Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Index Item IDs for Recommendation Foundation Models

Recommendation foundation model utilizes large language models (LLM) for recommendation by converting recommendation tasks into natural language tasks. It enables generative recommendation which directly generates the item(s) to recommend rather than calculating a ranking score for each and every candidate item as in traditional recommendation models, simplifying the recommendation pipeline from multi-stage filtering to single-stage filtering. To avoid generating excessively long text and hallucinated recommendations when deciding which item(s) to recommend, creating LLM-compatible item IDs to uniquely identify each item is essential for recommendation foundation models. In this study, we systematically examine the item ID creation and indexing problem for recommendation foundation models, using P5 as an example of the backbone LLM. To emphasize the importance of item indexing, we first discuss the issues of several trivial item indexing methods, such as random indexing, title indexing, and independent indexing. We then propose four simple yet effective solutions, including sequential indexing, collaborative indexing, semantic (content-based) indexing, and hybrid indexing. Our study highlights the significant influence of item indexing methods on the performance of LLM-based recommendation, and our results on real-world datasets validate the effectiveness of our proposed solutions. The research also demonstrates how recent advances on language modeling and traditional IR principles such as indexing can help each other for better learning and inference. Source code and data are available at https://github.com/Wenyueh/LLM-RecSys-ID.
Chain-of-Dictionary Prompting Elicits Translation in Large Language Models

Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data. Yet, despite the fact that the amount of training data is gigantic, they still struggle with translating rare words, particularly for low-resource languages. Even worse, it is usually unrealistic to retrieve relevant demonstrations for in-context learning with low-resource languages on LLMs, which restricts the practical use of LLMs for translation -- how should we mitigate this problem? To this end, we present a novel method, CoD, which augments LLMs with prior knowledge with the chains of multilingual dictionaries for a subset of input words to elicit translation abilities for LLMs. Extensive experiments indicate that augmenting ChatGPT with CoD elicits large gains by up to 13x chrF++ points for MNMT (3.08 to 42.63 for English to Serbian written in Cyrillic script) on FLORES-200 full devtest set. We further demonstrate the importance of chaining the multilingual dictionaries, as well as the superiority of CoD to few-shot demonstration for low-resource languages.
Dependence over subgroups of free groups

Given a finitely generated subgroup $H$ of a free group $F$, we present an algorithm which computes $g_1,\ldots,g_m\in F$, such that the set of elements $g\in F$, for which there exists a non-trivial $H$-equation having $g$ as a solution, is, precisely, the disjoint union of the double cosets $H\sqcup Hg_1H\sqcup \cdots \sqcup Hg_mH$. Moreover, we present an algorithm which, given a finitely generated subgroup $H\leqslant F$ and an element $g\in F$, computes a finite set of elements of $H * \langle x \rangle$ that generate (as a normal subgroup) the ``ideal"$I_H(g) \unlhd H * \langle x \rangle$ of all ``polynomials"$w(x)$, such that $w(g)=1$. The algorithms, as well as the proofs, are based on the graph-theory techniques introduced by Stallings and on the more classical combinatorial techniques of Nielsen transformations. The key notion here is that of dependence of an element $g\in F$ on a subgroup $H$. We also study the corresponding notions of dependence sequence and dependence closure of a subgroup.
Minority Stress Experienced by LGBTQ Online Communities during the COVID-19 Pandemic

The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although extensive research has been carried out on the impact of the COVID-19 pandemic on different aspects of the general population's lives, few studies are focused on the LGBTQ population. In this paper, we develop and evaluate two sets of machine learning classifiers using a pre-pandemic and a during-pandemic dataset to identify Twitter posts exhibiting minority stress, which is a unique pressure faced by the members of the LGBTQ population due to their sexual and gender identities. We demonstrate that our best pre- and during-pandemic models show strong and stable performance for detecting posts that contain minority stress. We investigate the linguistic differences in minority stress posts across pre- and during-pandemic periods. We find that anger words are strongly associated with minority stress during the COVID-19 pandemic. We explore the impact of the pandemic on the emotional states of the LGBTQ population by adopting propensity score-based matching to perform a causal analysis. The results show that the LGBTQ population have a greater increase in the usage of cognitive words and worsened observable attribute in the usage of positive emotion words than the group of the general population with similar pre-pandemic behavioral attributes. Our findings have implications for the public health domain and policy-makers to provide adequate support, especially with respect to mental health, to the LGBTQ population during future crises.
Coil sketching for computationally efficient MR iterative reconstruction

Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially for three‐dimensional (3D) non‐Cartesian acquisitions. One common approach is to reduce the number of coil channels actively used during reconstruction as in coil compression. While effective for Cartesian imaging, coil compression inherently loses signal energy, producing shading artifacts that compromise image quality for 3D non‐Cartesian imaging. We propose coil sketching, a general and versatile method for computationally‐efficient iterative MR image reconstruction.
Multiple polaritonic edge states in a Su-Schrieffer-Heeger chain strongly coupled to a multimode cavity

A dimerized chain of dipolar emitters strongly coupled to a multimode optical waveguide cavity is studied. By integrating out the photonic degrees of freedom of the cavity, the system is recast in a two-band model with an effective coupling, so that it mimics a variation of the paradigmatic Su-Schrieffer-Heeger model, which features a nontrivial topological phase and hosts topological edge states. In the strong-coupling regime, the cavity photons hybridize the bright dipolar bulk band into a polaritonic one, renormalizing the eigenspectrum and strongly breaking chiral symmetry. This leads to a formal loss of the in-gap edge states present in the topological phase while they merge into the polaritonic bulk band. Interestingly, however, we find that bulk polaritons entering in resonance with the edge states inherit part of their localization properties, so that multiple polaritonic edge states are observed. Although these states are not fully localized on the edges, they present unusual properties. In particular, due to their delocalized bulk part, owing from their polaritonic nature, such edge states exhibit efficient edge-to-edge transport characteristics. Instead of being degenerate, they occupy a large portion of the spectrum, allowing one to probe them in a wide driving frequency range. Moreover, being reminiscent of symmetry-protected topological edge states, they feature a strong tolerance to positional disorder.
Swift J1644+57 as an off-axis Jet

One of the intriguing puzzles concerning Swift J1644+57, the first jetted tidal disruption event (TDE) discovered, is the constant increase in its jet energy, as implied by radio observations. During the first two hundred days, the jet energy has increased by an order of magnitude. We suggest that the jet was viewed slightly off-axis. In this case, the apparent energy increase arises due to the slowing down of the jet and the corresponding broadening of its beaming cone. Using equipartition analysis, we infer an increasing jet energy as a larger region of the jet is observed. A simple off-axis model accounts nicely for the multi-wavelength radio observations, resolving this long-standing puzzle. The model allows us to self-consistently evolve the synchrotron signature from an off-axis jet as a function of time. It also allows us to estimate, for the first time, the beaming angle of the jet, θ0 ≈ 21°. Considering existing limits on the black hole mass, ≲107 M⊙, this angle implies that the prompt phase beaming corrected luminosity of Swift J1644+57, ∼1047 ergs sec−1, was super Eddington. We also present a closure relation between the spectral and temporal flux for off-axis jets, which can be used to test whether a given radio transient is off-axis or not.
GPU-initiated Fine-grained Overlap of Collective Communication with Computation

In order to satisfy their ever increasing capacity and compute requirements, many machine learning models are distributed across multiple nodes using space-efficient parallelism strategies. As a result, collective communications are often on the critical path, and hiding their latency by overlapping kernel-granular communication and computation is difficult due to the absence of independent computation. In this work, we propose fusing computation with communication using GPU-initiated networking, and leverage GPUs' massive parallelism to enable fine-grained overlap of the fused operations. We have developed a single, self-contained GPU kernel where workgroups (WGs) immediately communicate their results to remote GPUs when they complete their computation. Meanwhile, other WGs within the same kernel perform overlapping computation, maintaining high ALU utilization. Furthermore, we propose zero-copy optimizations for peer-to-peer GPU communication where the data computed by one GPU is directly written to the destination buffers within the peer GPUs, eliminating intermediate stores and extra buffering. Our approach leverages the emerging multi-node GPU system trend where GPUs are physically close to network with direct GPU-NIC interconnects. We demonstrate our approach by creating an embedding + All-to-All fused kernel which overlaps embedding operations and the dependent all-to-all collective in DLRM models. We evaluate our approach both using simulation and real hardware. Our evaluations show that our approach can effectively overlap All-to-All communication with embedding computations, subsequently reducing their combined execution time by 31% on average (up to 58%) for inter-node and by 25% (up to 35%) for intra-node configurations. Scale-out simulations indicate that our approach reduces DLRM execution time by ~10% for 128 node system.
Accelerating Batch Active Learning Using Continual Learning Techniques

A major problem with Active Learning (AL) is high training costs since models are typically retrained from scratch after every query round. We start by demonstrating that standard AL on neural networks with warm starting fails, both to accelerate training and to avoid catastrophic forgetting when using fine-tuning over AL query rounds. We then develop a new class of techniques, circumventing this problem, by biasing further training towards previously labeled sets. We accomplish this by employing existing, and developing novel, replay-based Continual Learning (CL) algorithms that are effective at quickly learning the new without forgetting the old, especially when data comes from an evolving distribution. We call this paradigm Continual Active Learning (CAL). We show CAL achieves significant speedups using a plethora of replay schemes that use model distillation and that select diverse, uncertain points from the history. We conduct experiments across many data domains, including natural language, vision, medical imaging, and computational biology, each with different neural architectures and dataset sizes. CAL consistently provides a 3x reduction in training time, while retaining performance.
An Asynchronous Massive Access Scheme with Dynamic Range Considerations

This paper studies the performance of a transmission and reception scheme for massive access under some practical challenges. One challenge is the near-far problem, i.e., an access point often receives signals from different transmitting devices at vastly different signal strengths. Another challenge is that the signals from different devices may be subject to arbitrary, analog, and heterogeneous delays. This paper considers a fully asynchronous model which is more realistic than the frame or symbol level synchrony needed in most existing work. A main theorem characterizes the asymptotic scaling of the codelength with the number of devices, a device delay upper bound, and the dynamic range of received signal strengths across devices. The scaling result suggests potential advantages of grouping devices with similar received signal strengths and letting the groups use time sharing. The performance of the proposed scheme is evaluated using simulations with and without grouping.
Effects of topological structure and destination selection strategies on agent dynamics in complex networks

We analyzed agent behavior in complex networks: Barabási–Albert, Erdos–Rényi, and Watts–Strogatz models under the following rules: agents (a) randomly select a destination among adjacent nodes; (b) exclude the most congested adjacent node as a potential destination and randomly select a destination among the remaining nodes; or (c) select the sparsest adjacent node as a destination. We focused on small complex networks with node degrees ranging from zero to a maximum of approximately 20 to study agent behavior in traffic and transportation networks. We measured the hunting rate, that is, the rate of change of agent amounts in each node per unit of time, and the imbalance of agent distribution among nodes. Our simulation study reveals that the topological structure of a network precisely determines agent distribution when agents perform full random walks; however, their destination selections alter the agent distribution. Notably, rule (c) makes hunting and imbalance rates significantly high compared with random walk cases (a) and (b), irrespective of network types, when the network has a high degree and high activity rate. Compared with the full random walk in (a) and (b) increases the hunting rate while decreasing the imbalance rate when activity is low; however, both increase when activity is high. These characteristics exhibit slight periodic undulations over time. Furthermore, our analysis shows that in the BA, ER, and WS network models, the hunting rate decreases and the imbalance rate increases when the system disconnects randomly selected nodes in simulations where agents follow rules (a)–(c) and the network has the ability to disconnect nodes within a certain time of all time steps. Our findings can be applied to various applications related to agent dynamics in complex networks.
Topology Optimization of self-contacting structures

Inclusion of contact in mechanical designs opens a large range of design possibilities, this includes classical designs with contact, such as gears, couplings, switches, clamps etc. However, incorporation of contact in topology optimization is challenging, as classical contact models are not readily applicable when the boundaries are not defined. This paper aims to address the limitations of contact in topology optimization by extending the third medium contact method for topology optimization problems with internal contact. When the objective is to maximize a given contact load for a specified displacement, instabilities may arise as an optimum is approached. In order to alleviate stability problems as well as provide robustness of the optimized designs, a tangent stiffness requirement is introduced to the design objective. To avoid a non-physical exploitation of the third medium in optimized designs, small features are penalized by evaluating the volume constraint on a dilated design. The present work incorporates well-established methods in topology optimization including Helmholtz PDE filtering, threshold projection, Solid Isotropic Material Interpolation with Penalization, and the Method of Moving Asymptotes. Three examples are used to illustrate how the approach exploits internal contact in the topology optimization of structures subjected to large deformations.
Weyl structures with special holonomy on compact conformal manifolds

We consider compact conformal manifolds $(M,[g])$ endowed with a closed Weyl structure $\nabla$, i.e. a torsion-free connection preserving the conformal structure, which is locally but not globally the Levi-Civita connection of a metric in $[g]$. Our aim is to classify all such structures when both $\nabla$ and $\nabla^g$, the Levi-Civita connection of $g$, have special holonomy. In such a setting, $(M,[g],\nabla)$ is either flat, or irreducible, or carries a locally conformally product (LCP) structure. Since the flat case is already completely classified, we focus on the last two cases. When $\nabla$ has irreducible holonomy we prove that $(M,g)$ is either Vaisman, or a mapping torus of an isometry of a compact nearly K\"ahler or nearly parallel $\mathrm{G}_2$ manifold, while in the LCP case we prove that $g$ is neither K\"ahler nor Einstein, thus reducible by the Berger-Simons Theorem, and we obtain the local classification of such structures in terms of adapted metrics.
A Diagonal Splitting Algorithm for Adaptive Group Testing

Group testing enables to identify infected individuals in a population using a smaller number of tests than individual testing. To achieve this, group testing algorithms commonly assume knowledge of the number of infected individuals; nonadaptive and several adaptive algorithms fall in this category. Some adaptive algorithms, like binary splitting, operate without this assumption, but require a number of stages that may scale linearly with the size of the population. In this paper, we contribute a new algorithm that enables a balance between the number of tests and the number of stages used, and which we term diagonal splitting algorithm (DSA). Diagonal splitting, like binary splitting, does not require knowledge of the number of infected individuals, yet unlike binary splitting, is orderoptimal w.r. t. the expected number of tests it requires and is guaranteed to succeed in a small number of stages that scales at most logarithmically with the size of the population. Numerical evaluations, for diagonal splitting and a hybrid approach we propose, support our theoretical findings.
Two Dimensional Irregular Parts Packing Based on the Improved Genetic Algorithm

Aiming at the problem of two dimensional irregular parts packing, a new optimal packing algorithm was proposed.In order to enhance the operation speed of the algorithm,the algorithm to calculate the least surrounding boxes was improved.In order to enhance the global convergence performance of the algorithm,the selection operator was improved by using elitist preservation strategy.In order to enhance the precision of the algorithm,the decoding algorithm was improved by using the Height Adjustment algorithm.Solutions of the numerical example showed the effectiveness of the algorithm.
Energy-preserving Indirect-feedback for Wireless Power Transfer

Recognising the limitations of various existing channel-estimation schemes for energy beamforming, we propose an energy-preserving indirect feedback-based approach for finding the optimal beamforming vector. Upon elaborating on the key ideas behind the proposed approach -- dynamics of the harvest-then-transmit protocol and the latency associated with the charging process -- we present an algorithm and its hardware architecture to concretise the proposed approach. The algorithm and the hardware architecture are supplemented by mathematical analysis, numerical simulation and hardware utilisation details, ASIC synthesis and post-layout simulation details, respectively. We firmly believe this paper, due to its unified algorithm-hardware design, will open up new avenues for research in radio frequency (RF) wireless power transfer.
On Gauss-Bonnet type formulas for mappings between surfaces with boundary and their applications

We define singular points of the first kind and singular points of the second kind as singular points of mappings between surfaces. Typical examples of these singular points are fold singular points and cusp singular points, respectively. We can construct coherent tangent bundles, which are natural intrinsic formulations of wave fronts, by using mappings between surfaces. It is known that two types of Gauss-Bonnet type formulas hold for coherent tangent bundles over surfaces (possibly with boundary). Hence, we obtain two Gauss-Bonnet type formulas for mappings between surfaces (possibly with boundary). By applying these Gauss-Bonnet type formulas, we prove the Levine formula that relates the rotation indices to the Euler characteristic, and the Quine-Fukuda-Ishikawa formula that relates the mapping degree to the Euler characteristic and the number of singular points.
Homogenizing elastic properties of large digital rock images by combining CNN with hierarchical homogenization method

Determining effective elastic properties of rocks from their pore-scale digital images is a key goal of digital rock physics (DRP). Direct numerical simulation (DNS) of elastic behavior, however, incurs high computational cost; and surrogate machine learning (ML) model, particularly convolutional neural network (CNN), show promises to accelerate homogenization process. 3D CNN models, however, are unable to handle large images due to memory issues. To address this challenge, we propose a novel method that combines 3D CNN with hierarchical homogenization method (HHM). The surrogate 3D CNN model homogenizes only small subimages, and a DNS is used to homogenize the intermediate image obtained by assembling small subimages. The 3D CNN model is designed to output the homogenized elastic constants within the Hashin-Shtrikman (HS) bounds of the input images. The 3D CNN model is first trained on data comprising equal proportions of five sandstone (quartz mineralogy) images, and, subsequently, fine-tuned for specific rocks using transfer learning. The proposed method is applied to homogenize the rock images of size 300x300x300 and 600x600x600 voxels, and the predicted homogenized elastic moduli are shown to agree with that obtained from the brute-force DNS. The transferability of the trained 3D CNN model (using transfer learning) is further demonstrated by predicting the homogenized elastic moduli of a limestone rock with calcite mineralogy. The surrogate 3D CNN model in combination with the HHM is thus shown to be a promising tool for the homogenization of large 3D digital rock images and other random media