Miracle-dz/UniFAR-FTUs huggingface dataset:
AxOCS: Scaling FPGA-based Approximate Operators using Configuration Supersampling

The rising usage of AI and ML-based processing across application domains has exacerbated the need for low-cost ML implementation, specifically for resource-constrained embedded systems. To this end, approximate computing, an approach that explores the power, performance, area (PPA), and behavioral accuracy (BEHAV) trade-offs, has emerged as a possible solution for implementing embedded machine learning. Due to the predominance of MAC operations in ML, designing platform-specific approximate arithmetic operators forms one of the major research problems in approximate computing. Recently there has been a rising usage of AI/ML-based design space exploration techniques for implementing approximate operators. However, most of these approaches are limited to using ML-based surrogate functions for predicting the PPA and BEHAV impact of a set of related design decisions. While this approach leverages the regression capabilities of ML methods, it does not exploit the more advanced approaches in ML. To this end, we propose AxOCS, a methodology for designing approximate arithmetic operators through ML-based supersampling. Specifically, we present a method to leverage the correlation of PPA and BEHAV metrics across operators of varying bit-widths for generating larger bit-width operators. The proposed approach involves traversing the relatively smaller design space of smaller bit-width operators and employing its associated Design-PPA-BEHAV relationship to generate initial solutions for metaheuristics-based optimization for larger operators. The experimental evaluation of AxOCS for FPGA-optimized approximate operators shows that the proposed approach significantly improves the quality-resulting hypervolume for multi-objective optimization-of 8x8 signed approximate multipliers.
Doubly Robust Proximal Causal Learning for Continuous Treatments

Proximal causal learning is a promising framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the doubly robust (DR) estimator was derived and has shown its effectiveness in estimation, especially when the model assumption is violated. However, the current form of the DR estimator is restricted to binary treatments, while the treatment can be continuous in many real-world applications. The primary obstacle to continuous treatments resides in the delta function present in the original DR estimator, making it infeasible in causal effect estimation and introducing a heavy computational burden in nuisance function estimation. To address these challenges, we propose a kernel-based DR estimator that can well handle continuous treatments. Equipped with its smoothness, we show that its oracle form is a consistent approximation of the influence function. Further, we propose a new approach to efficiently solve the nuisance functions. We then provide a comprehensive convergence analysis in terms of the mean square error. We demonstrate the utility of our estimator on synthetic datasets and real-world applications.
eWand: A calibration framework for wide baseline frame-based and event-based camera systems

Accurate calibration is crucial for using multiple cameras to triangulate the position of objects precisely. However, it is also a time-consuming process that needs to be repeated for every displacement of the cameras. The standard approach is to use a printed pattern with known geometry to estimate the intrinsic and extrinsic parameters of the cameras. The same idea can be applied to event-based cameras, though it requires extra work. By using frame reconstruction from events, a printed pattern can be detected. A blinking pattern can also be displayed on a screen. Then, the pattern can be directly detected from the events. Such calibration methods can provide accurate intrinsic calibration for both frame- and event-based cameras. However, using 2D patterns has several limitations for multi-camera extrinsic calibration, with cameras possessing highly different points of view and a wide baseline. The 2D pattern can only be detected from one direction and needs to be of significant size to compensate for its distance to the camera. This makes the extrinsic calibration time-consuming and cumbersome. To overcome these limitations, we propose eWand, a new method that uses blinking LEDs inside opaque spheres instead of a printed or displayed pattern. Our method provides a faster, easier-to-use extrinsic calibration approach that maintains high accuracy for both event- and frame-based cameras.
High-Dimensional Controller Tuning through Latent Representations

In this paper, we propose a method to automatically and efficiently tune high-dimensional vectors of controller parameters. The proposed method first learns a mapping from the high-dimensional controller parameter space to a lower dimensional space using a machine learning-based algorithm. This mapping is then utilized in an actor-critic framework using Bayesian optimization (BO). The proposed approach is applicable to complex systems (such as quadruped robots). In addition, the proposed approach also enables efficient generalization to different control tasks while also reducing the number of evaluations required while tuning the controller parameters. We evaluate our method on a legged locomotion application. We show the efficacy of the algorithm in tuning the high-dimensional controller parameters and also reducing the number of evaluations required for the tuning. Moreover, it is shown that the method is successful in generalizing to new tasks and is also transferable to other robot dynamics.
Sharpness-Aware Minimization and the Edge of Stability

Recent experiments have shown that, often, when training a neural network with gradient descent (GD) with a step size $\eta$, the operator norm of the Hessian of the loss grows until it approximately reaches $2/\eta$, after which it fluctuates around this value. The quantity $2/\eta$ has been called the"edge of stability"based on consideration of a local quadratic approximation of the loss. We perform a similar calculation to arrive at an"edge of stability"for Sharpness-Aware Minimization (SAM), a variant of GD which has been shown to improve its generalization. Unlike the case for GD, the resulting SAM-edge depends on the norm of the gradient. Using three deep learning training tasks, we see empirically that SAM operates on the edge of stability identified by this analysis.
Automatic Answerability Evaluation for Question Generation

Conventional automatic evaluation metrics, such as BLEU and ROUGE, developed for natural language generation (NLG) tasks, are based on measuring the n-gram overlap between the generated and reference text. These simple metrics may be insufficient for more complex tasks, such as question generation (QG), which requires generating questions that are answerable by the reference answers. Developing a more sophisticated automatic evaluation metric, thus, remains an urgent problem in QG research. This work proposes PMAN (Prompting-based Metric on ANswerability), a novel automatic evaluation metric to assess whether the generated questions are answerable by the reference answers for the QG tasks. Extensive experiments demonstrate that its evaluation results are reliable and align with human evaluations. We further apply our metric to evaluate the performance of QG models, which shows that our metric complements conventional metrics. Our implementation of a GPT-based QG model achieves state-of-the-art performance in generating answerable questions.
Quantum enhanced SU(1,1) matter-wave interferometry in a ring cavity

Quantum squeezed states offer metrological enhancement as compared to their classical counterparts. Here, we devise and numerically explore a novel method for performing SU(1,1) interferometry beyond the standard quantum limit, using quasi-cyclic nonlinear wave mixing dynamics of ultracold atoms in a ring cavity. The method is based on generating quantum correlations between many atoms via photon mediated optomechanical interaction. Timescales of the interferometer operation are here given by the inverse of photonic recoil frequency, and are orders of magnitude shorter than the timescales of collisional spin-mixing based interferometers. Such shorter timescales should enable not only faster measurement cycles, but also lower atomic losses from the trap during measurement, which may lead to significant quantum metrological gain of matter wave interferometry in state of the art cavity setups.
Probability of Default modelling with L\'evy-driven Ornstein-Uhlenbeck processes and applications in credit risk under the IFRS 9

In this paper we develop a framework for estimating Probability of Default (PD) based on stochastic models governing an appropriate asset value processes. In particular, we build upon a L\'evy-driven Ornstein-Uhlenbeck process and consider a generalized model that incorporates multiple latent variables affecting the evolution of the process. We obtain an Integral Equation (IE) formulation for the corresponding PD as a function of the initial position of the asset value process and the time until maturity, from which we then prove that the PD function satisfies an appropriate Partial Integro-Differential Equation (PIDE). These representations allow us to show that appropriate weak (viscosity) as well as strong solutions exist, and develop subsequent numerical schemes for the estimation of the PD function. Such a framework is necessary under the newly introduced International Financial Reporting Standards (IFRS) 9 regulation, which has imposed further requirements on the sophistication and rigor underlying credit modelling methodologies. We consider special cases of the generalized model that can be used for applications to credit risk modelling and provide examples specific to provisioning under IFRS 9, and more.
Nature of the magnetic coupling in infinite-layer nickelates versus cuprates

In contrast to the cuprates, where the proximity of antiferromagnetism (AFM) and superconductivity is well established, first indications for AFM interactions in superconducting infinite-layer nickelates were only recently obtained. Here, we explore, based on first-principles simulations, the nature of the magnetic coupling in NdNiO2 as a function of the on-site Coulomb and exchange interaction, varying the explicit hole doping and the treatment of the Nd $4f$ electrons. The $U$-$J$ phase diagrams for undoped nickelates and cuprates indicate $G$-type ordering, yet show different $U$ dependency. By either Sr hole doping or explicit treatment of the Nd $4f$ electrons, we find a transition to a Ni $C$-type AFM ground state. We trace the effect of Sr doping back to a distinct accommodation of the holes by the Ni versus Cu $e_g$ orbitals. The interaction between Nd $4f$ and Ni $3d$ states stabilizes $C$-type AFM order on both sublattices. Though spin-orbit interactions induce a band splitting near the Fermi energy, the bad-metal state is retained even under epitaxial strain. These results establish the distinct role of the magnetic interactions in the nickelates versus the cuprates and suggest the former as a unique platform to investigate the relation to unconventional superconductivity.
A Toponogov globalisation result for Lorentzian length spaces

In the synthetic geometric setting introduced by Kunzinger and S\"amann, we present an analogue of Toponogov's Globalisation Theorem which applies to Lorentzian length spaces with lower (timelike) curvature bounds. Our approach utilises a"cat's cradle"construction akin to that which appears in several proofs in the metric setting. On the road to our main result, we also provide a lemma regarding the subdivision of triangles in spaces with a local lower curvature bound and a synthetic Lorentzian version of the Lebesgue Number Lemma. Several properties of time functions and the null distance on globally hyperbolic Lorentzian length spaces are also highlighted. We conclude by presenting several applications of our results, including versions of the Bonnet--Myers Theorem and Splitting Theorem for Lorentzian length spaces with local lower curvature bounds, as well as discussion of stability of curvature bounds under Gromov--Hausdorff convergence.
Probing the strongly correlated magnetic state of Co2C nanoparticles at low temperatures using μSR

Co2C nanoparticles (NPs) are amongst transition metal carbides whose magnetic properties have not been well explored. An earlier study (Roy et al 2021 J. Phys.: Condens. Matter 33 375804) showed that a pellet made from Co2C NPs exhibits exchange bias (EB) effect below a temperature, T EB = 50 K and a spin glass (SG) feature emerges below T SG = 5 K. In the current study we use magnetic, electrical transport, specific heat, and muon spin rotation (μSR) measurements to explore further the magnetic properties of a pellet made with 40 nm diameter pure Co2C NPs. We uncover the onset of Kondo localization at Kondo temperature T K (= 40.1 K), which is close to the onset temperature (T EB) of the EB effect. A crossover from the Kondo-screened scenario to the Ruderman–Kittel–Kasuya–Yosida interaction-dominated regime is also observed for T < T K. Temperature-dependent specific heat measurement further supports the Kondo localization scenario in the pellet and shows the heavy fermionic nature of the strongly correlated electronic state in Co2C. The zero field μSR asymmetry spectra in the low-temperature regime are characterized by two distinct fast and slow relaxation rates. The spectra show the absence of long-range magnetic order in the sample. However, our analysis suggests the NPs-pellet shows the presence of a dominant magnetically disordered fraction and a smaller fraction with short-range order. Muons in the disordered fraction exhibit a slower relaxation rate, while muons in the smaller fraction with short-range order exhibit a faster relaxation rate. We observe an increase in this fast relaxation rate between T EB and T SG. This increase below T EB ∼ 50 K suggests a slowing down of the fluctuating local magnetic environment around muons. Transverse field-μSR asymmetry spectra show the emergence of a stable, multi-peaked local magnetic field distribution in the pellet below T EB. Longitudinal field μSR spectra shows distinct changes in the dynamics of fluctuations suggesting the presence of a frozen glassy like state below 6 K. Based on our results, we suggest that below T EB, the pellet of Co2C NPs develops a magnetic interface that separates the two magnetic fractions; one is a disordered fraction, and the other is a fraction with short-range order. The exchange interaction that sets in below T EB at the interface couples the two fractions, leading to a suppression of the fluctuations. With the suppression of magnetic fluctuations below T EB, strong correlation effects in the electronic state of Co2C lead to Kondo localization.
Econometric Analysis of Internal Migration in Russia (Ekonometrijska Analiza Unutrasnjih Migracija U Rusiji)

Using panel data for the 2001-2008 period we estimate the gravity model of migration between Russian regions. We show that though the migration flows have been quite stable, their determinants have changed substantially. Our special attention is drawn to the role of distance between the regions. Our special attention is drawn to the role of distance between regions as one of the main factors of migration. Dividing pairs of regions into nine groups according to the distance between regional centers, we estimate the model for each group separately. We find out that social and economic factors are affecting migration mainly between nearby regions. Yet the intensity of flows between distant (>500 km) areas is almost uncorrelated with indicators of social and economic development of regions. Koriscenjem tabelarnih podataka za period 2001. -2008.god. , obavili smo procijenu gravitacionog modela migracija između regiona Rusije. Iako su tokovi migracija prilicno stabilni, u ovom radu pokazujemo da je doslo do znacajnih promjena njihovih determinanti. Posebnu pažnju smo posvetili ulozi udaljenosti između datih regiona kao jednog od glavnih faktora migracija. Podjelom parova regiona na devet grupa, na osnovu razdaljine između regionalnih centara, procijenili smo dati model za svaku grupu pojedinacno. Otkrili smo da socijalni i ekonomski faktori uticu na migracije uglavnom između susjednih regiona. Sa druge strane, intezitet tokova između udaljenih podrucja (>500 km) gotovo da nema veze sa indikatorima drustvenog i ekonomskog razvoja regiona.
Water and Post-Conflict Peacebuilding

Foreword President Mikhail Gorbachev, Jessica Troell, Erika Weinthal Part 1: Basic Services and Human Security Introduction 1. The Role of Informal Service Providers in Post-conflict Reconstruction and State Building Jeremy Allouche 2. A Tale of Two Cities: Restoring Water Services in Kabul and Monrovia Jean-Francois Pinera, Robert Reed 3. Conflict and collaboration for water resources in Angola's post-war cities Allan Cain, Martin Mulenga 4. Thirsty for Peace: The Water Sector in South Sudan Sam Huston 5. Community Water Management: Experiences from the Democratic Republic of the Congo, Afghanistan, and Liberia Murray Burt, Bilha Keiru 6. Environmental Management of the Iraqi Marshlands in the Post-conflict Period Chizuru Aoki, Sivapragasam Kugaprasatham, Ali Al-Lami Part 2: Livelihoods Introduction 7. Lessons of Water Resource Management from Perspectives of Irrigation Water-use Management and Flood Control: A Case Study of Japan after World War II Mikiko Sugiura, Yuka Toguchi, Mona Funiciello 8. Refugee Rehabilitation and Transboundary Cooperation: India, Pakistan, and the Indus River System Neda A. Zawahri 9. Despite the Best Intentions? The Political Ecology of Water Resource Management in Northern Afghanistan Jennifer McCarthy, Daanish Mustafa 10. Water's Role in Security and Stabilization in Helmand Province, Afghanistan Laura Jean Palmer-Moloney Part 3: Peace Processes, Cooperation, and Confidence Building Introduction 11. The Jordan River Basin: A Conflict like no other Munther J. Haddadin 12. Transboundary Cooperation in the Lower Jordan River Basin Munqeth Mehyar, Nader Khateeb, Gidon Bromberg, Elizabeth Ya'ari 13. The Sava River: Transitioning to Peace in the Former Yugoslavia Amar Colakhodzic, Marija Filipovic, Jana Kovandzic, Stephen Stec 14. Transnational Cooperation over shared Water Resources in the South Caucasus - Reflections on USAID Interventions Marina Vardanyan, Richard Volk 15. Water Security and Scarcity: Potential Destabilization in Western Afghanistan and Iranian Sistan and Baluchestan due to Transboundary Water Conflicts Alex Dehgan, Laura Jean Palmer-Moloney, Medhi Mirazee 16. Water Resources in the Sudan North-South Peace Process and the Ramifications of the Secession of South Sudan Salman M. A. Salman Part 4: Legal Frameworks Introduction 17. Management of Waters in Post-Dayton Bosnia and Herzegovina: Policy, Legal, and Institutional Aspects Slavko Bogdanovic 18. The Right to Water and Sanitation in Post-conflict Legal Mechanisms: An Emerging Regime? , Mara Tignino Part 5: Lessons Learned 19. Harnessing Water Management for more Effective Peacebuilding: Lessons Learned Jessica Troell, Erika Weinthal
Robotic Offline RL from Internet Videos via Value-Function Pre-Training

Pre-training on Internet data has proven to be a key ingredient for broad generalization in many modern ML systems. What would it take to enable such capabilities in robotic reinforcement learning (RL)? Offline RL methods, which learn from datasets of robot experience, offer one way to leverage prior data into the robotic learning pipeline. However, these methods have a"type mismatch"with video data (such as Ego4D), the largest prior datasets available for robotics, since video offers observation-only experience without the action or reward annotations needed for RL methods. In this paper, we develop a system for leveraging large-scale human video datasets in robotic offline RL, based entirely on learning value functions via temporal-difference learning. We show that value learning on video datasets learns representations that are more conducive to downstream robotic offline RL than other approaches for learning from video data. Our system, called V-PTR, combines the benefits of pre-training on video data with robotic offline RL approaches that train on diverse robot data, resulting in value functions and policies for manipulation tasks that perform better, act robustly, and generalize broadly. On several manipulation tasks on a real WidowX robot, our framework produces policies that greatly improve over prior methods. Our video and additional details can be found at https://dibyaghosh.com/vptr/
Recurrent Temporal Revision Graph Networks

Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for temporal graphs, is currently straightforwardly extended from that of static graphs. It can be computationally expensive when involving all historical neighbors during such aggregation. In practice, typically only a subset of the most recent neighbors are involved. However, such subsampling leads to incomplete and biased neighbor information. To address this limitation, we propose a novel framework for temporal neighbor aggregation that uses the recurrent neural network with node-wise hidden states to integrate information from all historical neighbors for each node to acquire the complete neighbor information. We demonstrate the superior theoretical expressiveness of the proposed framework as well as its state-of-the-art performance in real-world applications. Notably, it achieves a significant +9.6% improvement on averaged precision in a real-world Ecommerce dataset over existing methods on 2-layer models.
Decoding Emotional Experiences in Dyadic Conversations of Married Couples: Leveraging Semantic Similarity through Sentence Embedding

Recent advancements in Natural Language Processing (NLP) have highlighted the potential of sentence embeddings in measuring semantic similarity (hereafter similarity). Yet, whether this approach can be used to analyze real-world dyadic interactions and predict people's emotional experiences in response to these interactions remains largely uncharted. To bridge this gap, the present study analyzes verbal conversations of 50 married couples who engage in naturalistic 10-minute conflict and 10-minute positive conversations. Transformer-based model General Text Embeddings-Large is employed to obtain the embeddings of the utterances from each speaker. The overall similarity of the conversations is then quantified by the average cosine similarity between the embeddings of adjacent utterances. Results show that lower similarity is associated with greater positive emotional experiences in the positive (but not conflict) conversation. Follow-up analyses show that (a) findings remain stable when controlling for marital satisfaction and the number of utterance pairs and (b) the similarity measure is valid in capturing critical features of a dyadic conversation. The present study underscores the potency of sentence embeddings in understanding links between interpersonal dynamics and individual emotional experiences, paving the way for innovative applications of NLP tools in affective and relationship science.