Stability and Motion around Equilibrium Points in the Rotating Plane-Symmetric Potential Field

This study presents a study of equilibrium points, periodic orbits, stabilities, and manifolds in a rotating plane symmetric potential field. It has been found that the dynamical behaviour near equilibrium points is completely determined by the structure of the submanifolds and subspaces. The non-degenerate equilibrium points are classified into twelve cases. The necessary and sufficient conditions for linearly stable, non resonant unstable and resonant equilibrium points are established. Furthermore, the results show that a resonant equilibrium point is a Hopf bifurcation point. In addition, if the rotating speed changes, two non degenerate equilibria may collide and annihilate each other. The theory developed here is lastly applied to two particular cases, motions around a rotating, homogeneous cube and the asteroid 1620 Geographos. We found that the mutual annihilation of equilibrium points occurs as the rotating speed increases, and then the first surface shedding begins near the intersection point of the x axis and the surface. The results can be applied to planetary science, including the birth and evolution of the minor bodies in the Solar system, the rotational breakup and surface mass shedding of asteroids, etc.Comment: 38 pages, 7 figures. arXiv admin note: text overlap with arXiv:1403.040

Context-Transformer: Tackling Object Confusion for Few-Shot Detection

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches.Comment: Accepted by AAAI-202

Minimizing the Makespan for Scheduling Problems with General Deterioration Effects

This paper investigates the scheduling problems with general deterioration models. By the deterioration models, the actual processing time functions of jobs depend not only on the scheduled position in the job sequence but also on the total weighted normal processing times of the jobs already processed. In this paper, the objective is to minimize the makespan. For the single-machine scheduling problems with general deterioration effects, we show that the considered problems are polynomially solvable. For the flow shop scheduling problems with general deterioration effects, we also show that the problems can be optimally solved in polynomial time under the proposed conditions

Lending Interaction Wings to Recommender Systems with Conversational Agents

Recommender systems trained on offline historical user behaviors are embracing conversational techniques to online query user preference. Unlike prior conversational recommendation approaches that systemically combine conversational and recommender parts through a reinforcement learning framework, we propose CORE, a new offline-training and online-checking paradigm that bridges a COnversational agent and REcommender systems via a unified uncertainty minimization framework. It can benefit any recommendation platform in a plug-and-play style. Here, CORE treats a recommender system as an offline relevance score estimator to produce an estimated relevance score for each item; while a conversational agent is regarded as an online relevance score checker to check these estimated scores in each session. We define uncertainty as the summation of unchecked relevance scores. In this regard, the conversational agent acts to minimize uncertainty via querying either attributes or items. Based on the uncertainty minimization framework, we derive the expected certainty gain of querying each attribute and item, and develop a novel online decision tree algorithm to decide what to query at each turn. Experimental results on 8 industrial datasets show that CORE could be seamlessly employed on 9 popular recommendation approaches. We further demonstrate that our conversational agent could communicate as a human if empowered by a pre-trained large language model.Comment: NeurIPS 202

Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank

Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existing solutions fall in the paradigm of probabilistic ranking principle (PRP), i.e., first score each item in the candidate set and then perform a sort operation to generate the top ranking list. However, these approaches neglect the contextual dependence among candidate items during individual scoring, and the sort operation is non-differentiable. To bypass the above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework directly generates the permutations of the candidate items without the need for individually scoring and sort operations; and is end-to-end differentiable. As a result, STARank can operate when only the ground-truth permutations are accessible without requiring access to the ground-truth relevance scores for items. For this purpose, STARank first reads the candidate items in the context of the user browsing history, whose representations are fed into a Plackett-Luce module to arrange the given items into a list. To effectively utilize the given ground-truth permutations for supervising STARank, we leverage the internal consistency property of Plackett-Luce models to derive a computationally efficient list-wise loss. Experimental comparisons against 9 the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3 top-N real-world recommendation datasets demonstrate the superiority of STARank in terms of conventional ranking metrics. Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases. STARank can consistently achieve better performance in terms of PBM and UBM simulation-based metrics.Comment: CIKM 202

Host genetic background rather than diet-induced gut microbiota shifts of sympatric black-necked crane, common crane and bar-headed goose

IntroductionGut microbiota of wild birds are affected by many factors, and host genetic background and diet are considered to be two important factors affecting their structure and function.MethodsIn order to clarify how these two factors influence the gut microbiota, this study selected the sympatric and closely related and similar-sized Black-necked Crane (Grus nigricollis) and Common Crane (Grus grus), as well as the distantly related and significantly different-sized Bar-headed Goose (Anser indicus). The fecal samples identified using sanger sequencing as the above three bird species were subjected to high-throughput sequencing of rbcL gene and 16S rRNA gene to identify the feeding types phytophagous food and gut microbiota.ResultsThe results showed significant differences in food diversity between black-necked cranes and Common Cranes, but no significant differences in gut microbiota, Potatoes accounted for approximately 50% of their diets. Bar-headed Geese mainly feed on medicinal plants such as Angelica sinensis, Alternanthera philoxeroides, and Ranunculus repens. Black-necked cranes and Common Cranes, which have a high-starch diet, have a similar degree of enrichment in metabolism and synthesis functions, which is significantly different from Bar-headed Geese with a high-fiber diet. The differences in metabolic pathways among the three bird species are driven by food. The feeding of medicinal plants promotes the health of Bar-headed Geese, indicating that food influences the functional pathways of gut microbiota. Spearman analysis showed that there were few gut microbiota related to food, but almost all metabolic pathways were related to food.ConclusionThe host genetic background is the dominant factor determining the composition of the microbiota. Monitoring the changes in gut microbiota and feeding types of wild birds through bird feces is of great reference value for the conservation of other endangered species

TBR2 coordinates neurogenesis expansion and precise microcircuit organization via Protocadherin 19 in the mammalian cortex.

Cerebral cortex expansion is a hallmark of mammalian brain evolution; yet, how increased neurogenesis is coordinated with structural and functional development remains largely unclear. The T-box protein TBR2/EOMES is preferentially enriched in intermediate progenitors and supports cortical neurogenesis expansion. Here we show that TBR2 regulates fine-scale spatial and circuit organization of excitatory neurons in addition to enhancing neurogenesis in the mouse cortex. TBR2 removal leads to a significant reduction in neuronal, but not glial, output of individual radial glial progenitors as revealed by mosaic analysis with double markers. Moreover, in the absence of TBR2, clonally related excitatory neurons become more laterally dispersed and their preferential synapse development is impaired. Interestingly, TBR2 directly regulates the expression of Protocadherin 19 (PCDH19), and simultaneous PCDH19 expression rescues neurogenesis and neuronal organization defects caused by TBR2 removal. Together, these results suggest that TBR2 coordinates neurogenesis expansion and precise microcircuit assembly via PCDH19 in the mammalian cortex