Miracle-dz/UniFAR-FTUs huggingface dataset:
Preformer: Predictive Transformer with Multi-Scale Segment-Wise Correlations for Long-Term Time Series Forecasting

In long-term time series forecasting, most Transformer-based methods adopt the standard point-wise attention mechanism, which not only has high complexity but also cannot explicitly capture the predictive dependencies from contexts since the corresponding key and value are transformed from the same point. This paper proposes a predictive Transformer-based model called Preformer. Preformer introduces a novel efficient Multi-Scale Segment-Correlation mechanism that divides time series into segments and utilizes segment-wise correlation-based attention to replace point-wise attention. A multi-scale structure is developed to aggregate dependencies at different temporal scales and facilitate the selection of segment length. Preformer further designs a predictive paradigm for decoding, where the key and value come from two successive segments rather than the same segment. Experiments demonstrate that Preformer outperforms other Transformer-based models. The codes are available at https://github.com/ddz16/Preformer.
Node Polynomials for Curves on Surfaces

We complete the proof of a theorem we announced and partly proved in [Math. Nachr. 271 (2004), 69-90, math.AG/0111299]. The theorem concerns a family of curves on a family of surfaces. It has two parts. The first was proved in that paper. It describes a natural cycle that enumerates the curves in the family with precisely $r$ ordinary nodes. The second part is proved here. It asserts that, for $r\le 8$, the class of this cycle is given by a computable universal polynomial in the pushdowns to the parameter space of products of the Chern classes of the family.
Localizing Small Apples in Complex Apple Orchard Environments

The localization of fruits is an essential first step in automated agricultural pipelines for yield estimation or fruit picking. One example of this is the localization of apples in images of entire apple trees. Since the apples are very small objects in such scenarios, we tackle this problem by adapting the object proposal generation system AttentionMask that focuses on small objects. We adapt AttentionMask by either adding a new module for very small apples or integrating it into a tiling framework. Both approaches clearly outperform standard object proposal generation systems on the MinneApple dataset covering complex apple orchard environments. Our evaluation further analyses the improvement w.r. t. the apple sizes and shows the different characteristics of our two approaches.
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

In practical federated learning scenarios, the participating devices may have different bitwidths for computation and memory storage by design. However, despite the progress made in device-heterogeneous federated learning scenarios, the heterogeneity in the bitwidth speciﬁcations in the hardware has been mostly overlooked. We introduce a pragmatic FL scenario with bitwidth heterogeneity across the participating devices, dubbed as Bitwidth Heterogeneous Federated Learning (BHFL). BHFL brings in a new challenge, that the aggregation of model parameters with different bitwidths could result in severe performance degeneration, especially for high-bitwidth models. To tackle this problem, we propose ProWD framework, which has a trainable weight dequantizer at the central server that progressively reconstructs the low-bitwidth weights into higher bitwidth weights, and ﬁnally into full-precision weights. ProWD further selectively aggregates the model parameters to maximize the compatibility across bit-heterogeneous weights. We validate ProWD against relevant FL baselines on the benchmark datasets, using clients with varying bitwidths. Our ProWD largely outperforms the baseline FL algorithms as well as naive approaches (e.g. grouped averaging) under the proposed BHFL scenario.
General model of sex distribution, mating probability and egg production for macroparasites with polygamous mating system

The reproductive habits of helminths are important for the study of the dynamics of their transmission. For populations of parasites distributed by Poisson or negative binomial models, these habits have already been studied. However, there are other statistical models that describe these populations, such as zero-inflated models, but where reproductive characteristics were not analyzed. Using an arbitrary model for the parasite population, we model the distribution of females and males per host, and from these we model the different reproductive variables such as the mean number of fertile females, the mean egg production, the mating probability, the mean fertilized egg production. We show that these variables change due to the effects of a negative density-dependence fecundity, a characteristic of helminth parasites. We present the results obtained for some particular models.
High performance telescope system design for the TianQin project

China is planning to construct a new space-borne gravitational-wave (GW) observatory, the TianQin project, in which the spaceborne telescope is an important component in laser interferometry. The telescope is aimed to transmit laser beams between the spacecrafts for the measurement of the displacements between proof-masses in long arms. The telescope should have ultra-small wavefront deviation to minimize noise caused by pointing error, ultra-stable structure to minimize optical path noise caused by temperature jitter, ultra-high stray light suppression ability to eliminate background noise. In this paper, we realize a telescope system design with ultra-stable structure as well as ultra-low wavefront distortion for the space-based GW detection mission. The design requirements demand extreme control of high image quality and extraordinary stray light suppression ability. Based on the primary aberration theory, the initial structure design of the mentioned four-mirror optical system is explored. After optimization, the maximum RMS wavefront error is less than λ/300 over the full field of view, which meets the noise budget on the telescope design. The stray light noise caused by the back reflection of the telescope is also analyzed. The noise at the position of optical bench is less than 10−10 of the transmitted power, satisfying the requirements of space GW detection. We believe that our design can be a good candidate for TianQin project, and can also be a good guide for the space telescope design in any other similar science project.
Fast Sparse Classification for Generalized Linear and Additive Models

We present fast classification techniques for sparse generalized linear and additive models. These techniques can handle thousands of features and thousands of observations in minutes, even in the presence of many highly correlated features. For fast sparse logistic regression, our computational speed-up over other best-subset search techniques owes to linear and quadratic surrogate cuts for the logistic loss that allow us to efficiently screen features for elimination, as well as use of a priority queue that favors a more uniform exploration of features. As an alternative to the logistic loss, we propose the exponential loss, which permits an analytical solution to the line search at each iteration. Our algorithms are generally 2 to 5 times faster than previous approaches. They produce interpretable models that have accuracy comparable to black box models on challenging datasets.
Incorporating Social Norms into a Configurable Agent-Based Model of the Decision to Perform Commuting Behaviour

Interventions to increase active commuting have been recommended as a method to increase population physical activity, but evidence is mixed. Social norms related to travel behaviour may influence the uptake of active commuting interventions but are rarely considered in their design and evaluation. In this study we develop an agent-based model that incorporates social norms related to travel behaviour and demonstrate the utility of this through implementing car-free Wednesdays. A synthetic population of Waltham Forest, London, UK was generated using a microsimulation approach with data from the UK Census 2011 and UK HLS datasets. An agent-based model was created using this synthetic population which modelled how the actions of peers and neighbours, subculture, habit, weather, bicycle ownership, car ownership, environmental supportiveness, and congestion affect the decision to trave. The developed model (MOTIVATE) is a configurable agent-based model where social norms related to travel behaviour are used to provide a more realistic representation of the socio-ecological systems in which active commuting interventions may be deployed. The utility of this model is demonstrated using car-free days as a hypothetical intervention. In the control scenario, the odds of active travel were plausible at 0.091 (89% HPDI: [0.091, 0.091]). Compared to the control scenario, the odds of active travel were increased by 70.3% (89% HPDI: [70.3%, 70.3%]), in the intervention scenario, on non-car-free days; the effect is sustained to non-car-free days. The model is a useful tool for investigating the effect of how social networks and social norms influence the effectiveness of various interventions. If configured using real-world built environment data, it may be useful for investigating how social norms interact with the built environment to cause the emergence of commuting conventions.
Deep Reinforcement Learning: Opportunities and Challenges

This article is a gentle discussion about the field of reinforcement learning for real life, about opportunities and challenges, with perspectives and without technical details, touching a broad range of topics. The article is based on both historical and recent research papers, surveys, tutorials, talks, blogs, and books. Various groups of readers, like researchers, engineers, students, managers, investors, officers, and people wanting to know more about the field, may find the article interesting. In this article, we first give a brief introduction to reinforcement learning (RL), and its relationship with deep learning, machine learning and AI. Then we discuss opportunities of RL, in particular, applications in products and services, games, recommender systems, robotics, transportation, economics and finance, healthcare, education, combinatorial optimization, computer systems, and science and engineering. The we discuss challenges, in particular, 1) foundation, 2) representation, 3) reward, 4) model, simulation, planning, and benchmarks, 5) learning to learn a.k. a. meta-learning, 6) off-policy/offline learning, 7) software development and deployment, 8) business perspectives, and 9) more challenges. We conclude with a discussion, attempting to answer: “Why has RL not been widely adopted in practice yet? ” and “When is RL helpful? ”.
Lattices from Linear Codes: Source and Channel Networks

The paper addresses the fundamental information theoretic limits — in terms of achievable rates and distortions — in a broad class of multiterminal communication scenarios with general continuous-valued sources and channels. A general framework is presented which involves fine discretization of the source and channel variables followed by communication over the resulting discretized network. In order to evaluate fundamental performance limits under the proposed discretization process, convergence results for information measures are provided. The framework is used to study the distributed source coding in source coding, as well as the computation over multiple access channels in channel coding. In each case, a communication scheme is presented, the resulting achievable region is derived, and the region is evaluated for Gaussian sources and channels.
On the Global Well-Posedness and Decay of a Free Boundary Problem of the Navier–Stokes Equation in Unbounded Domains

In this paper, we establish the unique existence and some decay properties of a global solution of a free boundary problem of the incompressible Navier–Stokes equations in Lp in time and Lq in space framework in a uniformly H∞2 domain Ω⊂RN for N≥4. We assume the unique solvability of the weak Dirichlet problem for the Poisson equation and the Lq-Lr estimates for the Stokes semigroup. The novelty of this paper is that we do not assume the compactness of the boundary, which is essentially used in the case of exterior domains proved by Shibata. The restriction N≥4 is required to deduce an estimate for the nonlinear term G(u) arising from divv=0. However, we establish the results in the half space R+N for N≥3 by reducing the linearized problem to the problem with G=0, where G is the right member corresponding to G(u).
Hydrogen Burning of 29Si and Its Impact on Presolar Stardust Grains from Classical Novae

Presolar stardust grains found in primitive meteorites are believed to retain the isotopic composition of stellar outflows at the time of grain condensation. Therefore, laboratory measurements of their isotopic ratios represent sensitive probes for investigating open questions related to stellar evolution, stellar explosions, nucleosynthesis, mixing mechanisms, dust formation, and galactic chemical evolution. For a few selected presolar grains, classical novae have been discussed as a potential source. For SiC, silicate, and graphite presolar grains, the association is based on the observation of small N(12C)/N(13C) and N(14N)/N(15N) number abundance ratios compared to solar values, and abundance excesses in 30Si relative to 29Si, as previously predicted by models of classical novae. We report on a direct measurement of the 29Si(p,γ)30P reaction, which strongly impacts simulated δ 29Si values from classical novae. Our new experimental 29Si(p,γ)30P thermonuclear reaction rate differs from previous results by up to 50% in the classical nova temperature range (T = 100–400 MK), while the rate uncertainty is reduced by up to a factor of 3. Using our new reaction rate in Monte Carlo reaction network and hydrodynamic simulations of classical novae, we estimate δ 29Si values with much reduced uncertainties. Our results establish δ 29Si values measured in presolar grains as a sensitive probe for assessing their classical nova paternity. We also demonstrate that δ 30Si values from nova simulations are currently not a useful diagnostic tool unless the large uncertainty of the 30P(p,γ)31S reaction rate can be significantly reduced.
Fe b 20 22 THE TROPICAL ANALOGUES OF REPRODUCING KERNELS

Hilbertian kernel methods and their positive semidefinite kernels have known an extensive use in various fields of applied mathematics and machine learning, owing to their several equivalent characterizations. We here unveil an analogy with concepts from tropical geometry, proving that tropical positive semidefinite kernels are also endowed with equivalent viewpoints, stemming from Fenchel-Moreau conjugations. We give a tropical analogue of Aronszajn theorem, showing that these kernels correspond to a feature map, define monotonous operators, and generate max-plus function spaces endowed with a reproducing property. They furthermore include all the Hilbertian kernels classically studied as well as Monge arrays. However, two relevant notions of tropical reproducing kernels must be distinguished, based either on linear or sesquilinear interpretations. The sesquilinear interpretation is the most expressive one, since reproducing spaces then encompass classical max-plus spaces, such as those of (semi)convex functions. In contrast, in the linear interpretation, the reproducing kernels are characterized by a restrictive condition, von Neumann regularity.
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints

Robotic navigation has been approached as a problem of 3D reconstruction and planning, as well as an end-to-end learning problem. However, long-range navigation requires both planning and reasoning about local traversability, as well as being able to utilize general knowledge about global geography, in the form of a roadmap, GPS, or other side information providing important cues. In this work, we propose an approach that integrates learning and planning, and can utilize side information such as schematic roadmaps, satellite maps and GPS coordinates as a planning heuristic, without relying on them being accurate. Our method, ViKiNG, incorporates a local traversability model, which looks at the robot's current camera observation and a potential subgoal to infer how easily that subgoal can be reached, as well as a heuristic model, which looks at overhead maps for hints and attempts to evaluate the appropriateness of these subgoals in order to reach the goal. These models are used by a heuristic planner to identify the best waypoint in order to reach the final destination. Our method performs no explicit geometric reconstruction, utilizing only a topological representation of the environment. Despite having never seen trajectories longer than 80 meters in its training dataset, ViKiNG can leverage its image-based learned controller and goal-directed heuristic to navigate to goals up to 3 kilometers away in previously unseen environments, and exhibit complex behaviors such as probing potential paths and backtracking when they are found to be non-viable. ViKiNG is also robust to unreliable maps and GPS, since the low-level controller ultimately makes decisions based on egocentric image observations, using maps only as planning heuristics. For videos of our experiments, please check out our project page https://sites.google. com/view/viking-release.
Inference of affordances and active motor control in simulated agents

Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of such behavior from a computational neuroscience perspective. Based on the theory, we introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture, which processes sensorimotor information, infers behavior-relevant aspects of its world, and invokes highly flexible, goal-directed behavior. We show that our architecture, which is trained end-to-end to minimize an approximation of free energy, develops latent states that can be interpreted as affordance maps. That is, the emerging latent states signal which actions lead to which effects dependent on the local context. In combination with active inference, we show that flexible, goal-directed behavior can be invoked, incorporating the emerging affordance maps. As a result, our simulated agent flexibly steers through continuous spaces, avoids collisions with obstacles, and prefers pathways that lead to the goal with high certainty. Additionally, we show that the learned agent is highly suitable for zero-shot generalization across environments: After training the agent in a handful of fixed environments with obstacles and other terrains affecting its behavior, it performs similarly well in procedurally generated environments containing different amounts of obstacles and terrains of various sizes at different locations.
Research of Low-Energy Routing on Single-Lamp Monitoring of Airfield Lighting

To accomplish data collection by utilizing the limited resource in WSNs(Wireless Sensor Networks),and prolong the lifetime of the networks,a WSN routing protocol AL-CADE(Airfield Lighting-Clustering Algorithm based on Distance and Energy)based on logic zones was developed to monitor the airfield lighting in approach zone.In the protocol,the monitoring area was set several small logic zones according to geography locations of the nodes,and there were some advanced nodes placed in the logic zones as relay nodes.The cluster-head number of the nodes was optimized in the elliptical area,and the cluster-head election algorithm based on energy and distribution of nodes was put forward.The simulation results show that the protocol is effective in reducing node energy consumption and prolonging the network lifetime.The monitoring system of airfield lighting is essential for ensuring airplane safety during taking off and landing,and improving the work efficiency as well as guaranteeing ability of maintenance.
The Composition and Power of the Jet of the Broad-line Radio Galaxy 3C 120

We calculated the electron–positron pair-production rate at the base of the jet of 3C 120 due to collisions of photons from the hot accretion flow using the measurement of its average soft gamma-ray spectrum by the Compton Gamma Ray Observatory. We found that this rate approximately equals the flow rate of leptons emitting the observed synchrotron radio-to-IR spectrum of the jet core, calculated using the extended jet model following Blandford & Königl. This coincidence shows the jet composition is likely to be pair dominated. We then calculated the jet power in the bulk motion of ions and found it greatly exceeds that achievable by the magnetically arrested disk scenario for the maximum black hole spin unless the jet contains mostly pairs. Next, we found that the magnetic flux through the synchrotron-emitting jet equals the maximum poloidal flux that can thread the black hole. Finally, we compared two estimates of the magnetization parameter at the onset of the synchrotron emission and found they are in agreement only if pairs dominate the jet content.
How viscous bubbles collapse: topological and symmetry-breaking instabilities in curvature-driven hydrodynamics

The duality between deformations of elastic bodies and non-inertial flows in viscous liquids has been a guiding principle in decades of research. However, this duality is broken when a spheroidal or other doubly-curved liquid film is suddenly forced out of mechanical equilibrium, as occurs e.g. when the pressure inside a liquid bubble drops rapidly due to rupture or controlled evacuation. In such cases the film may evolve through a non-inertial yet geometrically-nonlinear surface dynamics, which has remained largely unexplored. We reveal the driver of such dynamics as temporal variations in the curvature of the evolving surface. Focusing on the prototypical example of a floating bubble that undergoes rapid depressurization, we show that the bubble surface evolves via a topological instability and a subsequent front propagation, whereby a small planar zone nucleates and expands in the spherically-shaped film, bringing about hoop compression and triggering another, symmetry-breaking instability and radial wrinkles that grow in amplitude and invade the flattening film. Our analysis reveals the dynamics as a non-equilibrium branch of"Jellium"physics, whereby a rate-of-change of surface curvature in a viscous film is akin to charge in an electrostatic medium that comprises polarizable and conducting domains. We explain key features underlying recent experiments and highlight a qualitative inconsistency between the prediction of linear stability analysis and the observed"wavelength"of surface wrinkles. Our analysis points to the existence of a nonlinear curvature-driven mechanism for pattern selection in viscous flows.
Finding Safe Zones of Markov Decision Processes Policies

Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset. The quality of a SafeZone is parameterized by the number of states and the escape probability, i.e., the probability that a random trajectory will leave the subset. SafeZones are especially interesting when they have a small number of states and low escape probability. We study the complexity of finding optimal SafeZones, and show that in general, the problem is computationally hard. Our main result is a bi-criteria approximation learning algorithm with a factor of almost $2$ approximation for both the escape probability and SafeZone size, using a polynomial size sample complexity.