Group Based Interference Alignment

In the $K$-user single-input single-output (SISO) frequency-selective fading interference channel, it is shown that the maximal achievable multiplexing gain is almost surely $K/2$ by using interference alignment (IA). However, when the signaling dimensions are limited, allocating all the resources to all users simultaneously is not optimal. So, a group based interference alignment (GIA) scheme is proposed, and it is formulated as an unbounded knapsack problem. Optimal and greedy search algorithms are proposed to obtain group patterns. Analysis and numerical results show that the GIA scheme can obtain a higher multiplexing gain when the resources are limited.Comment: 3 pages, 3 figures. resubmitted to IEEE Communications Letter

Negative tunneling magnetoresistance by canted magnetization in MgO/NiO tunnel barriers

The influence of insertion of an ultra-thin NiO layer between the MgO barrier and ferromagnetic electrode in magnetic tunnel junctions has been investigated by measuring the tunneling magnetoresistance and the X-ray magnetic circular dichroism (XMCD). The magnetoresistance shows a high asymmetry with respect to bias voltage, giving rise to a negative value of -16% at 2.8 K. We attribute this to the formation of non-collinear spin structures in the NiO layer as observed by XMCD. The magnetic moments of the interface Ni atoms tilt from the easy axis due to exchange interaction and the tilting angle decreases with increasing the NiO thickness. The experimental observations are further support by non-collinear spin density functional theory

Is technology always helpful?: A critical review of the impact on learning outcomes of education technology in supporting formative assessment in schools

While education technology has been widely used in classrooms, and considerable investments have been made to support its use in the UK, the evidence base for many such rapidly changing technologies is weak, and their efficacy is unclear. The aim of this paper is to systematically review and synthesise empirical research on the use of technology in formative assessment, to identify approaches that are effective in improving pupils’ learning outcomes. The review involved a search of 11 major databases, and included 55 eligible studies. The results suggest promising evidence that digitally delivered formative assessment could facilitate the learning of maths and reading for young children, but there is no good evidence that it is effective for other subjects, or for older children, or that it is any more effective than formative assessment without technology. The review found no good evidence that learner response systems work in enhancing children’s academic attainment, and there is no evidence supporting the effectiveness of such technologies that embed gaming features. Much research in this area is of poor quality. More rigorous studies using causal designs are thus urgently needed. Meantime, there should be no rush to use technology on the basis of improving attainment

The Factors Influencing Teachers' Willingness to Remain in Rural Areas After the Expiration of Their Compulsory Service Contract: A Case Study in Enshi Prefecture, Hubei Province, China

Quality education can only be achieved with the assistance of experienced and qualified teachers. In order to ensure quality education in disadvantaged areas in China, it is necessary to keep experienced teachers in rural areas. This paper investigates factors that could influence rural teachers’ decision to stay or not on teaching at the expiry of their service contract. As a part of project, “Research on rural Teacher Training Mode in Minority Areas of Hubei Province in post-poverty Era “data from 1193 rural teachers who had qualified from a government teacher training programme from selected villages under the jurisdiction of the Enshi Prefecture, Hubei province was collected. As part of the survey, a number of factors are examined that influence a teacher's willingness to stay or leave, as well as remedial measures that are deemed necessary to prevent teachers from leaving. For the analysis of the data, SPSS 25 and AMOS 24 were used. The study found that the teachers have a high willingness to stay on the job after the contract expires. Among the factors contributing to their willingness are the local cultural environment, the local infrastructure attachment to the area, and the school's working environment. Wage income, professional identity, professional development, and school working conditions also play an important role as maintenance factors. There is a direct relationship between the rural infrastructure and a person's willingness to continue working after the expiration of a contract, and there is also direct relationship between the local attachment and the cultural atmosphere of the local community. Local attachment is the dominant factor, the local infrastructure is a secondary improvement area, and salary, promotion of opportunities, respect for teachers and education are in the priority improvement area. Finally, the paper proposes that the retention of rural teachers should be based on the cultivation of local attachment, rural revitalization, improvement of health factors and construction of “UGSR–P” cultivation of community

DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding

Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ the attention mechanism to learn the relations between video moments and the text query. However, naive attention might not be able to appropriately capture such relations, resulting in ineffective distributions where target video moments are difficult to separate from the remaining ones. To resolve the issue, we propose an energy-based model framework to explicitly learn moment-query distributions. Moreover, we propose DemaFormer, a novel Transformer-based architecture that utilizes exponential moving average with a learnable damping factor to effectively encode moment-query inputs. Comprehensive experiments on four public temporal language grounding datasets showcase the superiority of our methods over the state-of-the-art baselines.Comment: Accepted at EMNLP 2023 (Findings

Topic Modeling as Multi-Objective Contrastive Optimization

Recent representation learning approaches enhance neural topic models by optimizing the weighted linear combination of the evidence lower bound (ELBO) of the log-likelihood and the contrastive learning objective that contrasts pairs of input documents. However, document-level contrastive learning might capture low-level mutual information, such as word ratio, which disturbs topic modeling. Moreover, there is a potential conflict between the ELBO loss that memorizes input details for better reconstruction quality, and the contrastive loss which attempts to learn topic representations that generalize among input documents. To address these issues, we first introduce a novel contrastive learning method oriented towards sets of topic vectors to capture useful semantics that are shared among a set of input documents. Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective. Extensive experiments demonstrate that our framework consistently produces higher-performing neural topic models in terms of topic coherence, topic diversity, and downstream performance.Comment: Accepted at ICLR 2024 (poster

Supersymmetry and the relationship between a class of singular potentials in arbitrary dimensions

The eigenvalues of the potentials $V_{1}(r)=\frac{A_{1}}{r}+\frac{A_{2}}{r^{2}}+\frac{A_{3}}{r^{3}}+\frac{A_{4 }}{r^{4}}$ and $V_{2}(r)=B_{1}r^{2}+\frac{B_{2}}{r^{2}}+\frac{B_{3}}{r^{4}}+\frac{B_{4}}{r^ {6}}$, and of the special cases of these potentials such as the Kratzer and Goldman-Krivchenkov potentials, are obtained in N-dimensional space. The explicit dependence of these potentials in higher-dimensional space is discussed, which have not been previously covered.Comment: 13 pages article in LaTEX (uses standard article.sty). Please check "http://www1.gantep.edu.tr/~ozer" for other studies of Nuclear Physics Group at University of Gaziante

READ-PVLA: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling

Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization. With a growing number of tasks and limited training data, such full fine-tuning approach leads to costly model storage and unstable training. To overcome these shortcomings, we introduce lightweight adapters to the pre-trained model and only update them at fine-tuning time. However, existing adapters fail to capture intrinsic temporal relations among video frames or textual words. Moreover, they neglect the preservation of critical task-related information that flows from the raw video-language input into the adapter's low-dimensional space. To address these issues, we first propose a novel REcurrent ADapter (READ) that employs recurrent computation to enable temporal modeling capability. Second, we propose Partial Video-Language Alignment (PVLA) objective via the use of partial optimal transport to maintain task-related information flowing into our READ modules. We validate our READ-PVLA framework through extensive experiments where READ-PVLA significantly outperforms all existing fine-tuning strategies on multiple low-resource temporal language grounding and video-language summarization benchmarks.Comment: Accepted at AAAI 202