The Differentiable ARchiTecture Search (DARTS) has dominated the neural architecture search community due to its search efficiency and simplicity. DARTS leverages continuous relaxation to convert the intractable operation selection problem into a continuous magnitude optimization problem which can be easily handled with gradient-descent, while it poses an additional challenge in measuring the operation importance or selecting an architecture from the optimized magnitudes. The vanilla DARTS assumes the optimized magnitudes reflect the importance of operations, while more recent works find this naive assumption leads to poor generalization and is without any theoretical guarantees. In this work, we leverage influence functions, the functional derivatives of the loss function, to theoretically reveal the operation selection part in DARTS and estimate the candidate operation importance by approximating its influence on the supernet with Taylor expansions. We show the operation strength is not only related to the magnitude but also secondorder information, leading to a fundamentally new criterion for operation selection in DARTS, named Influential Magnitude. Empirical studies across different tasks on several spaces show that vanilla DARTS and its variants can avoid most failures by leveraging the proposed theory-driven operation selection criterion.

We consider escaping saddle points of nonconvex problems where only the function evaluations can be accessed. Although a variety of works have been proposed, the majority of them require either second or first-order information, and only a few of them have exploited zeroth-order methods, particularly the technique of negative curvature finding with zeroth-order methods which has been proven to be the most efficient method for escaping saddle points. To fill this gap, in this paper, we propose two zeroth-order negative curvature finding frameworks that can replace Hessian-vector product computations without increasing the iteration complexity. We apply the proposed frameworks to ZO-GD, ZO-SGD, ZO-SCSG, ZO-SPIDER and prove that these ZO algorithms can converge to (ϵ, δ)-approximate secondorder stationary points with less query complexity compared with prior zeroth-order works for finding local minima.

School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Emails: {23b903088, zhangzhaoxin, 22s030153, li.ning, 22b303010}@stu.hit.edu.cn School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China Email: xinran_Liu@bupt.edu.cn Abstract --Fine-grained IP geolocation plays a critical role in applications such as location-based services and cybersecurity. Most existing fine-grained IP geolocation methods are regression-based; however, due to noise in the input data, these methods typically encounter kilometer-level prediction errors and provide incorrect region information for users. T o address this issue, this paper proposes a novel hierarchical multi-label classification framework for IP region prediction, named HMCGeo. This framework treats IP geolocation as a hierarchical multi-label classification problem and employs residual connection-based feature extraction and attention prediction units to predict the target host region across multiple geographical granularities. Furthermore, we introduce probabilistic classification loss during training, combining it with hierarchical cross-entropy loss to form a composite loss function. IP region prediction experiments on the New Y ork, Los Angeles, and Shanghai datasets demonstrate that HMCGeo achieves superior performance across all geographical granularities, significantly outperforming existing IP geolocation methods. P geolocation is a technique used to predict the geographical location of a host based on its IP address [1], playing a crucial role in location-based services, network topology optimization, and cybersecurity [2], [3], [4], [5], [6], [7], [8]. Using IP geolocation technology, online services and applications infer the geographical location of users to deliver localized weather updates, news, and event notifications [3]. Internet service providers (ISPs) estimate the approximate location of target hosts to optimize traffic transmission paths, reduce network latency, and improve transmission efficiency [4]. Network analysts examine the geographical origins of incoming traffic to assess security threats from suspicious addresses. This research was supported by the National Key R&D Program of China (2024QY1103, 2018YFB18002). Based on the accuracy of prediction results, IP geolocation is categorized into coarse-grained and fine-grained geolocation. Coarse-grained IP geolocation predicts the location of a target host by utilizing allocation information such as Autonomous System Numbers (ASN), ISP, and BGP, or by analyzing the relationship between latency and distance. These methods construct geolocation databases that provide location information at the country or city level. Building on this foundation, fine-grained IP geolocation reduces prediction errors to a few kilometers in certain regions by leveraging richer landmarks or employing more effective prediction methods.

The Differentiable ARchiTecture Search (DARTS) has dominated the neural architecture search community due to its search efficiency and simplicity. DARTS leverages continuous relaxation to convert the intractable operation selection problem into a continuous magnitude optimization problem which can be easily handled with gradient-descent, while it poses an additional challenge in measuring the operation importance or selecting an architecture from the optimized magnitudes. The vanilla DARTS assumes the optimized magnitudes reflect the importance of operations, while more recent works find this naive assumption leads to poor generalization and is without any theoretical guarantees. In this work, we leverage influence functions, the functional derivatives of the loss function, to theoretically reveal the operation selection part in DARTS and estimate the candidate operation importance by approximating its influence on the supernet with Taylor expansions. We show the operation strength is not only related to the magnitude but also secondorder information, leading to a fundamentally new criterion for operation selection in DARTS, named Influential Magnitude. Empirical studies across different tasks on several spaces show that vanilla DARTS and its variants can avoid most failures by leveraging the proposed theory-driven operation selection criterion.

Abstract--Place recognition is a fundamental task for robotic application, allowing robots to perform loop closure detection within simultaneous localization and mapping (SLAM), and achieve re-localization on prior maps. Current range imagebased networks use single-column convolution to maintain feature invariance to shifts in image columns caused by LiDAR viewpoint change. However, this raises the issues such as restricted receptive fields and excessive focus on local regions, degrading the performance of networks. To address the aforementioned issues, we propose a lightweight circular convolutional Transformer network denoted as CCTNet, which boosts performance by capturing structural information in point clouds and facilitating cross-dimensional interaction of spatial and channel information. Through extensive experiments on the KITTI and Ford Campus datasets, CCTNet surpasses comparable methods, achieving Recall@1 of 0.924 and 0.965, Results on the self-collected dataset further demonstrate the proposed method's potential for practical Hai Zhang is with the Centre for Composite Materials and Structures, Harbin Institute of Technology, Harbin 150001, P.R.China (e-mail: Materials and Structures, Harbin Institute of Technology, Harbin 150001, P.R.China (e-mail: juehundt@hit.edu.cn). Rhling et al. [14] proposed In this paper, a circular convolutional Transformer network a statistical-based method called Fast Histogram algorithm, with a regression loss is proposed for place recognition task which generates a one-dimensional histogram as a descriptor in scenarios with movable object occlusion. It treats the range image as Moreover, Scan Context [11] employed the polar coordinate a ring, utilizing multi-column convolution to learn local feature to map the point cloud into a two-dimensional (2D) matrix details, relationships between range image columns, and along radial and angular directions, serving as descriptors for circular structural features of the point clouds. However, crafting manual features usually a Range Transformer module is proposed to dynamically allocate requires domain-specific expertise, and manual descriptors weights to various channels and pixel regions, enabling exhibit limited robustness in handling variations and uncertainties the fusion and interaction of information from both channel in complex scenes [15].

In view of the fact that most of the existing machine translation evaluation algorithms only consider the lexical and syntactic information, but ignore the deep semantic information contained in the sentence, this paper proposes a computational method for evaluating the semantic correctness of machine translations based on reference translations and incorporating semantic dependencies and sentence keyword information. Use the language technology platform developed by the Social Computing and Information Retrieval Research Center of Harbin Institute of Technology to conduct semantic dependency analysis and keyword analysis on sentences, and obtain semantic dependency graphs, keywords, and weight information corresponding to keywords. It includes all word information with semantic dependencies in the sentence and keyword information that affects semantic information. Construct semantic association pairs including word and dependency multi-features. The key semantics of the sentence cannot be highlighted in the semantic information extracted through semantic dependence, resulting in vague semantics analysis. Therefore, the sentence keyword information is also included in the scope of machine translation semantic evaluation. To achieve a comprehensive and in-depth evaluation of the semantic correctness of sentences, the experimental results show that the accuracy of the evaluation algorithm has been improved compared with similar methods, and it can more accurately measure the semantic correctness of machine translation.

Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened by adversarial samples [1]. Such adversarial attacks can be invisible to human eyes, but can lead to DNN misclassification, and often exhibits transferability between deep learning and machine learning models [2] and real-world achievability[3]. Adversarial attacks can be divided into white-box attacks (Section 2.1), for which the attacker knows the parameters and gradient of the model, and black-box attacks (Section 2.2), for the latter, the attacker can only obtain the input and output of the model. In terms of the attacker's purpose, it can be divided into targeted attacks and non-targeted attacks, which means that the attacker wants the model to misclassify the original sample into the specified class, which is more practical, while the non-targeted attack just needs to make the model misclassify the sample. The black box setting is a scenario we will encounter in practice. Black-box attacks can also be divided into query-based attacks, which require a lot of repeated query model output to adjust perturbations, while transfer-based attacks do not, which makes the latter easier to do because too many queries are not allowed in practice.Transferbased attacks often require the use of a white-box surrogate model to create adversarial perturbations, which are mostly developed from existing white-box attacks. Zhang Yutong - graduate student, Harbin Institute of Technology; e-mail: 22s112078@stu.hit.edu.cn Li Yao - assistant professor, Harbin Institute of Technology; e-mail: yaoli0508@hit.edu.cn,

A robot that uses artificial intelligence (AI), image recognition, automatic control and other technologies has been adopted in the city of Harbin, northeast China's Heilongjiang Province, to help with the training of curling athletes for the upcoming Beijing 2022 Olympic Winter Games.

When Chen Siyu met a consular official at the U.S. embassy in Beijing in March to review her qualifications for a student visa, “Everything was going well,” she says—or so it seemed. Chen, who has a master's in public health from the University of Hong Kong, had won a fully funded slot in an epidemiology Ph.D. program at the University of Florida. When the consular officer asked about her current employment, Chen explained that she had worked as an epidemiology research assistant at a major hospital for 5 years. She mentioned that the hospital is affiliated with a military medical university. The consular officer thanked Chen for the information and moments later handed her a rejection form letter with “Other: 212(f)” ticked off from among a selection of reasons. The interview was over, as were her dreams of earning a Ph.D. in the United States. Chen is one of a growing group of Chinese students barred from the United States based on 212(f), a clause in the decades-old Immigration and Nationality Act (INA) that allows the U.S. president to identify aliens whose entry would be “detrimental to the interests of the United States.” In May 2020, then-President Donald Trump signed a proclamation that invoked the clause to bar Chinese graduate students and postgraduate researchers with ties to an entity in China “that implements or supports China's ‘military-civil fusion strategy.’” The proclamation exempts those working in fields that don't contribute to that strategy—but apparently epidemiology is not among them. Now, Chen is one of 2500 activists—Chinese students with visa problems and their supporters—who are fighting back against what they see as an arbitrary and discriminatory policy. Armed with a website and a Twitter account, the students have written to more than 50 top U.S. research universities to focus attention on their plight. They are getting a sympathetic hearing in the U.S. academic world: A 10 June letter from the American Council on Education to the Department of State warned of “delays in students' academic careers and critical projects.” The group is also discussing legal action with a U.S. immigration lawyer and recently launched a fundraising campaign to try to cover the costs. “We think this is a policy of discrimination based on nationality,” says Hu Desheng, a doctoral candidate in computer science at Northeastern University who got stuck in China because of pandemic-related travel restrictions in early 2020, and whose visa application is now backlogged. Trump's proclamation initially had little impact because the pandemic disrupted academic travel globally. But after more than a year, the U.S. embassy and consulates in China resumed processing routine visa applications on 4 May. Between then and mid-June, more than 500 visa applications have been rejected, according to the students' tally. More than 1000 Chinese scholars already in the United States reportedly had their visas revoked by September 2020. Many others hesitate to leave the United States, fearing they won't get back in. How many students will be affected annually is unclear, in part because the U.S. government has not said which Chinese entities are deemed to be supporting the military-civil fusion strategy and which fields of study are considered sensitive or exempt. A study of the measure's potential impact published in February by Georgetown University's Center for Security and Emerging Technology (CSET) assumed the designated entities include 11 universities subject to stringent export control restrictions by the U.S. Department of Commerce, including the so-called Seven Sons of National Defence—schools with historical ties to China's defense establishment. The study also assumed the sensitive fields mentioned in the proclamation will cover all areas of science, technology, engineering, and math (STEM). If so, it could block 3000 to 5000 of the roughly 19,000 Chinese students who start graduate programs each year, CSET estimated. The report did not cover postdoctoral and visiting researchers, graduates of other universities, or those in non-STEM fields. (The proclamation exempts undergraduate students from scrutiny.) A spokesperson for the State Department declined to name which institutions are blacklisted, but said the sensitive technologies include quantum computing, big data, semiconductors, biotechnology, 5G, advanced nuclear technology, aerospace technology, and artificial intelligence. “By design, the policy is narrowly targeted,” the spokesperson says. But the Chinese students say rejections are broad. Even those intending to study finance, obstetrics and gynecology, water conservation, medicine, agronomy, and other seemingly nonmilitary topics have had visas rejected under clause 212(f), they say. Li Xiang, for example, earned a master's in linguistics from the Harbin Institute of Technology, one of the schools with historical defense ties, then studied at an art school to prepare for a master's program in game development at the Academy of Art University in San Francisco. “To be an artist in the game and film industry is my dream,” she says. Her application was rejected and she was told she is not even eligible for a visa to visit her husband, who is working in the United States. The visa of another student, Xue Shilue, was revoked in the summer of 2020 after she had completed the first year of a master's program in “user experience design” at the University of Texas, Austin. She happened to be in China at the time and can't go back to Austin to complete her degree or even collect her personal belongings. The proclamation also appears to target students supported by the China Scholarship Council (CSC), which falls under China's Ministry of Education but has been under scrutiny for supposed links to the defense establishment, according to a separate CSET study. Blacklisting CSC could have dramatic implications. CSET estimates that during the 2017–18 academic year, the council supported 26,000 Chinese scholars in all disciplines in the United States. Huang Yunan, who last year started a Ph.D. program in food science at Cornell University remotely because of the pandemic, was denied a visa after telling a consular officer about her CSC support during a May interview. More than 100 of some 500 CSC-supported members of a chat group she belongs to have recently had visa applications rejected, she says. The students object to the absence of any individual assessment. “There is a presumption of guilt on the part of every Chinese student who has studied at a targeted university,” Hu says. As to the Seven Sons, “We go to those schools because they are top-ranked universities,” Hu says, not because of their military ties. Wendy Wolford, vice provost for International Affairs at Cornell University, asked U.S. Secretary of State Antony Blinken in a 26 May letter to rectify the “capricious, unclear, and excessive” interpretations of the proclamation that are “creating tremendous uncertainty and confusion for international students and their U.S. universities.” (Wolford did not respond to an email asking whether she had heard back from Blinken.) A lawsuit, however, is a long shot, says Charles Kuck, a U.S. immigration lawyer who has advised the students. “The Supreme Court has given a literal carte blanche to the president to use INA 212(f), along with a ‘reasonable’ explanation, for whatever entry ban the president wants to put into place,” Kuck says. The problems are driving some students to pursue advanced degrees elsewhere; Chen, for one, will now get her Ph.D. at the University of Hong Kong. Moves like hers should be a bigger worry than the possibility that graduate students are stealing U.S. technology, says Denis Simon, an expert in innovation at Duke University who studies China's research efforts. “The notion of there being a conspiratorial effort [to acquire advanced technology] is just far beyond the reality.” In contrast, he says, slowing the flow of Chinese students will harm the United States, where they help sustain many research programs. “It's a pipeline that has been built over 40 years, and by deconstructing it, we will do some very serious damage to our ability to have the kind of talent needed to drive our innovation system forward.”

CNN are great blablabla… Let's get to the point. SOTA for image classification on Imagenet is EfficientNet with 88.5% top 1 accuracy in 2020. In this article, I introduce a combination of EfficientNet and Efficient Channel Attention (ECA) to highlight the results of the ECA paper from Tianjin/Dalian/Harbin universities. MobileNetV2 is composed of multiple blocks which are called linear bottlenecks or inverted residuals (they're almost the same). Linear Bottleneck is a residual layer composed of one 1x1 convolution, followed by a 3x3 depthwise convolution, then finally a 1x1 convolution.