Dynamic Pricing and Inventory Management: Theory and Applications

We develop the models and methods to study the impact of some emerging trends in technology, marketplace, and society upon the pricing and inventory policy of a firm. We focus on the situation where the firm is in a dynamic, uncertain, and (possibly) competitive market environment. The market trends of particular interest to us are: (a) social networks, (b) sustainability concerns, and (c) customer behaviors. The two main running questions this dissertation aims to address are: (a) How these emerging market trends would influence the operations decisions and profitability of a firm; and (b) What pricing and inventory strategies a firm could use to leverage these trends. We also develop an effective comparative statics analysis method to address these two questions under different market trends. Overall, our results suggest that the current market trends of social networks, sustainability concerns, and customer behaviors have significant and interesting impact upon the operations policy of a firm, and that the firm could adopt some innovative pricing and inventory strategies to exploit these trends and substantially improve its profit. Our main findings are: (a) Network externalities (the monopoly setting). We find that network externalities prompt a firm to face the tradeoff between generating current profits and inducing future demands when making the price and inventory decisions, so that it should increase the base-stock level, and to decrease [increase] the sales price when the network size is small [large]. Our extensive numerical experiments also demonstrate the effectiveness of the heuristic policies that leverage network externalities by balancing generating current profits and inducing demands in the near future. (Chapter 2.) (b) Network externalities (the dynamic competition setting). In a competitive market with network externalities, the competing firms face the tradeoff between generating current profits and winning future market shares (i.e., the exploitation-induction tradeoff). We characterize the pure strategy Markov perfect equilibrium in both the simultaneous competition and the promotion-first competition. We show that, to balance the exploitation-induction tradeoff, the competing firms should increase promotional efforts, offer price discounts, and improve service levels. The exploitation-induction tradeoff could be a new driving force for the fat-cat effect (i.e., the equilibrium promotional efforts are higher under the promotion-first competition than those under the simultaneous competition). (Chapter 3.) (d) Trade-in remanufacturing. We show that, with the adoption of the very commonly used trade-in remanufacturing program, the firm may enjoy a higher profit with strategic customers than with myopic customers. Moreover, trade-in remanufacturing creates a tension between firm profitability and environmental sustainability with strategic customers, but benefits both the firm and the environment with myopic customers. We also find that, with either strategic or myopic customers, the socially optimal outcome can be achieved by using a simple linear subsidy and tax scheme. The commonly used government policy to subsidize for remanufacturing alone, however, does not induce the social optimum in general. (Chapter 4.) (d) Scarcity effect of inventory. We show that the scarcity effect drives both optimal prices and order-up-to levels down, whereas increased operational flexibilities (e.g., the inventory disposal and inventory withholding opportunities) mitigate the demand loss caused by high excess inventory and increase the optimal order-up-to levels and sales prices. Our extensive numerical studies also demonstrate that dynamic pricing leads to a much more significant profit improvement with the scarcity effect of inventory than without. (Chapter 5.) (e) Comparative statics analysis method. We develop a comparative statics method to study a general joint pricing and inventory management model with multiple demand segments, multiple suppliers, and stochastically evolving market conditions. Our new method makes componentwise comparisons between the focal decision variables under different parameter values, so it is capable of performing comparative statics analysis in a model where part of the decision variables are non-monotone, and it is well scalable. Hence, our new method is promising for comparative statics analysis in other operations management models. (Chapter 6.

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold

Enhancing Instance-Level Image Classification with Set-Level Labels

Instance-level image classification tasks have traditionally relied on single-instance labels to train models, e.g., few-shot learning and transfer learning. However, set-level coarse-grained labels that capture relationships among instances can provide richer information in real-world scenarios. In this paper, we present a novel approach to enhance instance-level image classification by leveraging set-level labels. We provide a theoretical analysis of the proposed method, including recognition conditions for fast excess risk rate, shedding light on the theoretical foundations of our approach. We conducted experiments on two distinct categories of datasets: natural image datasets and histopathology image datasets. Our experimental results demonstrate the effectiveness of our approach, showcasing improved classification performance compared to traditional single-instance label-based methods. Notably, our algorithm achieves 13% improvement in classification accuracy compared to the strongest baseline on the histopathology image classification benchmarks. Importantly, our experimental findings align with the theoretical analysis, reinforcing the robustness and reliability of our proposed method. This work bridges the gap between instance-level and set-level image classification, offering a promising avenue for advancing the capabilities of image classification models with set-level coarse-grained labels

No escaping helium from 55 Cnc e

We search for escaping helium from the hot super Earth 55 Cnc e by taking high-resolution spectra of the 1083 nm line during two transits using Keck/NIRSPEC. We detect no helium absorption down to a 90% upper limit of 250 ppm in excess absorption or 0.27 milli-Angstrom in equivalent width. This corresponds to a mass loss rate of less than ∼10⁹ g/s, although the precise constraint is heavily dependent on model assumptions. This rate is notably below that predicted by both the 1D hydrodynamical simulations of Salz et al 2016 and our own 2.5D models, even for implausibly thin hydrogen/helium atmospheres with surface pressures of less than 100 microbar. We consider both hydrogen- and helium-dominated atmospheric compositions, and find similar bounds on the mass loss rate in both scenarios. Together with the non-detection of Lyman α absorption by Ehrenreich et al 2012, our helium non-detection indicates that 55 Cnc e either never accreted a primordial atmosphere in the first place, or lost its primordial atmosphere shortly after the dissipation of the gas disk

Sequential method for rapid early diagnosis of white spot syndrome virus in crayfish

We developed a practical method to rapidly detect and diagnose latent white spot syndrome virus (WSSV) in infected crayfish that were non-symptomatic for WSSV. This method included a simplified extraction of DNA template, optimized loop-mediated isothermal amplification (LAMP), and final visualization of the product by means of staining with SYBR green I. Using this method, WSSV was detected in crayfish that had been artificially infected in two ways: at 5 h after injection, and 24 h after feeding with tissue from WSSV-infected crayfish (at a stage when such infected crayfish were non-symptomatic), and a thousand times or more dilution can omit fluorescent background when SYBR green I was used. Results indicate that this was a rapid, convenient, and highly sensitive method for the early diagnosis and detection of WSSV. The whole detection procedure took less than one hour to complete.Key words: White spot syndrome virus, loop-mediated isothermal amplification, SYBR green I, Procambarus clarkii, early diagnosis

PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE JIGSAW UNTUK MENINGKATKAN HASIL BELAJAR AQIDAH AKHLAK PESERTA DIDIK KELAS V MI IRSYADUT THOLIBIN TUGU REJOTANGAN TULUNGAGUNG

ABSTRAK Skripsi dengan judul “Penerapan Model Pembelajaran Kooperatif Tipe Jigsaw Untuk Meningkatkan Hasil Belajar Aqidah Akhlak Peserta Didik Kelas V MI Irsyadut Tholibin Tugu Rejotangan Tulungagung” ini ditulis oleh Sunatul Laila, NIM. 2817133189, Jurusan Pendidikan Guru Madrasah Ibtidaiyah (PGMI) Fakultas Tarbiyah dan Ilmu Keguruan, IAIN Tulungagung, dibimbing oleh Dr. Agus Purwowidodo, M.Pd. Kata Kunci: Kooperatif, Jigsaw, Hasil Belajar Penelitian dalam skripsi ini dilatar belakangi oleh temuan permasalahan dalam pembelajaran Aqidah Akhlak yaitu rendahnya hasil belajar. Hal ini tampak ketika pembelajaran Aqidah Akhlak berlangsung, banyak peserta didik yang sibuk ngobrol sendiri ketika guru menjelaskan materi. Model pembelajaran yang telah digunakan dirasa kurang mampu membuat peserta didik berperan aktif dalam proses pembelajaran, sehingga minat belajar peserta didik berkurang. Dalam hal ini peneliti berusaha mengatasi permasalahan tersebut melalui penerapan model pembelajaran kooperatif tipe Jigsaw yang diharapkan mampu meningkatkan hasil belajar peserta didik Kelas V MI Irsyadut Tholibin Tugu Rejotangan Tulungagung. Rumusan masalah pada penelitian ini meliputi: 1) Bagaimana peningkatan kerjasama kelompok peserta didik dalam kegiatan pembelajaran Aqidah Akhlak pokok bahasan Akhlak Tercela melalui Penerapan Model Pembelajaran Kooperatif Tipe Jigsaw peserta didik kelas V MI Irsyadut Tholibin Tugu Rejotangan Tulungagung 2016/2017? 2) Bagaimana peningkatan keaktifan belajar Aqidah Akhlak pokok bahasan Akhlak Tercela melalui Penerapan Model Pembelajaran Kooperatif Tipe Jigsaw peserta didik kelas V MI Irsyadut Tholibin Tugu Rejotangan Tulungagung 2016/2017? 3) Bagaimana peningkatan hasil belajar Aqidah Akhlak pokok bahasan Akhlak Tercela melalui Penerapan Model Pembelajaran Kooperatif Tipe Jigsaw peserta didik kelas V MI Irsyadut Tholibin Tugu Rejotangan Tulungagung 2016/2017?. Adapun yang menjadi tujuan dari penelitian ini adalah untuk memaparkan peningkatan kerjasama, keaktifan dan hasil belajar peserta didik dalam kegiatan pembelajaran Aqidah Akhlak materi Akhlak Tercela melalui penerapan model pembelajaran kooperatif tipe Jigsaw kelas V MI Irsyadut Tholibin Tugu Rejotangan Tulungagung. Jenis penelitian yang digunakan dalam penelitian ini adalah Penelitian Tindakan Kelas, yang terdiri dari dua siklus dan setiap siklus terdiri dari empat tahapan, yakni: 1) menyusun perencanaan (planning), 2) pelaksanaan tindakan (action), 3) melaksanakan pengamatan (observing), 4) mengadakan refleksi (reflection). Subjek penelitian yaitu peserta didik kelas V MI Irsyadut Tholibin Tugu Rejotangan Tulungagung tahun ajaran 2016/2017 yaitu terdiri dari 25 peserta didik. Teknik pengumpulan data yang digunakan yaitu tes, observasi, wawancara, dokumentasi, catatan lapangan. Teknik analisis data yang digunakan yaitu: 1) reduksi data, 2) penyajian data, 3) penarikan kesimpulan. Berdasarkan penelitian yang telah penulis lakukan, maka telah terjadi peningkatan kerjasama, keaktifan dan hasil belajar peserta didik dengan diterapkannya model pembelajaran kooperatif tipe Jigsaw. Hal ini dibuktikan berdasarkan observasi aktivitas peneliti meningkat dari 77% dengan kategori baik pada siklus I menjadi 94% dengan kategori sangat baik pada siklus II. Kerjasama peserta didik meningkat dari 75% dengan kategori cukup pada siklus I menjadi 95% dengan kategori sangat baik pada siklus II. Serta keaktifan peserta didik meningkat dari 80% dengan kategori baik pada siklus I menjadi 90% dengan kategori sangat baik pada siklus II. Begitu juga dengan ketuntasan belajar peserta didik yang mengalami peningkatan dengan ditunjukkan presentase hasil belajar 16% dengan kategori kurang sekali pada tes awal (pre test) setelah mendapat penanganan pada siklus I hasil belajar peserta didik meningkat menjadi 70,83% dengan kategori baik. Pada siklus II hasil belajar meningkat lagi menjadi 100% dengan kategori sangat baik. Dari hasil belajar pada siklus II telah menunjukkan adanya ketercapaian indikator keberhasilan yang telah ditentukan

BP-Im2col: Implicit Im2col Supporting AI Backpropagation on Systolic Arrays

State-of-the-art systolic array-based accelerators adopt the traditional im2col algorithm to accelerate the inference of convolutional layers. However, traditional im2col cannot efficiently support AI backpropagation. Backpropagation in convolutional layers involves performing transposed convolution and dilated convolution, which usually introduces plenty of zero-spaces into the feature map or kernel. The zero-space data reorganization interfere with the continuity of training and incur additional and non-negligible overhead in terms of off- and on-chip storage, access and performance. Since countermeasures for backpropagation are rarely proposed, we propose BP-im2col, a novel im2col algorithm for AI backpropagation, and implement it in RTL on a TPU-like accelerator. Experiments on TPU-like accelerator indicate that BP-im2col reduces the backpropagation runtime by 34.9% on average, and reduces the bandwidth of off-chip memory and on-chip buffers by at least 22.7% and 70.6% respectively, over a baseline accelerator adopting the traditional im2col. It further reduces the additional storage overhead in the backpropagation process by at least 74.78%.Comment: Accepted in ICCD 2022, The 40th IEEE International Conference on Computer Desig

Re-Veal the Beef Industry: Strategies to Produce High- Quality Beef From Young Cattle in Pastoral Systems

Veal is a high-value meat produced from young cattle less than 12 mo of age. The characteristic light red/pink color, tenderness, and low-fat content of veal products (especially milk-fed white veal or bobby veal) are the main features preferred by consumers. However, consumer concerns over the impact of meat production and consumption on the environment and animal welfare have increased significantly in recent years, becoming a threat to the sustained growth of the meat sector. On the other hand, processing veal from young calves (especially bobby calves) has threatened the social license to operate for both dairy and meat industries. Recently, research has been conducted to develop alternative strategies to produce beef with reduced environmental impacts and to improve animal welfare. One of the strategies could be to accelerate the beef production cycle by producing beef from younger animals of 8 to 12 mo old (i.e., vealers), especially those from dairy surplus, meanwhile reducing the number of mature animals, which are the main contributors to greenhouse gases. Information on veal from feedlots with concentrate diets is more available in the literature, compared to the equivalent from veal produced in pastoral systems, limiting the strategies that can be developed to improve the quality of veal as a whole. The present review aimed to overview the factors affecting the nutritional composition and quality of veal reported in the literature and to offer some strategies to produce value-added veal products to support the sustainable growth of veal in the dairy and beef industries