enrich the research content of internal control quality and earnings management, this paper uses the data of listed manufacturing companies from 2011 to 2020 as samples to explore the relationship of internal control quality with accrued and real earnings management, and further analyses the difference of influence degree caused by different property rights, listed sectors and marketization process of enterprise location. It is found that for listed manufacturing enterprises, the improvement of internal control quality has a significant inhibiting effect on the degree of accrued and real earnings management. For accrued earnings management, the inhibiting effect is more obvious in non-state-owned enterprises, main board listed companies and enterprises in the region of low marketization process. For real earnings management, the inhibiting effect is more obvious in state-owned enterprises and enterprises in the region of high marketization process.
ROSE: Rotation-based Squeezing Robotic Gripper toward Universal Handling of Objects

Robotics hand/grippers nowadays are not limited to manufacturing lines; instead, they are widely utilized in cluttered environments, such as restaurants, farms, and warehouses. In such scenarios, they need to deal with high uncertainty of the grasped objects' shapes, postures, surfaces, and material properties, which requires complex integration of sensing and decision-making process. On the other hand, integrating soft materials into the gripper's design may tolerate the above uncertainties and reduce complexity in control. In this paper, we introduce ROSE, a novel soft gripper that can embrace the object and squeeze it by buckling a funnel-liked thin-walled soft membrane around the object by simple rotation of the base. Thanks to this design, ROSE hand can adapt to a wide range of objects that can fit in the funnel and handle with gentle gripping force. Regardless of this, ROSE can generate a high lift force (up to 33kgf) while significantly reducing the normal pressure on the gripped objects. In our experiment, a 198g ROSE can be integrated into a robot arm with a single actuation and successfully lift various types of objects, even after 400,000 trials. The embracing mechanism helps reduce the dependence of friction between the object and the membrane, as ROSE could pick up a chicken egg submerged inside an olive oil tank. We also report a feasible design for equipping the ROSE hand with tactile sensing while appealing to the scalability of the design to fit a wide range of objects. Video: https://youtu.be/E1wAI09LaoY