Miracle-dz/UniFAR-FTUs huggingface dataset:
Driving rapidly while remaining in control: classical shortcuts from Hamiltonian to stochastic dynamics

Stochastic thermodynamics lays down a broad framework to revisit the venerable concepts of heat, work and entropy production for individual stochastic trajectories of mesoscopic systems. Remarkably, this approach, relying on stochastic equations of motion, introduces time into the description of thermodynamic processes—which opens the way to fine control them. As a result, the field of finite-time thermodynamics of mesoscopic systems has blossomed. In this article, after introducing a few concepts of control for isolated mechanical systems evolving according to deterministic equations of motion, we review the different strategies that have been developed to realize finite-time state-to-state transformations in both over and underdamped regimes, by the proper design of time-dependent control parameters/driving. The systems under study are stochastic, epitomized by a Brownian object immersed in a fluid; they are thus strongly coupled to their environment playing the role of a reservoir. Interestingly, a few of those methods (inverse engineering, counterdiabatic driving, fast-forward) are directly inspired by their counterpart in quantum control. The review also analyzes the control through reservoir engineering. Besides the reachability of a given target state from a known initial state, the question of the optimal path is discussed. Optimality is here defined with respect to a cost function, a subject intimately related to the field of information thermodynamics and the question of speed limit. Another natural extension discussed deals with the connection between arbitrary states or non-equilibrium steady states. This field of control in stochastic thermodynamics enjoys a wealth of applications, ranging from optimal mesoscopic heat engines to population control in biological systems.
Uncoupled Learning Dynamics with O(log T) Swap Regret in Multiplayer Games

In this paper we establish efficient and \emph{uncoupled} learning dynamics so that, when employed by all players in a general-sum multiplayer game, the \emph{swap regret} of each player after $T$ repetitions of the game is bounded by $O(\log T)$, improving over the prior best bounds of $O(\log^4 (T))$. At the same time, we guarantee optimal $O(\sqrt{T})$ swap regret in the adversarial regime as well. To obtain these results, our primary contribution is to show that when all players follow our dynamics with a \emph{time-invariant} learning rate, the \emph{second-order path lengths} of the dynamics up to time $T$ are bounded by $O(\log T)$, a fundamental property which could have further implications beyond near-optimally bounding the (swap) regret. Our proposed learning dynamics combine in a novel way \emph{optimistic} regularized learning with the use of \emph{self-concordant barriers}. Further, our analysis is remarkably simple, bypassing the cumbersome framework of higher-order smoothness recently developed by Daskalakis, Fishelson, and Golowich (NeurIPS'21).
Personality Traits in Game Development

Existing work on personality traits in software development excludes game developers as a discrete group. Whilst games are software, game development has unique considerations, so game developers may exhibit different personality traits from other software professionals. We assessed responses from 123 game developers on an International Personality Item Pool Five Factor Model scale and demographic questionnaire using factor analysis. Programmers reported lower Extraversion than designers, artists and production team members; lower Openness than designers and production, and reported higher Neuroticism than production—potentially linked to burnout and crunch time. Compared to published norms of software developers, game developers reported lower Openness, Conscientiousness, Extraversion and Agreeableness, but higher Neuroticism. These personality differences have many practical implications: differences in Extraversion among roles may precipitate communication breakdowns; differences in Openness may induce conflict between programmers and designers. Understanding the relationship between personality traits and roles can help recruiters steer new employees into appropriate roles, and help managers apply appropriate stress management techniques. To realise these benefits, individuals must be distinguished from roles: just because an individual occupies a role does not mean they possess personality traits associated with that role.
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?

Task specification is at the core of programming autonomous robots. A low-effort modality for task specification is critical for engagement of non-expert end-users and ultimate adoption of personalized robot agents. A widely studied approach to task specification is through goals, using either compact state vectors or goal images from the same robot scene. The former is hard to interpret for non-experts and necessitates detailed state estimation and scene understanding. The latter requires the generation of desired goal image, which often requires a human to complete the task, defeating the purpose of having autonomous robots. In this work, we explore alternate and more general forms of goal specification that are expected to be easier for humans to specify and use such as images obtained from the internet, hand sketches that provide a visual description of the desired task, or simple language descriptions. As a preliminary step towards this, we investigate the capabilities of large scale pre-trained models (foundation models) for zero-shot goal specification, and find promising results in a collection of simulated robot manipulation tasks and real-world datasets.
PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework

Time-domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions. Given the urgency of conducting follow-up observations for such objects, the development of an algorithm capable of detecting them and determining their magnitudes and positions has become imperative. Leveraging the advancements in deep neural networks, we present PNet, an end-to-end framework designed not only to detect celestial objects and extract their magnitudes and positions, but also to estimate the photometric uncertainty. PNet comprises two essential steps. First, it detects stars and retrieves their positions, magnitudes, and calibrated magnitudes. Subsequently, in the second phase, PNet estimates the uncertainty associated with the photometry results, serving as a valuable reference for the light-curve classification algorithm. Our algorithm has been tested using both simulated and real observation data, demonstrating the ability of PNet to deliver consistent and reliable outcomes. Integration of PNet into data-processing pipelines for time-domain astronomy holds significant potential for enhancing response speed and improving the detection capabilities for celestial objects with variable positions and magnitudes.
On Stute's representation for a class of smooth, possibly data-adaptive empirical copula processes

Given a random sample from a continuous multivariate distribution, Stute’s representation is obtained for empirical copula processes constructed from a broad class of smooth, possibly data-adaptive nonparametric copula estimators. The latter class contains for instance empirical Bernstein copulas introduced by Sancetta and Satchell and thus the empirical beta copula proposed by Segers, Sibuya and Tsukahara. The almost sure rate in Stute’s representation is expressed in terms of a parameter controlling the speed at which the spread of the smoothing region decreases as the sample size increases.
Simulating bulk viscosity in neutron stars. II. Evolution in spherical symmetry

Out-of-equilibrium reactions between different particle species are the main processes contributing to bulk viscosity in neutron stars. In this work, we numerically compare three different approaches to the modeling of bulk viscosity: the multi-component fluid with reacting particle species and two bulk stress formalism based on the M\"uller-Israel-Stewart theory, namely the Hiscock-Lindblom and the Maxwell-Cattaneo models, whose flux-conservative formulation in radial gauge-polar slicing coordinates and spherical symmetry is derived in a companion paper. To our knowledge, this is the first time that a neutron star is simulated with the complete Hiscock-Lindblom model of bulk viscosity. We find that the Hiscock-Lindblom and Maxwell-Cattaneo models are good approximations of the multi-component fluid for small perturbations and when the non-equilibrium equation of state of the fluid depends on only one independent particle fraction. For more than one independent particle fraction and for large perturbations, the bulk stress approximation is still valid but less accurate. In addition, we include the energy loss due to the luminosity of the reactions in the bulk stress formulation. We find that the energy loss due to bulk viscosity has a larger effect on the dynamics than the bulk stress or the variation in particle composition per se. The new one-dimensional, general-relativistic hydrodynamic code developed for this work, hydro-bulk-1D, is publicly available.
Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem

Smart speakers collect voice commands, which can be used to infer sensitive information about users. Given the potential for privacy harms, there is a need for greater transparency and control over the data collected, used, and shared by smart speaker platforms as well as third party skills supported on them. To bridge this gap, we build a framework to measure data collection, usage, and sharing by the smart speaker platforms. We apply our framework to the Amazon smart speaker ecosystem. Our results show that Amazon and third parties, including advertising and tracking services that are unique to the smart speaker ecosystem, collect smart speaker interaction data. We also find that Amazon processes smart speaker interaction data to infer user interests and uses those inferences to serve targeted ads to users. Smart speaker interaction also leads to ad targeting and as much as 30X higher bids in ad auctions, from third party advertisers. Finally, we find that Amazon's and third party skills' data practices are often not clearly disclosed in their policy documents.
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning

Reasoning about visual relationships is central to how humans interpret the visual world. This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pairs of entities, and 3) generalizing to novel object-relation combinations, i.e., systematic generalization. In this work, we use vision transformers (ViTs) as our base model for visual reasoning and make better use of concepts defined as object entities and their relations to improve the reasoning ability of ViTs. Specifically, we introduce a novel concept-feature dictionary to allow flexible image feature retrieval at training time with concept keys. This dictionary enables two new concept-guided auxiliary tasks: 1) a global task for promoting relational reasoning, and 2) a local task for facilitating semantic object-centric correspondence learning. To examine the systematic generalization of visual reasoning models, we introduce systematic splits for the standard HICO and GQA benchmarks. We show the resulting model, Concept-guided Vision Transformer (or RelViT for short) significantly outperforms prior approaches on HICO and GQA by 16% and 13% in the original split, and by 43% and 18% in the systematic split. Our ablation analyses also reveal our model's compatibility with multiple ViT variants and robustness to hyper-parameters.
Orbital Dynamics and Extreme Scattering Event Properties from Long-term Scintillation Observations of PSR J1603−7202

We model long-term variations in the scintillation of binary pulsar PSR J1603−7202, observed by the 64 m Parkes radio telescope (Murriyang) between 2004 and 2016. We find that the time variation in the scintillation arc curvature is well-modeled by scattering from an anisotropic thin screen of plasma between the Earth and the pulsar. Using our scintillation model, we measure the inclination angle and longitude of ascending node of the orbit, yielding a significant improvement over the constraints from pulsar timing. From our measurement of the inclination angle, we place a lower bound on the mass of J1603−7202's companion of ≳0.5 M ⊙ assuming a pulsar mass of ≳1.2 M ⊙. We find that the scintillation arcs are most pronounced when the electron column density along the line of sight is increased, and that arcs are present during a known extreme scattering event. We measure the distance to the interstellar plasma and its velocity, and we discuss some structures seen in individual scintillation arcs within the context of our model.
DESIGN AND DEVELOPMENT OF SAFE ENCLOSURE FOR TRACTOR OPERATORS

DESIGN AND DEVELOPMENT OF SAFE ENCLOSURE FOR TRACTOR OPERATORS Dust and heat exposure is severe when tractor operators perform agricultural operation in hot summer season. So, there is a need to provide comfort inside the cabin to protect the operator from the severity of dust and heat, as well as to improve the efficiency of the tractor operators. It was found that the mean dust concentration during secondary tillage operation with cultivator was 18.5 mg/m 3 and transportation 4.4 mg/m 3 without protective system. During summer, temperature varies 38.8 o C to 43 o C and Relative humidity 11.6 to 17%. This high temperature and dusty environment due to agricultural operations significantly affect operator physiological parameters (heart rate) and comfort. The mean heart rate was 130 beats min -1 for secondary tillage with cultivator and 112 beats min -1 for transportation. The overall discomfort score was 3.8 for secondary tillage operation and 2.4 for transportation. Tractor operator protective system was designed to mitigate the dust and temperature exposure. Square hollow mild steel bar (37.5x37.5x3mm) was used to fabricate the cab frame and 4mm thick polycarbonate sheet used as covering material. Wet pad evaporative cooling system was developed to improve the working efficiency and lengthen the effective working time of tractor operators during the summer who cannot afford an expensive air-conditioned cab. The cab is cooled by passing air through a wet evaporative surface, made up of 35-mm-thick layer of shredded wood packaged between the wire mesh. The evaporative surface is kept continuously wet with recirculation of water by a low-capacity, water pump installed at the bottom of a water tank. The air cooled by evaporation of water from the evaporative surface is sucked into the cab through a duct by a fan located at the center of the cab roof. The motor of the pump and fan are energized by a 12-V DC battery of the tractor. At tractor speed of 5 kmph, relative air velocity at the face of the pad was about 1.1 m/s. Evaluation of protective enclosure was done with different field operation and at different environmental parameters level. The temperature significantly reduces from 43 o C to 33.25 o C with the relative humidity 32% to 39% for secondary tillage operation and 42 o C
Retrieval-Augmented Diffusion Models

Generative image synthesis with diffusion models has recently achieved excellent visual quality in several tasks such as text-based or class-conditional image synthesis. Much of this success is due to a dramatic increase in the computational capacity invested in training these models. This work presents an alternative approach: inspired by its successful application in natural language processing, we propose to complement the diffusion model with a retrieval-based approach and to introduce an explicit memory in the form of an external database. During training, our diffusion model is trained with similar visual features retrieved via CLIP and from the neighborhood of each training instance. By leveraging CLIP’s joint image-text embedding space, our model achieves highly competitive performance on tasks for which it has not been explicitly trained, such as class-conditional or text-image synthesis, and can be conditioned on both text and image embeddings. Moreover, we can apply our approach to unconditional generation, where it achieves state-of-the-art performance. Our approach incurs low computational and memory overheads and is easy to implement. We discuss its relationship to concurrent work and will publish code and pretrained models soon.
Asymptotic of the dissipative eigenvalues of Maxwell’s equations

Let Ω = R 3 ∖ K ¯, where K is an open bounded domain with smooth boundary Γ. Let V ( t ) = e t G b , t ⩾ 0, be the semigroup related to Maxwell’s equations in Ω with dissipative boundary condition ν ∧ ( ν ∧ E ) + γ ( x ) ( ν ∧ H ) = 0, γ ( x ) > 0, ∀ x ∈ Γ. We study the case when γ ( x ) ≠ 1, ∀ x ∈ Γ, and we establish a Weyl formula for the counting function of the eigenvalues of G b in a polynomial neighbourhood of the negative real axis.
Numerically analyzing self-interacting dark matter

We consider the scenario of self-interacting dark matter(SIDM) with a light mediator in a model-independent way, which can alleviate two long-standing issues of the small scale cosmology namely cusp vs. core and too-big-to-fail. A Yukawa potential is chosen to achieve mediator exchange between DM particles as part of their self-interactions. The dynamics of self-interacting transfer cross-section are studied for a range of mediator mass($m_Z'$). Also, a relationship is established between the cross-section and DM particles' relative velocity, which ensures the solution to the DM crisis at small scales. Our obtained numerical results are efficient compared to the earlier works in the context that a lesser number of $\ell$ modes have been used by us to achieve the same level of accuracy in the cross-section calculations. For a better understanding of the SIDM parameter space, we perform an analytical analysis on the dependence of transfer cross-section over the other important SIDM parameters using a Hulth\'{e}n potential which is similar in its behavior to Yukawa potential. A detailed evolution of particle dynamics using the Boltzmann equation and the effect of Sommerfeld enhancement on such calculations has also been studied here. We also provide a minimal anomaly-free leptophilic extension of the standard model, that can incorporate SIDM and its mediator candidate in the framework.
Adaptive regression testing strategies for cost-effective regression testing

Regression testing is an important part of the software development life-cycle. However, it is also very expensive. Many different techniques have been proposed for reducing the cost of regression testing. To date, much research has been performed comparing regression testing techniques, but very little research has been performed to aid practitioners and researchers in choosing the most cost-effective technique for a particular regression testing session. One recent study investigated this problem and proposed Adaptive Regression Testing (ART) strategies to aid practitioners in choosing the most cost-effective technique for a specific version of a software system. The results of this study showed that the techniques chosen by the ART strategy were more cost-effective than techniques that did not consider system lifetime and testing processes. This work has several limitations, however. First, it only considers one ART strategy. There are many other strategies which could be developed and studied that could be more cost-effective. Second, the ART strategy used the Analytical Hierarchy Process (AHP). The AHP method is subjective to the weights made by the decision maker. Also, the AHP method is very time consuming because it requires many pairwise comparisons. Pairwise comparisons also limit the scalability of the approach and are often found to be inconsistent. This work proposes three new ART strategies to address these limitations. One strategy utilizing the fuzzy AHP method is proposed to address imprecision in the judgment made by the decision maker. A second strategy utilizing a fuzzy expert system is proposed to reduce the time required by the decision maker, eliminate inconsistencies
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training

Recent studies on the lottery ticket hypothesis (LTH) show that pre-trained language models (PLMs) like BERT contain matching subnetworks that have similar transfer learning performance as the original PLM. These subnetworks are found using magnitude-based pruning. In this paper, we find that the BERT subnetworks have even more potential than these studies have shown. Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability. Inspired by this, we propose to directly optimize the subnetwork structure towards the pre-training objectives, which can better preserve the pre-training performance. Specifically, we train binary masks over model weights on the pre-training tasks, with the aim of preserving the universal transferability of the subnetwork, which is agnostic to any specific downstream tasks. We then fine-tune the subnetworks on the GLUE benchmark and the SQuAD dataset. The results show that, compared with magnitude pruning, mask training can effectively find BERT subnetworks with improved overall performance on downstream tasks. Moreover, our method is also more efficient in searching subnetworks and more advantageous when fine-tuning within a certain range of data scarcity. Our code is available at https://github.com/llyx97/TAMT.
Robust inference for nondestructive one‐shot device testing under step‐stress model with exponential lifetimes

One‐shot devices analysis involves an extreme case of interval censoring, wherein one can only know whether the failure time is either before or after the test time. Some kind of one‐shot devices do not get destroyed when tested, and so can continue within the experiment, providing extra information for inference, if they did not fail before an inspection time. In addition, their reliability can be rapidly estimated via accelerated life tests (ALTs) by running the tests at varying and higher stress levels than working conditions. In particular, step‐stress tests allow the experimenter to increase the stress levels at prefixed times gradually during the life‐testing experiment. The cumulative exposure model is commonly assumed for step‐stress models, relating the lifetime distribution of units at one stress level to the lifetime distributions at preceding stress levels. In this paper, we develop robust estimators and Z‐type test statistics based on the density power divergence (DPD) for testing linear null hypothesis for nondestructive one‐shot devices under the step‐stress ALTs with exponential lifetime distribution. We study asymptotic and robustness properties of the estimators and test statistics, yielding point estimation and confidence intervals for different lifetime characteristic such as reliability, distribution quantiles, and mean lifetime of the devices. A simulation study is carried out to assess the performance of the methods of inference developed here and some real‐life data sets are analyzed finally for illustrative purpose.